Category Archives: General Ecology

On Log-Log Regressions

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Log-log regressions are commonly used in ecological papers, and my attention to their limitations was twigged by a recent paper by Hatton et al. (2015) in Science. I want to look at just one example of a log-log regression from this paper as an illustration of what I think might be some pitfalls of this approach. The regression under discussion is Figure 1 in the Hatton paper, a plot of predator biomass (Y) on prey biomass (X) for a variety of African large mammal ecosystems. I emphasize that this is a critique of log-log regression problems, not a detailed critique of this paper.

Figure 1 shows the raw data reported in the Hatton et al. (2015) paper but plotted in arithmetic space. It is clear that the variance increases with the mean and the data are highly variable, as well as slightly curvilinear, so a transformation is clearly desirable for statistical analysis. Unfortunately we are given no error bars on each of the point estimates, so it is not possible to plot confidence limits for each estimate.

Figure 1A

We log both the axes and get Figure 2 which is identical to that plotted as Figure 1 in Hatton et al. (2015). Clearly the regression fit is better that that of Figure 1 and yet there is still considerable variation around the line of best fit.

Figure 2A

The variation around this log-log line is the main issue I wish to discuss here. Much depends on the reason for the regression line. Mac Nally (2000) made the point that regressions are often used for predictive purposes but sometimes used only as explanations. I assume here one wishes this to be a predictive regression.

So the next question is if the Figure 2 regression is predictive, how wide are the confidence limits? In this case we will adopt the usual 95% confidence predictions for a single data point. The result is shown in Figure 3, which did not appear in the Science article. The red lines define the 95% confidence belt.

Figure 3A

Now comes the main point of my concerns with log-log regressions. What do these error limits really mean when they are translated back to the original measurements that define the graph?

The table given below gives the prediction intervals for a hypothetical set of 8 prey abundances scattered along the span of prey densities reported.

Prey abundance (kg/km2)

Estimated predator abundance (kg/km2)

Predicted lower 95% confidence limit

Predicted upper 95% confidence limit

Width of lower confidence interval (%)

Width of upper confidence interval (%)

200

4.4

2.46

7.74

-44%

+76%

1000

14.1

8.16

24.6

-42%

+74%

1500

19.0

11.0

33.2

-42%

+70%

2000

23.4

13.2

41.0

-44%

+75%

4000

38.7

22.4

69.0

-42%

+78%

8000

64.0

35.4

113.6

-45%

+78%

10000

75.2

43.6

134.4

-42%

+79%

12000

85.8

49.0

147.6

-43%

+72%

The overall average confidence limits for this log-log regression are -43% to +75%, given that the SE of the predictions varies little across the range of values used in the regression. These are very broad confidence limits for any prediction from a regression line.

The bottom line is that log-log regressions can camouflage a great deal of variation, which may or may not be acceptable depending on the use of the regression. These plots always visually look much better than they are. You probably already knew this but I worry that it is a point that can be easily overlooked.

Lastly, a minor quibble with this regression. Some authors (e.g. Ricker 1983, Smith 2009) have discussed the issue of using the reduced major axis (or geometric mean regression) when the X variable is measured with error instead of the standard regression method. One could argue for this particular data set that the X variable is measured with error, so that I have used a reduced major axis regression in this discussion. The overall conclusions are not changed if standard regression methods are used.

Hatton, I.A., McCann, K.S., Fryxell, J.M., Davies, T.J., Smerlak, M., Sinclair, A.R.E. & Loreau, M. (2015) The predator-prey power law: Biomass scaling across terrestrial and aquatic biomes. Science 349 (6252). doi: 10.1126/science.aac6284

Mac Nally, R. (2000) Regression and model-building in conservation biology, biogeography and ecology: The distinction between – and reconciliation of – ‘predictive’ and ‘explanatory’ models. Biodiversity & Conservation, 9, 655-671. doi: 10.1023/A:1008985925162

Ricker, W.E. (1984) Computation and uses of central trend lines. Canadian Journal of Zoology 62 (10), 1897-1905.doi: 10.1139/z84-279

Smith, R.J. (2009) Use and misuse of the reduced major axis for line-fitting. American Journal of Physical Anthropology, 140, 476-486. doi: 10.1002/ajpa.21090

The Volkswagen Syndrome and Ecological Science

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We have all been hearing the reports that Volkswagen fixed diesel cars by some engineering trick to show low levels of pollution, while the actual pollution produced on the road is 10-100 times higher than the laboratory predicted pollution levels. I wonder if this is an analogous situation to what we have in ecology when we compare laboratory studies and conclusions to real-world situations.

