Category Archives: History of Ecology

On Progress in Ecology

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We are in ecology continually discussing what progress we are making in answering the central questions of our science. For this reason, it is sometimes interesting to compare our situation with that of economics, the queen of the social sciences, where the same argument also continues. A review by David Graeber (2019) in the New York Review of Books contains some comments about the ‘theoretical war’ in economics that might apply to some ecology subdisciplines. In it he discusses the arguments in social science between two divergent views of economics, that of the school of Keynesians and that of the now dominant Neoclassical School led by Frederich Hayek and later by Milton Friedman and many others of the Chicago School. John Maynard Keynes threw down a challenge illustrated in this quote from Graeber (2019):

“In other words, ‘(Keynes)’ assumed that the ground was always shifting under the analysts’ feet; the object of any social science was inherently unstable. Max Weber, for similar reasons, argued that it would never be possible for social scientists to come up with anything remotely like the laws of physics, because by the time they had come anywhere near to gathering enough information, society itself, and what analysts felt was important to know about it, would have changed so much that the information would be irrelevant. (p. 57)”

Precise quantitative predictions could be provided by simplified economic models, the Chicago School argued in rebutting Keynes. Graeber (2019) comments:

“Surely there’s nothing wrong with creating simplified models. Arguably, this is how any science of human affairs has to proceed. But an empirical science then goes on to test those models against what people actually do, and adjust them accordingly. This is precisely what economists did not do. Instead, they discovered that, if one encased those models in mathematical formulae completely impenetrable to the noninitiate, it would be possible to create a universe in which those premises could never be refuted. (“All actors are engaged in the maximization of utility. What is utility? Whatever it is that an actor appears to be maximizing.”) The mathematical equations allowed economists to plausibly claim theirs was the only branch of social theory that had advanced to anything like a predictive science.  (p. 57)”

In ecology the major divergence between schools of thought promoting progress have never been quite this distinct. Shades of complaint are evident in the writings of Peters (1991) and a burst of comment after that ranged from optimism (e.g. Bibby 2003) to more support for Peter’s critique (Underwood et al. 2000, Graham and Dayton 2002). Interest at this time seems to have waned in favour of very specific topics for review. If you check the Web of Science for the last 5 years for “progress” and “ecology” you will find reviews of root microbes, remote sensing of the carbon cycle, reintroduction of fishes in Canada and a host of very important reviews of small parts of the broad nature of ecology. As Kingsland (2004, 2005) recognized, ecology is an integrating science that brings together data from diverse fields of study. If this is correct, it is not surprising that ecologists differ in answering questions about progress in ecology. We should stick to small specific problems on which we can make detailed studies, measurements, and experiments to increase understanding of the causes of the original problem.

One of the most thoughtful papers on progress in ecology was that of Graham and Dayton (2002) who made an important point about progress in ecology:

“We believe that many consequences of ecological advancement will be obstacles to future progress. Here we briefly discuss just a few: (1) ecological specialization; (2) erasure of history; and (3) expansion of the literature. These problems are interconnected and have the potential to divert researchers and hinder ecological breakthroughs.” (p. 1486)

My question to all ecologists is whether or not we agree with this ‘prediction’ from 2002. There is no question in my judgement that ecology is much more specialized now, that history is erased in spite of search engines like the Web of Science and that the ecology literature is booming so rapidly that it feeds back to ecological specialization. There is no clear solution to these problems. The fact that ecology is integrative has developed into a belief that anyone with a little training in ecological science can call themselves an ecologist and pontificate about the problems of our day. This element of ‘fake news’ is not confined to ecology and we can counter it only by calling out errors propagated by politicians and others who continue to confuse truth in science with their uneducated beliefs.

Bibby, C.J. (2003). Fifty years of Bird Study. Bird Study 50, 194-210. Doi: 10.1080/00063650309461314.

Graham, M.H. and Dayton, P.K. (2002). On the evolution of ecological ideas: paradigms and scientific progress. Ecology 83, 1481-1489. Doi: 10.1890/0012-9658(2002)083[1481:OTEOEI]2.0.CO;2.

