Category Archives: History of Ecology

On the Loss of Large Mammals

The loss of large mammals and birds in the Pleistocene was highlighted many years ago (Martin and Wright 1967, Grayson 1977, Guthrie 1984 and many other papers). Hypotheses about why these extinctions occurred were flying left and right for many years with no clear consensus (e.g. Choquenot and Bowman 1998). The museums of the world are filled with mastodons, moas, sabre-tooth tigers and many other skeletons of large mammals and birds long extinct. The topic has come up again in a discussion of these extinctions and a prognosis of future losses (Smith et al. 2018). I do not want to question the analysis in Smith et al. (2018) but I want to concentrate on this one quotation that has captured the essence of this paper in the media:

“Because megafauna have a disproportionate influence on ecosystem structure and function, past and present body size downgrading is reshaping Earth’s biosphere.”
(pg. 310).

What is the evidence for this very strong statement? The first thought that comes to mind is from my botanical colleagues who keep reminding me that plants make of 99% of the biomass of the Earth’s ecosystems. So, if this statement is correct, it must mean that large mammals have a very strong effect on plant ecosystem structure and function. And it must also imply that large mammals are virtually immune to predators, so no trophic cascade can occur to prevent plant overgrazing.

I appreciate that it is very difficult to test such a statement since evolution has been going on for a long time before humans arrived, and so there must have been a lot of other factors causing ecosystem changes in those early years. Humans have a disproportionate love for biodiversity that is larger than us. So, we revel in elephants, tigers, bears, and whales, while at the same time we pay little attention to the insects, small mammals, most fish, and plankton. Because of this size bias, we are greatly concerned with the conservation of large animals, as we should be, but much less concerned about what is happening to the small chaps.

What is the evidence that large mammals and birds have a disproportionate influence on ecosystem structure and function? In my experience, I would say there is very little evidence for strong ecosystem effects from the collapse of the megafauna. DeMaster et al. (2006) evaluated a proposed explanation for ecosystem collapse caused by whaling in the North Pacific Ocean and concluded that the evidence was weak for a sequential megafauna collapse caused by commercial whaling. Trites et al. (2007) and Wade et al. (2007) supported this conclusion. Citing paleo-ecological data for Australia, Johnson (2010) and Rule et al. (2012) argued in another evaluation of ecosystem changes that the human-driven extinction of the megafauna in Australia resulted in large changes in plant communities, potentially confounded by climate change and increases in fire frequency about 40K years ago. If we accept these controversies, we are left with trying to decide if the current losses of large mammals are of similar strength to those assigned to the Pleistocene megafauna, as suggested by Smith et al. (2018).

If we define ecosystem function as primary productivity and ecosystem structure as species diversity, I cannot think of a single case in recent studies where this idea has been clearly tested and supported. Perhaps this simply reflects my biased career working in arctic and subarctic ecosystems in which the vast majority of the energy flow in the system rotates through the smaller species rather than the larger ones. Take the Great Plains of North America with and without the bison herds. What aspect of ecosystem function has changed because of their loss? It is impossible to say because of human intervention in the fire cycle and agricultural pre-emption of much of the landscape. It is certainly correct that overgrazing impacts can be severe in human-managed landscapes with overstocking of cattle and sheep, and that is a tragedy brought on by economics, predator elimination programs, and human land use decisions. All the changes we can describe with paleo-ecological methods have potential explanations that are highly confounded.

I think the challenge is this: to demonstrate that the loss of large mammals at the present time creates a large change in ecosystem structure and function with data on energy flow and species diversity. The only place I can see it possible to do this experimentally today would be in arctic Canada where, at least in some areas, caribou come and go in large numbers and with relatively little human impact. I doubt that you could detect any large effect in this hypothetical experiment. It is the little chaps that matter to ecosystem function, not the big chaps that we all love so much. And I would worry if you could do this experiment, the argument would be that it is a special case of extreme environments not relevant to Africa or Australia.

No one should want the large mammals and birds to disappear, but the question of how this might play out in the coming 200 years in relation to ecosystem function requires more analysis. And unlike the current political inactivity over the looming crisis in climate change, we conservation biologists should certainly try to prevent the loss of megafauna.

Choquenot, D., and Bowman, D.M.J.S. 1998. Marsupial megafauna, Aborigines and the overkill hypothesis: application of predator-prey models to the question of Pleistocene extinction in Australia. Global Ecology and Biogeography Letters 7: 167-180.