The push in ecology has always been to simplify the system first by creating models full of assumptions, and then by laboratory experiments that are greatly oversimplified compared with the real world. There are very good reasons to try to do this, since the real world is rather complicated, but I wonder if we should call a partial moratorium on such research by conducting a review of how far we have been led astray by both simple models and simple laboratory population, community and ecosystem studies in microcosms and mesocosms. I can almost hear the screams coming up that of course this is not possible since graduate students must complete a degree in 2 or 3 years, and postdocs must do something in 2 years. If this is our main justification for models and microcosms, that is fair enough but we ought to be explicit about stating that and then evaluate how much we have been misled by such oversimplification.

Let me try to be clear about this problem. It is an empirical question of whether or not studies in laboratory or field microcosms can give us reliable generalizations for much more extensive communities and ecosystems that are not in some sense space limited or time limited. I have a personal view on this question, heavily influenced by studies of small mammal populations in microcosms. But my experience may be atypical of the rest of natural systems, and this is an empirical question, not one on which we can simply state our opinions.

If the world is much more complex than our current understanding of it, we must conclude that an extensive list of climate change papers should be moved to the fiction section of our libraries. If we assume equilibrial dynamics in our communities and ecosystems, we fly in violation of almost all long term studies of populations, communities, and ecosystems. The problem lies in the space and time vision of our science. Our studies are too short to show even a good representation of dynamics over a 100 year time scale, and the problems of landscape ecology highlight that what we see in patch A may be greatly influenced by whether patches B and C are close by or not. We see this darkly in a few small studies but are compelled to believe that such landscape effects are unusual or atypical. This may in fact be the case, but we need much more work to see if it is rare or common. And the broader issue is what use do we as ecologists have for ecological predictions that cannot be tested without data for the next 100 years?

Are all our grand generalizations of ecology falling by the wayside without us noticing it? Prins and Gordon (2014) in their overview seem to feel that the real world is poorly reflected in many of our beloved theories. I think this is a reflection of the Volkswagen Syndrome, of the failure to appreciate that the laboratory in its simplicity is so far removed from real world community and ecosystem dynamics that we ought to start over to build an ecological edifice of generalizations or rules with a strong appreciation of the limited validity of most generalizations until much more research has been done. The complications of the real world can be ignored in the search for simplicity, but one has to do this with the realization that predictions that flow from faulty generalizations can harm our science. We ecologists have very much research yet to do to establish secure generalizations that lead to reliable predictions.

Prins, H.H.T. & Gordon, I.J. (2014) Invasion Biology and Ecological Theory: Insights from a Continent in Transformation. Cambridge University Press, Cambridge. 540 pp. ISBN 9781107035812.

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

On Sequencing the Entire Biosphere

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There is an eternal war going on in science which rests on the simple question of “What should we fund?” If you are at a cocktail party and want to set up a storm of argument you should ask this question. There may be general agreement among many scientists that we should reduce funding on guns and wars and increase funding on alleviating poverty. But then the going gets tough. It is easier to restrict our discussion to science. There is a clear hierarchy in science funding favouring the physical sciences that can make money and the medical sciences that keep us alive until 150 years of age. But now let’s go down to biology.

The major rift in biology is between funding blue sky research and practical research. In the discussions about funding, protagonists often confound these two categories by saying that blue sky research will lead us to practical research and nirvana. We can accept salesmanship to a degree. The current bandwagon in Canada is to barcode all of life on earth, at a cost of perhaps $2 billion but probably much more. Or we can sequence everything we can get our hands on with the implicit promise that it will help us understand these organisms better or solve practical problems in conservation and management. But all of this is driven by what we can do technically, so it is machine driven, not necessarily thought driven. So if you want another heated discussion among ecologists, ask them how they would spend $2 billion for research in ecology.