Graeber, D. (2019). Against Economics. New York Review of Books 66, 52-58. December 5, 2019.

Kingsland, S. (2004). Conveying the intellectual challenge of ecology: an historical perspective. Frontiers in Ecology and the Environment 2, 367-374. Doi: 10.1890/1540-9295(2004)002[0367:CTICOE]2.0.CO;2.

Kingsland, S.E. (2005) The Evolution of American Ecology, 1890-2000. Johns Hopkins University Press: Baltimore. 313 pp. ISBN 0801881714

Peters, R.H. (1991). A Critique for Ecology. Cambridge University Press: Cambridge, England. 366 pp. ISBN:0521400171

Underwood, A.J., Chapman, M.G., and Connell, S.D. (2000). Observations in ecology: you can’t make progress on processes without understanding the patterns. Journal of Experimental Marine Biology and Ecology 250, 97-115. Doi: 10.1016/S0022-0981(00)00181-7.

On Salmon Hatcheries as an Ecological Paradigm

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The West Coast of North America hosts 5 species of Pacific salmon that are an invaluable fishery resource and at least in theory a resource that is completely sustainable. The management of these fisheries provides a useful case study in how humans currently approach major resources, the mistakes they make, and how attempts to fix mistakes can lead to even further mistakes.

Salmon have been a major resource utilized by the First Nations of the Pacific Coast after the glaciers melted some 10-12,000 years ago. Salmon are anadromous fish, living in the ocean and spawning in fresh water. Their populations fluctuate from year to year but until the 1900s they were essentially considered an inexhaustible resource and thus became a target for exploitation. The buildup of salmon fisheries during the last 100 years coincided with an increase in environmental damage to freshwater spawning grounds. Dams on rivers cut migration routes to spawning grounds, pollution arising from mining, and erosion from forestry and agriculture all began to cut into spawning habitat and subsequently the available catch for the fishery. Salmon catches began to decline and in the late 1800s hatcheries began to be built both to restore fish stocks that were threatened and to increase the abundance of desirable fish like salmon (Naish et al 2007).

The simple model of salmon hatcheries was that the abundance of juvenile fish was the main factor limiting the adult population, so that adding more juveniles to wild juveniles moving out into the ocean would be profitable. This view of the world I call the “Farmer Paradigm” and if you are a dairy farmer with 4 cows that produce X milk, if you add 4 more cows to your farm, you now get 2X milk and thus more profit. But it became apparent with fish hatcheries that adding more juvenile fish did not necessarily increase the resulting fish catch. Some simple reasons might be that more juveniles were eaten by the predators waiting at the mouth of the river or stream, so that predation on juvenile fish was limiting. Alternatively, perhaps the ocean only had a given amount of food for juvenile growth, so that adding too many juveniles induced starvation deaths. Other explanations involving disease transmission could also be invoked.

Whatever the mechanism, it became clear that hatcheries for salmon sometimes worked and sometimes did not work to increase the productivity of the fishery. The Farmer Paradigm had to add a footnote to say “its complicated”. One complication noted early on was the possibility that natural selection in hatcheries was not equivalent to natural selection in wild populations. If hatchery fish were replacing wild fish in any population, the genetic changes involved could work in two directions by either making the entire population more fit or less fit, more productive or less. Much depends on what traits are selected for in hatcheries. In one example for sockeye salmon in Washington State, hatcheries appear to have selected for earlier spawning, so that wild sockeye in one river system return to spawn later than hatchery raised sockeye raised in the same river (Tillotson et al. 2019). Since in general juveniles from early spawners have poorer survival, climate change could favour earlier breeding and thereby reduce the overall productivity of the sockeye population in the river system. We are far from knowing the long-term selection that is occurring in hatcheries, and what it means for future populations of salmon (Cline et al. 2019, Stevenson et al. 2019).