DeMaster, D.P., Trites, A.W., Clapham, P., Mizroch, S., Wade, P., Small, R.J., and Hoef, J.V. 2006. The sequential megafaunal collapse hypothesis: testing with existing data. Progress in Oceanography 68(2-4): 329-342. doi:10.1016/j.pocean.2006.02.007

Grayson, D.K. 1977. Pleistocene avifaunas and the Overkill Hypothesis. Science 195: 691-693.

Guthrie, R.D. 1984. Mosaics, allelochemics and nutrients: An ecological theory of late Pleistocene megafaunal extinctions. In: Quaternary Extinctions: A Prehistoric Revolution ed by P.S. Martin and R.G. Klein. University of Arizona Press Tucson.

Johnson, C.N. 2010. Ecological consequences of Late Quaternary extinctions of megafauna. Proceeding of the Royal Society of London, Series B 276(1667): 2509-2519. doi: 10.1098/rspb.2008.1921.

Martin, P.S., and Wright, H.E. (eds). 1967. Pleistocene Extinctions; The Search for a Cause. Yale University Press, New Haven, Connecticut. 453 pp.

Rule, S., Brook, B.W., Haberle, S.G., Turney, C.S.M., Kershaw, A.P., and Johnson, C.N. 2012. The aftermath of megafaunal extinction: ecosystem transformation in Pleistocene Australia. Science 335(6075): 1483-1486. doi: 10.1126/science.1214261.

Smith, F.A., Elliott Smith, R.E., Lyons, S.K., and Payne, J.L. 2018. Body size downgrading of mammals over the late Quaternary. Science 360(6386): 310-313. doi: 10.1126/science.aao5987.

Trites, A.W., Deecke, V.B., Gregr, E.J., Ford, J.K.B., and Olesiuk, P.F. 2007. Killer whales, whaling, and sequential megafaunal collapse in the North Pacific: a comparative analysis of the dynamics of marine mammals in Alaska and British Columbia following commercial whaling. Marine Mammal Science 23(4): 751-765. doi: 10.1111/j.1748-7692.2006.00076.x.

Wade, P.R., et al. 2007. Killer whales and marine mammal trends in the North Pacific – a re-examination of evidence for sequential megafaunal collapse and the prey-switching hypothesis. Marine Mammal Science 23(4): 766-802. doi: 10.1111/j.1748-7692.2006.00093.x.

On the Tasks of Retirement

The end of another year in retirement and time to clean up the office. So this week I recycled 15,000 reprints – my personal library of scientific papers. I would guess that many young scientists would wonder why anyone would have 15,000 paper reprints when you could have all that on a small memory stick. Hence this blog.

Rule #1 of science: read the literature. In 1957 when I began graduate studies there were perhaps 6 journals that you had to read to keep up in terrestrial ecology. Most of them came out 3 or 4 times a year, and if you could not afford to have a personal copy of the paper either by buying the journal or later by xeroxing, you wrote to authors to ask them to post a copy of their paper to you – a reprint. The university even printed special postcards to request reprints with your name and address for the return mail. So scientists gathered paper copies of important papers. Then it became necessary to catalog them, and the simplest thing was to type the title and reference on a 3 by 5-inch card and put them in categories in a file cabinet. All of this will be incomprehensible to modern scientists.

A corollary of this old-style approach to science was that when you published, you had to purchase paper copies of reprints of your own papers. When someone got interested in your research, you would get reprint requests and then had to post them around the world. All this cost money and moreover you had to guess how popular your paper might be in future. The journal usually gave you 25 or 50 free reprints when you published a paper but if you thought you’d need more then you had to purchase them in advance. The first xerox machines were not commercially available until 1959. Xeroxing was quite expensive even when many different types of copying machines started to become available in the late 1960s. But it was always cheaper to buy a reprint when your paper was printed by a journal that it was to xerox a copy of the paper at a later date.

Meanwhile scientists had to write papers and textbooks, so the sorting of references became a major chore for all writers. In 1988 Endnote was first released as a software program that could incorporate references and allow one to sort and print them via a computer, so we were off and running, converting all the 3×5 cards into electronic format. One could then generate a bibliography in a short time and look up forgotten references by author or title or keywords. Through the 1990s the computer world progressed rapidly to approximate what you see today, with computer searches of the literature, and ultimately the ability to download a copy of a PDF of a scientific paper without even telling the author.

But there were two missing elements. All the pre-2000 literature was still piled on Library shelves, and at least in ecology is it possible that some literature published before 2000 might be worth reading. JSTOR (= Journal Storage) came to the rescue in 1995 and began to scan and compile electronic documents of much of this old literature, so even much of the earlier literature became readily available by the early 2000s. Currently there are about 1900 journals in most scientific disciplines that are available in JSTOR. Since by the late 1990s the volume of the scientific literature was doubling about every 7 years, the electronic world saved all of us from yet more paper copies of important papers.