We sequence because we can. Fifty years ago I heard a lecture by Richard Lewontin in which he asked what we would know if we had a telephone book with all the genetic sequences of all the organisms on earth. He concluded, as I remember, that we would know nothing unless we had a purely ‘genetic-determinism’ view of life. There is more to life than amino acid sequences perhaps.

No one I know thinks that current ecological changes are driven by genetics, but perhaps I do not know the right people. So for example, if we sequence the genomes of all the top predators on earth (Estes et al. 2011, Ripple et al. 2014), would we know anything about their importance in community and ecosystem dynamics? Probably not. But still we are told that if in New Zealand we sequence the common wasp genome we will find new ways to control this insect pest. Perhaps an equally important area would be funding to understand their biology in New Zealand, and the threats and threatening processes in an ecosystem context.

We are back to the starting question about the allocation of resources within biology. Perhaps we cycle endlessly in science funding in search of the Promised Land. In a recent paper Richards (2015) makes the argument that genome sequencing is the key to biology and thus the Promised Land:

“The unifying theme of biology is evolutionary conservation of the gene set and the resultant proteins that make up the biochemical and structural networks of cells and organisms throughout the tree of life.”

“The absence of these genome references is not just slowing research into specific questions; it is precluding a complete description of the molecular underpinnings of biology necessary for a true understanding of life on our planet.” (p. 414)

There seems little room in all this for ecological thought or ecological viewpoints. It is implicit to me that these arguments for genome sequencing have as a background assumption that ecological research is rather useless for achieving biological understanding or for solving any of the problems we currently face in conservation or management. Richards (2015) makes the point himself in saying:

“While the author is fond of ‘stamp collecting’, there are many good reasons to expand the reference sequences that underlie biological research (Table 2).”

The table he refers to in his paper has not a single item on ecological research, except that this approach will achieve “Acceleration of total biological research output”. It remains to be seen whether this view will achieve much more than stamp collecting and a massive confusion of correlation with causation. It requires a great leap of faith that this approach through genome sequencing can help to solve practical ecological problems.

Richards, S. (2015) It’s more than stamp collecting: how genome sequencing can unify biological research. Trends in Genetics, 31, 411-421.

Estes, J.A., et al. (2011) Trophic downgrading of Planet Earth. Science, 333, 301-306.

Ripple, W.J., et al. (2014) Status and ecological effects of the world’s largest carnivores. Science, 343, 1241484.

On the Use of “Density-dependent” in the Ecological Literature

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The words ‘density-dependent’ or ‘density dependence’ appear very frequently in the ecological literature, and I write this blog in a plea to never use these words unless you have a very strong definition attached to them. If you have a spare day, count how many times these words appear in a single recent issue of Ecology or the Journal of Animal Ecology and you will get a dose of my dismay. In the Web of Science a search for these words in a general ecology context gives about 1300 papers using these words since 2010, or approximately 1 paper per day.

There is an extensive literature on what density dependence means. In the modeling world, the definition is simple and can be found in every introductory ecology textbook. But it is the usage of the words ‘density-dependence’ in the real world that I want to discuss in this blog.

The concept can be quite meaningless, as Murray (1982) pointed out so many years ago. At its most modest extreme, it only says that, sooner or later, something happens when a population gets too large. Everyone could agree with that simple definition. But if you want to understand or manage population changes, you will need something much more specific. More specific might mean to plot a regression of some demographic variable with population density on the X axis. As Don Strong (1986) pointed out long ago a more typical result is density-vagueness. So if and when you write about a density-dependent relationship, at least determine how well the data fit a straight or curved line, and if the correlation coefficient is 0.3 or less you should get concerned that density has little to do with your demographic variable. If you wish to understand population dynamics, you will need to understand mechanisms and population density is not a mechanism.

Often the term density-dependent is used as a shorthand to indicate that some measured variable such as the amount of item X in the diet is related to population density. In most of these cases it is more appropriate to say that item X is statistically related to population density, and avoid all the baggage associated with the original term. Too often statements are made about mortality process X being ‘inversely density dependent’ or ‘directly density dependent’ with no data that supports such a strong conclusion.