Hatcheries are popular with the public because they indicate the government is doing something to assist fishers and hatcheries should increase and maintain fisheries production for species we love to eat. Consequently, there is a social signal that might be suppressed in data that might suggest a particular hatchery was in fact harming the fishery for a particular river or lake system. If someone wishes to do an economic analysis of the costs and benefits of a hatchery, one runs up against the standard simple belief that more juvenile fish equals higher fishery production. When Amoroso et al. (2017) tried to evaluate for pink salmon in Alaska whether hatcheries were an economic benefit or a loss, their best analysis suggested that recent increases in pink salmon productivity were higher in areas of Alaska with no hatcheries, compared with those with hatcheries. Since different river populations of pink salmon mix in their oceanic phase, it is difficult to obtain a clear experimental signal of hatchery success or failure. The immediate and the longer-term unintended consequences of hatcheries require further study. The assumption that every hatchery is an ecological and social good cannot be presumed.  

Salmon hatcheries are for me an ecological paradigm because they illustrate the management sequence: unlimited abundance → overharvesting → collapse of resource → find a technological fix → misdiagnosed problem → failure of technological fix → better diagnosis of the problem → competing socio-economic objectives → failure to act → collapse of the resource. This need not be the case, and we need to do better (Bendriem et al. 2019).

Amoroso, R.O. et al. (2017). Measuring the net biological impact of fisheries enhancement: Pink salmon hatcheries can increase yield, but with apparent costs to wild populations. Canadian Journal of Fisheries and Aquatic Sciences 74, 1233-1242. doi: 10.1139/cjfas-2016-0334.

Bendriem, N. et al. (2019). A review of the fate of southern British Columbia coho salmon over time. Fisheries Research 218, 10-21. doi: 10.1016/j.fishres.2019.04.002.

Cline, T.J. et al. (2019). Effects of warming climate and competition in the ocean for life-histories of Pacific salmon. Nature Ecology & Evolution 3, 935-942. doi: 10.1038/s41559-019-0901-7.

Naish, K.A. et al. (2007). An evaluation of the effects of conservation and fishery enhancement hatcheries on wild populations of salmon. Advances in Marine Biology 53, 61-194. doi: 10.1016/S0065-2881(07)53002-6.

Stevenson, C.F. et al. (2019). The influence of smolt age on freshwater and early marine behavior and survival of migrating juvenile sockeye salmon. Transactions of the American Fisheries Society 148, 636-651. doi: 10.1002/tafs.10156.

Tillotson, M.D. et al. (2019). Artificial selection on reproductive timing in hatchery salmon drives a phenological shift and potential maladaptation to climate change. Evolutionary Applications 12, 1344-1359. doi: 10.1111/eva.12730.

Another Simple Problem in Ecology

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In 1951 Bill Ricker and his colleagues at the Pacific Biological Station became interested in the size of the salmon caught in the West Coast fishery (Ricker 1995). By 1975 they had observed that pink salmon (Oncorhynchus gorbuscha), coho (O. kisutch) and chinook (O. tshawytscha) had all declined in size, pink salmon by 40%, coho dropped 20-30%, and chinook from 9kg to 6kg. Since 1975 changes have become more complicated in different parts of the fishery (Ricker 1995). Why these changes? Ricker concluded:

“From the data available up to 1975, I suggested that a change in the genetic constitution of salmon stocks was mainly responsible for the observed decreases in size. After all, if you are raising beef cattle, for example, you select breeding stock with a proven history of fast growth. Our fisheries have been doing exactly the opposite.” (Ricker 1995 page 600).

Everyone had read Charles Darwin and knew about natural selection and here was more clear evidence in the natural world.