What was missing still were many government and foundation documents, reviews of programs that were never published in the formal literature, now called the ‘grey literature’. Some of these are lost unless governments scan them and make them available. The result of any loss of this grey literature is that studies are sometimes repeated needlessly and money is wasted.

About 2.5 million scientific papers are published every year at the present time (http://www.cdnsciencepub.com/blog/21st-century-science-overload.aspx ) and the consequence of this explosion must be that each of us has to concentrate on a smaller and smaller area of science. What this means for instructors and textbook writers who must synthesize these new contributions is difficult to guess. We need more critical syntheses, but these kinds of papers are not welcomed by those that distribute our research funds so that young scientists feel they should not get caught up in writing an extensive review, however important that is for our science.

In contrast to my feeling of being overwhelmed at the present time, Fanelli and Larivière (2016) concluded that the publication rate of individuals has not changed in the last 100 years. Like most meta-analyses this one is suspicious in arguing against the simple observation in ecology that everyone seems to publish from their thesis many small papers rather than one synthetic one. Anyone who has served on a search committee for university or government jobs in the last 30 years would attest to the fact that the number of publications expected now for new graduates has become quite ridiculous. When I started my postdoc in 1962 I had one published paper, and for my first university job in 1964 this had increased to 3. There were at that time many job opportunities for anyone in my position with a total of 2 or 3 publications. To complicate things, Steen et al. (2013) have suggested that the number of retracted papers in science has been increasing at a faster rate than the number of publications. Whether again this applies to ecology papers is far from clear because the problem in ecology is typically that the methods or experimental design are inadequate rather than fraudulent.

If there is a simple message here, it is that the literature and the potential access to it is changing rapidly and young scientists need to be ready for this. Yet progress in ecology is not a simple metric of counts of papers or even citations. Quality trumps quantity.

Fanelli, D., and Larivière, V. 2016. Researchers’ individual publication rate has not increased in a century. PLoS ONE 11(3): e0149504. doi: 10.1371/journal.pone.0149504.

Steen, R.G., Casadevall, A., and Fang, F.C. 2013. Why has the number of scientific retractions increased?  PLoS ONE 8(7): e68397. doi: 10.1371/journal.pone.0068397.

 

On Mauna Loa and Long-Term Studies

If there is one important element missing in many of our current ecological paradigms it is long-term studies. This observation boils down to the lack of proper controls for our observations. If we do not know the background of our data sets, we lack critical perspective on how to interpret short-term studies. We should have learned this from paleoecologists whose many studies of plant pollen profiles and other time series from the geological record show that models of stability which occupy most of the superstructure of ecological theory are not very useful for understanding what is happening in the real world today.

All of this got me wondering what it might have been like for Charles Keeling when he began to measure CO2 levels on Mauna Loa in Hawaii in 1958. Let us do a thought experiment and suggest that he was at that time a typical postgraduate students told by his professors to get his research done in 4 or at most 5 years and write his thesis. These would be the basic data he got if he was restricted to this framework:

Keeling would have had an interesting seasonal pattern of change that could be discussed and lead to the recommendation of having more CO2 monitoring stations around the world. And he might have thought that CO2 levels were increasing slightly but this trend would not be statistically significant, especially if he has been cut off after 4 years of work. In fact the US government closed the Mauna Loa observatory in 1964 to save money, but fortunately Keeling’s program was rescued after a few months of closure (Harris 2010).

Charles Keeling could in fact be a “patron saint” for aspiring ecology graduate students. In 1957 as a postdoc he worked on developing the best way to measure CO2 in the air by the use of an infrared gas analyzer, and in 1958 he had one of these instruments installed at the top of Mauna Loa in Hawaii (3394 m, 11,135 ft) to measure pristine air. By that time he had 3 published papers (Marx et al. 2017). By 1970 at age 42 his publication list had increased to a total of 22 papers and an accumulated total of about 50 citations to his research papers. It was not until 1995 that his citation rate began to exceed 100 citations per year, and after 1995 at age 67 his citation rate increased very much. So, if we can do a thought experiment, in the modern era he could never even apply for a postdoctoral fellowship, much less a permanent job. Marx et al. (2017) have an interesting discussion of why Keeling was undercited and unappreciated for so long on what is now considered one of the world’s most critical environmental issues.