So if there is a simple message here it is only that when you write ‘density-dependent’ in your manuscript, see if is related to the population regulation concept or if it is a simple statistical statement that is better described in simple statistical language. In both cases evaluate the strength of the evidence.

Ecology is plagued with imprecise words that can mean almost anything if they are not specified clearly, so statements about ‘biodiversity’, ‘ecosystems’, ‘resilience’, ‘diversity’, ‘metapopulations’, and ‘competition’ are fine to use so long as you indicate exactly what the operational meaning of the word entails. ‘Density-dependence’ is one of these slippery words best avoided unless you have some clear mechanism or process in mind.

Murray, B.G., Jr. (1982) On the meaning of density dependence. Oecologia, 53, 370-373.

Strong, D.R. (1986) Density-vague population change. Trends in Ecology and Evolution, 1, 39-42.

Is Conservation Ecology a Science?

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Now this is certainly a silly question. To be sure conservation ecologists collect much data, use rigorous statistical models, and do their best to achieve the general goal of protecting the Earth’s biodiversity, so clearly what they do must be the foundations of a science. But a look through some of the recent literature could give you second thoughts.

Consider for example – what are the hallmarks of science? Collecting data is one hallmark of science but is clearly not a distinguishing feature. Collecting data on the prices of breakfast cereals in several supermarkets may be useful for some purposes but it would not be confused with science. The newspapers are full of economic statistics about this and that and again no one would confuse that with science. We commonly remark that ‘this is a good scientific way to go about doing things” without thinking too much about what this means.

Back to basics. Science is a way of knowing, of accumulating knowledge to answer questions or problems in an independently verifiable way. Science deals with questions or problems that require some explanation, and the explanation is a hypothesis that needs to be tested. If the test is retrospective, the explanation may be useful for understanding the past. But science at its best is predictive about what will happen in the future, given a set of assumptions. And science always has alternative explanations or hypotheses in case the first one fails. So much everyone knows.

Conservation ecology is akin to history in having a great deal of information about the past but wishing to use that information to inform the future. In a certain sense it has a lot of the problems of history. History, according to many historians (Spinney 2012) is “just one damn thing after another”, so that there can be no science of history. But Turchin disagrees (2003, 2012) and claims that general laws can be recognized in history and general mathematical models developed. He predicts from these historical models that unrest will break out in the USA around 2020 as cycles of violence have broken out in the past every 30-50 years in this country (Spinney 2012). This is a testable prediction in a reasonable time frame.

If we look at the literature of conservation ecology and conservation genetics, we can find many observations of species declines, of geographical range shifts, and many predictions of general deterioration in the Earth’s biota. Virtually all of these predictions are not testable in any realistic time frame. We can extrapolate linear trends in population size to zero but there are so many assumptions that have to be incorporated to make these predictions, few would put money on them. For the most part the concern is rather to do something now to prevent these losses and that is very useful research. But since the major drivers of potential extinctions are habitat loss and climate change, two forces that conservation biologists have no direct control over, it is not at all clear how optimistic or pessimistic we should be when we see negative trends. Are we becoming biological historians?

There are unfortunately too few general ‘laws’ in conservation ecology to make specific predictions about the protection of biodiversity. Every one of the “ecological theory predicts…” statements I have seen in conservation papers refer to theory with so many exceptions that it ought not to be called theory at all. There are some certain predictions – if we eliminate all the habitat a species occupies, it will certainly go extinct. But exactly how much can we get rid of is an open question that there are no general rules about. “Protect genetic diversity” is another general rule of conservation biology, but the consequences of the loss of genetic diversity cannot be estimated except for controlled laboratory populations that bear little relationship to the real world.

The problems of conservation genetics are even more severe. I am amazed that conservation geneticists think they can decide what species are most ‘important’ for future evolution so that we should protect certain clades (Vane-Wright et al. 1991, Redding et al. 2014 and much additional literature). Again this is largely a guess based on so many assumptions that who knows what we would have chosen if we were in the time of the dinosaurs. The overarching problem of conservation biology is the temptation to play God. We should do this, we should do that. Who will be around to pick up the pieces when the assumptions are all wrong? Who should play God?