            About this time wildlife managers became interested in the same possibility that by hunters selecting the largest mammals in order to obtain a trophy catch, there might be changes in the genetics of large mammal populations that could be detrimental. Festa-Bianchet and Mysterud (2018) have now reviewed the literature on the evolutionary pressures from size-selective harvests of large mammals and have shown that a series of strong conditions must be present to determine if hunting is exerting evolutionary pressure within a harvested population. They point out that the problem is far from simple. For a start one must determine if the trait that hunters select for is heritable. Since often this is antler or horn size or other dominance traits, we need to know how heritable such a trait is. For those large mammals for which we have data, heritability is low to moderate (20-40%). Then we need data on the strength of hunting selection in relation to sexual selection for large antlers or horns or body size. In general, the detection of evolutionary changes in natural populations is difficult.

Festa-Bianchet and Mysterud (2018) review the strength of inference from the available data and point out that the gold standard of ‘experimental manipulation with identified genes that affect horn/antler size, and evidence of changes in both gene frequency and trait size after manipulation’ has not been achieved for any species at the present time. Weaker evidence is available from long-term monitoring studies of several populations of the same species that are subject to different hunting pressures, and even these weaker studies are available for only a handful of ungulate species. The bottom line is that we need much more research on this ‘simple problem’ to make sure that hunting is sustainable from both an ecological and an evolutionary viewpoint.

Back to the fisheries. There has been an explosion of interest in the potential effects of fishing methods on changes in fish stocks. Kuparinen and Festa-Bianchet (2017) provide a good overview while Tillotson and Quinn (2018) dig into the details of potential selection by fisheries on the timing of migration and breeding. Morita (2019) leads us back to the Pacific salmon and how we could be selecting for earlier migrations from ocean to fresh water breeding grounds. Clearly there is much left to do on this important ‘simple’ topic.

Festa-Bianchet, M. & Mysterud, A. (2018) Hunting and evolution: theory, evidence, and unknowns. Journal of Mammalogy, 99, 1281-1292. doi: 10.1093/jmammal/gyy138

Kuparinen, A. & Festa-Bianchet, M. (2017) Harvest-induced evolution: insights from aquatic and terrestrial systems. Philosophical Transactions of the Royal Society of London, B, 372, 20160036. doi: 10.1098/rstb.2016.0036

Morita, K. (2019) Earlier migration timing of salmonids: an adaptation to climate change or maladaptation to the fishery? Canadian Journal of Fisheries and Aquatic Sciences, 76, 475-479. doi: 10.1139/cjfas-2018-0078

Ricker, W.E. (1995) Trends in the average size of Pacific salmon in Canadian catches. Climate Change and Northern Fish Populations (ed. R.J. Beamish), pp. 593-602.Canadian Special Publication of Fisheries and Aquatic Sciences, Ottawa, Ontario.

Tillotson, M.D. & Quinn, T.P. (2018) Selection on the timing of migration and breeding: A neglected aspect of fishing-induced evolution and trait change. Fish and Fisheries, 19, 170-181. doi: 10.1111/faf.12248

Is Conservation Ecology Destroying Ecology?

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Ecology became a serious science some 100 years ago when the problems that it sought to understand were clear and simple: the reasons for the distribution and abundance of organisms on Earth. It subdivided fairly early into three parts, population, community, and ecosystem ecology. It was widely understood that to understand population ecology you needed to know a great deal about physiology and behaviour in relation to the environment, and to understand community ecology you had to know a great deal about population dynamics. Ecosystem ecology then moved into community ecology plus all the physical and chemical interactions with the whole environment. But the sciences are not static, and ecology in the past 60 years has come to include nearly everything from chemistry and geography to meteorological sciences, so if you tell someone you are an ‘ecologist’ now, they have only a vague idea of what you do.

The latest invader into the ecology sphere has been conservation biology so that in the last 20 years it has become a dominant driver of ecological concerns. This has brought ecology into the forefront of publicity and the resulting political areas of controversy, not necessarily bad but with some scientific consequences. ‘Bandwagons’ are for the most part good in science because it attracts good students and professors and brings public support on side. Bandwagons are detrimental when they draw too much of the available scientific funding away from critical basic research and champion scientific fads.