What is the message for mere mortals? For postgraduate students, do not judge the importance of your research by its citation rate. Worry about your measurement methods. Do not conclude too much from short-term studies. For professors, let your bright students loose with guidance but without being a dictator. For granting committees and appointment committees, do not be fooled into thinking that citation rates are a sure metric of excellence. For theoretical ecologists, be concerned about the precision and accuracy of the data you build models about. And for everyone, be aware that good science was carried out before the year 2000.

And CO2 levels yesterday were 407 ppm while Nero is still fiddling.

Harris, D.C. (2010) Charles David Keeling and the story of atmospheric CO2 measurements. Analytical Chemistry, 82, 7865-7870. doi: 10.1021/ac1001492

Marx, W., Haunschild, R., French, B. & Bornmann, L. (2017) Slow reception and under-citedness in climate change research: A case study of Charles David Keeling, discoverer of the risk of global warming. Scientometrics, 112, 1079-1092. doi: 10.1007/s11192-017-2405-z

In Praise of Long Term Studies

I have been fortunate this week to have had a tour of the Konza Prairie Long Term Ecological Research (LTER) site in central Kansas. Kansas State University has run this LTER site for about the last 30 years with support from the National Science Foundation (NSF) of the USA. Whoever set up this program in NSF so many years ago deserves the praise of all ecologists for their foresight, and the staff of KSU who have managed the Konza site should be given our highest congratulations for their research plan and their hard work.

The tall grass prairie used to occupy much of the central part of the temperate zone of North America from Canada to Texas. There is almost none of it left, in Kansas about 1% of the original area with the rest given over to agriculture and grazing. The practical person sees this as progress through the lens of dollar bills, the ecologist sees it as a biodiversity catastrophe. The big questions for the tall-grass prairie are clear and apply to many ecosystems: What keeps this community going? Is it fire or grazing or both in some combination? If fire is too frequent, what are the consequences for the plant community of tall-grass prairie, not to mention the aquatic community of fishes in the streams and rivers? How can shrub and tree encroachment be prevented? All of these questions are under investigation, and the answers are clear in general but uncertain in many details about effects on particular species of birds or forbs.

It strikes me that ecology very much needs more LTER programs. To my knowledge Canada and Australia have nothing like this LTER program that NSF funds. We need to ask why this is, and whether this money could be used much better for other kinds of ecological research. To my mind ecology is unique among the hard sciences in requiring long term studies, and this is because the ecological world is not an equilibrial system in the way we thought 50 years ago. Environments change, species geographical ranges change, climate varies, and all of this on top of the major human impacts on the Earth. So we need to ask questions like why is the tall grass prairie so susceptible to shrub and tree encroachment now when it apparently was not this way 200 years ago? Or why are polar bears now threatened in Hudson’s Bay when they thrived there for the last 1000 or more years? The simple answer is that the ecosystem has changed, but the ecologist wants to know how and why, so that we have some idea if these changes can be managed.

By contrast with ecological systems, physics and chemistry deal with equilibrial systems. So nobody now would investigate whether the laws of gravitation have changed in the last 30 years, and you would be laughed out of the room by physical scientists for even asking such a question and trying to get a research grant to answer this question. Continuous system change is what makes ecology among the most difficult of the hard sciences. Understanding the ecosystem dynamics of the tall-grass prairie might have been simpler 200 years ago, but is now complicated by landscape alteration by agriculture, nitrogen deposition from air pollution, the introduction of weeds from overseas, and the loss of large herbivores like bison.

Long-term studies always lead us back to the question of when we can quit such studies. There are two aspects of this issue. One is scientific, and that question is relatively easy to answer – stop when you find there are no important questions left to pursue. But this means we must have some mental image of what ‘important’ questions are (itself another issue needing continuous discussion). Scientists typically answer this question with their intuition, but not everyone’s intuition is identical. The other aspect leads us into the monitoring question – should we monitor ecosystems? The irony of this question is that we monitor the weather, and we do so because we do not know the future. So the same justification can be made for ecosystem monitoring which should be as much a part of our science as weather monitoring, human health monitoring, or stock market monitoring are to our daily lives. The next level of discussion, once we agree that monitoring is necessary, is how much money should go into ecological monitoring? The current answer in general seems to be only a little, so we stumble on with too few LTER sites and inadequate knowledge of where we are headed, like cars driving at night with weak headlights. We should do better.