Redding, D.W., Mazel, F. & Mooers, A.Ø. (2014) Measuring evolutionary isolation for conservation. PLoS ONE, 9, e113490.

Spinney, L. (2012) History as science. Nature, 488, 24-26.

Turchin, P. (2003) Historical dynamics : why states rise and fall. Princeton University Press, Princeton, New Jersey.

Turchin, P. (2012) Dynamics of political instability in the United States, 1780–2010. Journal of Peace Research, 49, 577-591.

Vane-Wright, R.I., Humphries, C.J. & Williams, P.H. (1991) What to protect?—Systematics and the agony of choice. Biological Conservation, 55, 235-254.

On Tipping Points and Regime Shifts in Ecosystems

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A new important paper raises red flags about our preoccupation with tipping points, alternative stable states and regime shifts (I’ll call them collectively sharp transitions) in ecosystems (Capon et al. 2015). I do not usually call attention to papers but this paper and a previous review (Mac Nally et al. 2014) seem to me to be critical for how we think about ecosystem changes in both aquatic and terrestrial ecosystems.

Consider an oversimplified example of how a sharp transition might work. Suppose we dumped fertilizer into a temperate clear-water lake. The clear water soon turns into pea soup with a new batch of algal species, a clear shift in the ecosystem, and this change is not good for many of the invertebrates or fish that were living there. Now suppose we stop dumping fertilizer into the lake. In time, and this could be a few years, the lake can either go back to its original state of clear water or it could remain as a pea soup lake for a very long time even though the pressure of added fertilizer was stopped. This second outcome would be a sharp transition, “you cannot go back from here” and the question for ecologists is how often does this happen? Clearly the answer is of great interest to natural resource managers and restoration ecologists.

The history of this idea for me was from the 1970s at UBC when Buzz Holling and Carl Walters were modelling the spruce budworm outbreak problem in eastern Canadian coniferous forests. They produced a model with a manifold surface that tipped the budworm from a regime of high abundance to one of low abundance (Holling 1973). We were all suitably amazed and began to wonder if this kind of thinking might be helpful in understanding snowshoe hare population cycles and lemming cycles. The evidence was very thin for the spruce budworm, but the model was fascinating. Then by the 1980s the bandwagon started to roll, and alternative stable states and regime change seemed to be everywhere. Many ideas about ecosystem change got entangled with sharp transition, and the following two reviews help to unravel them.

Of the 135 papers reviewed by Capon et al. (2015) very few showed good evidence of alternative stable states in freshwater ecosystems. They highlighted the use and potential misuse of ecological theory in trying to predict future ecosystem trajectories by managers, and emphasized the need of a detailed analysis of the mechanisms causing ecosystem change. In a similar paper for estuaries and near inshore marine ecosystems, Mac Nally et al. (2014) showed that of 376 papers that suggested sharp transitions, only 8 seemed to have sufficient data to satisfy the criteria needed to conclude that a transition had occurred and was linkable to an identifiable pressure. Most of the changes described in these studies are examples of gradual ecosystem changes rather than a dramatic shift; indeed, the timescale against which changes are assessed is critical. As always the devil is in the details.

All of this is to recognize that strong ecosystem changes do occur in response to human actions but they are not often sharp transitions that are closely linked to human actions, as far as we can tell now. And the general message is clearly to increase rigor in our ecological publications, and to carry out the long-term studies that provide a background of natural variation in ecosystems so that we have a ruler to measure human induced changes. Reviews such as these two papers go a long way to helping ecologists lift our game.

Perhaps it is best to end with part of the abstract in Capon et al. (2015):

“We found limited understanding of the subtleties of the relevant theoretical concepts and encountered few mechanistic studies that investigated or identified cause-and-effect relationships between ecological responses and nominal pressures. Our results mirror those of reviews for estuarine, nearshore and marine aquatic ecosystems, demonstrating that although the concepts of regime shifts and alternative stable states have become prominent in the scientific and management literature, their empirical underpinning is weak outside of a specific environmental setting. The application of these concepts in future research and management applications should include evidence on the mechanistic links between pressures and consequent ecological change. Explicit consideration should also be given to whether observed temporal dynamics represent variation along a continuum rather than categorically different states.”