The question I wish to raise is whether conservation ecology has become the latest fad in the broad science of ecology and whether this has derailed important background research. Conservation science begins with the broad and desirable goal of preserving all life on Earth and thus thwarting extinctions. This is an impossible goal and the question then becomes how can we trim it down to an achievable scientific aim? We could argue that the most important goal is to describe all the species on Earth, so that we would then know what “money” we have in the “bank”. But if we look at the insects alone, we see that this is not an achievable goal in the short term. And the key to many of these issues is what we mean by “the short term”. If we are talking10 years, we may have very specific goals, if 100 years we may redesign the goal posts, and if 1000 years again our views might change.

This is a key point. As humans we design our goals in the time frames of months and a few years, not in general in geological time. Because of climate change we are now being forced to view many things in a shorter and shorter time frame. If you live in Miami, you should do something about sea level rise now. If you grow wheat in Australia, you should worry about decreasing annual rainfall. But science in general does not have a time frame. Technology does, and we need a new phone every year, but the understanding of cancer or the ecology of tropical rain forests does not have a deadline.

But conservation biology has a ticking clock called extinction. Now we can compound our concerns about climate change and conservation to capture more of the funding for biological research in order to prevent extinctions of rare and endangered species. 

Ecological science over the past 40 years has been progressing slowly through population ecology into community and ecosystem ecology while learning that the details of populations are critical to the understanding of community function and learning how communities operate is necessary for understanding ecosystem change. None of this has been linear progress but rather a halting progression with many deviations and false leads. In order to push this agenda forward more funding has clearly been needed because teams of researchers are needed to understand a community and even more people to study an ecosystem. At the same time the value of long-term studies has become evident and equipment has become more expensive.

We have now moved into the Anthropocene in which in my opinion the focus has shifted completely from trying to answer the primary problems of ecological science to the conservation of organisms. In practice this has too often resulted in research that could only be called poor population ecology. Poor in the sense of the need for immediate short-term answers for declining species populations with no proper understanding of the underlying problem. We are faced with calls for funding that are ‘crying wolf’ with inadequate data but heartfelt opinions. Recovery plans for single species or closely related groups focus on a set of unstudied opinions that may well be correct, but to test these ideas in a reliable scientific manner would take years. Triage on a large scale is practiced without discussing the issue, and money is thrown at problems based on the publicity generated. Populations of threatened species continue to decline in what can only be described as failed management. Blame is spread in all directions to developers or farmers or foresters or chemical companies. I do not think these are the signs of a good science which above all ought to work from the strength of evidence and prepare recovery plans based on empirical science.

Part of the problem I think lies in the modern need to ‘do something’, ‘do anything’ to show that you care about a particular problem. ‘We have now no time for slow-moving conventional science, we need immediate results now’. Fortunately, many ecologists are critical of these undesirable trends in our science and carry on (e.g. Amos et al. 2013). You will not likely read tweets about these people or read about them in your daily newspapers. Evidence-based science is rarely quick, and complaints like those that I give here are not new (Sutherland et al. 2004, Likens 2010, Nichols 2012).  

Amos, J.N., Balasubramaniam, S., Grootendorst, L. et al. (2013). Little evidence that condition, stress indicators, sex ratio, or homozygosity are related to landscape or habitat attributes in declining woodland birds. Journal of Avian Biology 44, 45-54. doi: 10.1111/j.1600-048X.2012.05746.x

Likens, G.E. (2010). The role of science in decision making: does evidence-based science drive environmental policy? Frontiers in Ecology and the Environment 8, e1-e9. doi: 10.1890/090132

Nichols, J.D. (2012). Evidence, models, conservation programs and limits to management. Animal Conservation 15, 331-333. doi: 10.1111/j.1469-1795.2012.00574.x

Sutherland, W.J., Pullin, A.S., Dolman, P.M., Knight, T.M. (2004). The need for evidence-based conservation. Trends in Ecology and Evolution 19, 305-308. doi: 10.1016/j.tree.2004.03.018

On the Loss of Large Mammals

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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

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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

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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

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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?

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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

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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.