A few of the 186 papers listed in the Web of Science since 2010 that include reference to Konza Prairie data:

Raynor, E.J., Joern, A. & Briggs, J.M. (2014) Bison foraging responds to fire frequency in nutritionally heterogeneous grassland. Ecology, 96, 1586-1597. doi: 10.1890/14-2027.1

Sandercock, B.K., Alfaro-Barrios, M., Casey, A.E., Johnson, T.N. & Mong, T.W. (2015) Effects of grazing and prescribed fire on resource selection and nest survival of upland sandpipers in an experimental landscape. Landscape Ecology, 30, 325-337. doi: 10.1007/s10980-014-0133-9

Ungerer, M.C., Weitekamp, C.A., Joern, A., Towne, G. & Briggs, J.M. (2013) Genetic variation and mating success in managed American plains bison. Journal of Heredity, 104, 182-191. doi: 10.1093/jhered/ess095

Veach, A.M., Dodds, W.K. & Skibbe, A. (2014) Fire and grazing influences on rates of riparian woody plant expansion along grassland streams. PLoS ONE, 9, e106922. doi: 10.1371/journal.pone.0106922

Was the Chitty Hypothesis of Population Regulation a ‘Big Idea’ in Ecology and was it successful?

Jeremy Fox in his ‘Dynamic Ecology’ Blog has raised the eternal question of what have been the big ideas in ecology and were they successful, and this has stimulated me to write about the Chitty Hypothesis and its history since 1952. I will write this from my personal observations which can be faulty, and I will not bother to put in many references since this is a blog and not a formal paper.

In 1952 when Dennis Chitty at Oxford finished his thesis on vole cycles in Wales, he was considered a relatively young heretic because he did not see any evidence in favour of the two dominant paradigms of population dynamics – that populations rose and fell because of food shortage or predation. David Lack vetoed the publication of his Ph.D. paper because he did not agree with Chitty’s findings (Lack believed that food supplies explained all population changes). His 1952 thesis paper was published only because of the intervention of Peter Medawar. Chitty could see no evidence of these two factors in his vole populations and he began to suspect that social factors were involved in population cycles. He tested Jack Christian’s ideas that social stress was a possible cause, since it was well known that some rodents were territorial and highly aggressive, but stress as measured by adrenal gland size did not fit the population trends very well. He then began to suspect that there might be genetic changes in fluctuating vole populations, and that population processes that occurred in voles and lemmings may occur in a wide variety of species, not just in the relatively small group of rodent species, which everyone could ignore as a special case of no generality. This culminated in his 1960 paper in the Canadian Journal of Zoology. This paper stimulated many field ecologists to begin experiments on population regulation in small mammals.

Chitty’s early work contained a ‘big idea’ that population dynamics and population genetics might have something to contribute to each other, and that one could not assume that every individual had equal properties. These ideas of course were not just his, and Bill Wellington had many of the same ideas in studying tent caterpillar population fluctuations. When Chitty suggested these ideas during the late 1950s he was told by several eminent geneticists who must remain nameless that his ideas were impossible, and that ecologists should stay out of genetics because the speed of natural selection was so slow that nothing could be achieved in ecological time. Clearly thinking has now changed on this general idea.

So if one could recognize these early beginnings as a ‘big idea’ it might be stated simply as ‘study individual behaviour, physiology, and genetics to understand population changes’, and it was instrumental in adding another page to the many discussions of population changes that had previously mostly included only predators, food supplies, and potentially disease. All this happened before the rise of behavioural ecology in the 1970s.

I leave others to judge the longer term effects of Chitty’s early suggestions. At present the evidence is largely against any rapid genetic changes in fluctuating populations of mammals and birds, and maternal effects now seem a strong candidate for non-genetic inheritance of traits that affect fitness in a variety of vertebrate species. And in a turn of fate, stress seems to be a strong candidate for at least some maternal effects, and we are back to the early ideas of Jack Christian and Hans Selye of the 1940s, but with greatly improved techniques of measurement of stress in field populations.

Dennis Chitty was a stickler for field experiments in ecology, a trend now long established, and he made many predictions from his ideas, often rejected later but always leading to more insights of what might be happening in field populations. He was a champion of discussing mechanisms of population change, and found little use for the dominant paradigm of the density dependent regulation of populations. Was he successful? I think so, from my biased viewpoint. I note he had less recognition in his lifetime than he deserved because he offended the powers that be. For example, he was never elected to the Royal Society, a victim of the insularity and politics of British science. But that is another story.

Chitty, D. (1952) Mortality among voles (Microtus agrestis) at Lake Vyrnwy, Montgomeryshire in 1936-9. Philosophical Transactions of the Royal Society of London, 236, 505-552.

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

On Adaptive Management

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.

Back to p-Values

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

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.

Some Reflections on Evo-Eco

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

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.