 

Capon, S.J., Lynch, A.J.J., Bond, N., Chessman, B.C., Davis, J., Davidson, N., Finlayson, M., Gell, P.A., Hohnberg, D., Humphrey, C., Kingsford, R.T., Nielsen, D., Thomson, J.R., Ward, K., and Mac Nally, R. 2015. Regime shifts, thresholds and multiple stable states in freshwater ecosystems; a critical appraisal of the evidence. Science of The Total Environment 517(0): in press. doi:10.1016/j.scitotenv.2015.02.045.

Holling, C.S. 1973. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4: 1-23. doi:10.1146/annurev.es.04.110173.000245.

Mac Nally, R., Albano, C., and Fleishman, E. 2014. A scrutiny of the evidence for pressure-induced state shifts in estuarine and nearshore ecosystems. Austral Ecology 39: 898-906. doi:10.1111/aec.12162.

The Anatomy of an Ecological Controversy – Dingos and Conservation in Australia

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Conservation is a most contentious discipline, partly because it is ecology plus a moral stance. As such you might compare it to discussions about religious truths in the last several centuries but it is a discussion among scientists who accept the priority of scientific evidence. In Australia for the past few years there has been much discussion of the role of the dingo in protecting biodiversity via mesopredator release of foxes and cats (Allen et al. 2013; Colman et al. 2014; Hayward and Marlow 2014; Letnic et al. 2011, and many more papers). I do not propose here to declare a winner in this controversy but I want to dissect it as an example of an ecological issue with so many dimensions it could continue for a long time.

Dingos in Australia are viewed like wolves in North America – the ultimate enemy that must be reduced or eradicated if possible. When in doubt about what to do, killing dingos or wolves has become the first commandment of wildlife management and conservation. The ecologist would like to know, given this socially determined goal, what are the ecological consequences of reduction or eradication of dingos or wolves. How do we determine that?

The experimentalist suggests doing a removal experiment (or conversely a re-introduction experiment) so we have ecosystems with and without dingos (Newsome et al. 2015). This would have to be carried out on a large scale dependent on the home range size of the dingo and for a number of years so that the benefits or the costs of the removal would be clear. Here is the first hurdle, this kind of experiment cannot be done, and only a quasi-experiment is possible by finding areas that have dingos and others that do not have any (or a reduced population) and comparing ecosystems. This decision immediately introduces 5 problems:

  1. The areas with- and without- the dingo are not comparable in many respects. Areas with dingos for example may be national parks placed in the mountains or in areas that humans cannot use for agriculture, while areas with dingo control are in fertile agricultural landscapes with farming subsidies.
  2. Even given areas with and without dingos there is the problem of validating the usual dingo reduction carried out by poison baits or shooting. This is an important methodological issue.
  3. One has to census the mesopredators, in Australia foxes and cats, with further methodological issues of how to achieve that with accuracy.
  4. In addition one has to census the smaller vertebrates presumed to be possibly affected by the mesopredator offtake.
  5. Finally one has to do this for several years, possibly 5-10 years, particularly in variable environments, and in several pairs of areas chosen to represent the range of ecosystems of interest.

All in all this is a formidable research program, and one that has been carried out in part by the researchers working on dingos. And we owe them our congratulations for their hard work. The major part of the current controversy has been how one measures population abundance of all the species involved. The larger the organism, paradoxically the more difficult and expensive the methods of estimating abundance. Indirect measures, often from predator tracks in sand plots, are forced on researchers because of a lack of funding and the landscape scale of the problem. The essence of the problem is that tracks in sand or mud measure both abundance and activity. If movements increase in the breeding season, tracks may indicate activity more than abundance. If old roads are the main sampling sites, the measurements are not a random sample of the landscape.

This monumental sampling headache can be eliminated by the bold stroke of concluding with Nimmo et al. (2015) and Stephens et al. (2015) that indirect measures of abundance are sufficient for guiding actions in conservation management. They may be, they may not be, and we fall back into the ecological dilemma that different ecosystems may give different answers. And the background question is what level of accuracy do you need in your study? We are all in a hurry now and want action for conservation. If you need to know only whether you have “few” or “many” dingos or tigers in your area, indirect methods may well serve the purpose. We are rushing now into the “Era of the Camera” in wildlife management because the cost is low and the volume of data is large. Camera ecology may be sufficient for occupancy questions, but may not be enough for demographic analysis without detailed studies.

The moral issue that emerges from this particular dingo controversy is similar to the one that bedevils wolf control in North America and Eurasia – should we remove large predators from ecosystems? The ecologist’s job is to determine the biodiversity costs and benefits of such actions. But in the end we are moral beings as well as ecologists, and for the record, not the scientific record but the moral one, I think it is poor policy to remove dingos, wolves, and all large predators from ecosystems. Society however seems to disagree.

 

Allen, B.L., Allen, L.R., Engeman, R.M., and Leung, L.K.P. 2013. Intraguild relationships between sympatric predators exposed to lethal control: predator manipulation experiments. Frontiers in Zoology 10(39): 1-18. doi:10.1186/1742-9994-10-39.

Colman, N.J., Gordon, C.E., Crowther, M.S., and Letnic, M. 2014. Lethal control of an apex predator has unintended cascading effects on forest mammal assemblages. Proceedings of the Royal Society of London, Series B 281(1803): 20133094. doi:DOI: 10.1098/rspb.2013.3094.

Hayward, M.W., and Marlow, N. 2014. Will dingoes really conserve wildlife and can our methods tell? Journal of Applied Ecology 51(4): 835-838. doi:10.1111/1365-2664.12250.

Letnic, M., Greenville, A., Denny, E., Dickman, C.R., Tischler, M., Gordon, C., and Koch, F. 2011. Does a top predator suppress the abundance of an invasive mesopredator at a continental scale? Global Ecology and Biogeography 20(2): 343-353. doi:10.1111/j.1466-8238.2010.00600.x.

Newsome, T.M., et al. (2015) Resolving the value of the dingo in ecological restoration. Restoration Ecology, 23 (in press). doi: 10.1111/rec.12186

Nimmo, D.G., Watson, S.J., Forsyth, D.M., and Bradshaw, C.J.A. 2015. Dingoes can help conserve wildlife and our methods can tell. Journal of Applied Ecology 52. (in press, 27 Jan. 2015). doi:10.1111/1365-2664.12369.

Stephens, P.A., Pettorelli, N., Barlow, J., Whittingham, M.J., and Cadotte, M.W. 2015. Management by proxy? The use of indices in applied ecology. Journal of Applied Ecology 52(1): 1-6. doi:10.1111/1365-2664.12383.

A Survey of Strong Inference in Ecology Papers: Platt’s Test and Medawar’s Fraud Model

10 Replies

In 1897 Chamberlin wrote an article in the Journal of Geology on the method of multiple working hypotheses as a way of experimentally testing scientific ideas (Chamberlin 1897 reprinted in Science). Ecology was scarcely invented at that time and this has stimulated my quest here to see if current ecology journals subscribe to Chamberlin’s approach to science. Platt (1964) formalized this approach as “strong inference” and argued that it was the best way for science to progress rapidly. If this is the case (and some do not agree that this approach is suitable for ecology) then we might use this model to check now and then on the state of ecology via published papers.

I did a very small survey in the Journal of Animal Ecology for 2015. Most ecologists I hope would classify this as one of our leading journals. I asked the simple question of whether in the Introduction to each paper there were explicit hypotheses stated and explicit alternative hypotheses, and categorized each paper as ‘yes’ or ‘no’. There is certainly a problem here in that many papers stated a hypothesis or idea they wanted to investigate but never discussed what the alternative was, or indeed if there was an alternative hypothesis. As a potential set of covariates, I tallied how many times the word ‘hypothesis’ or ‘hypotheses’ occurred in each paper, as well as the word ‘test’, ‘prediction’, and ‘model’. Most ‘model’ and ‘test’ words were used in the context of statistical models or statistical tests of significance. Singular and plural forms of these words were all counted.

This is not a publication and I did not want to spend the rest of my life looking at all the other ecology journals and many issues, so I concentrated on the Journal of Animal Ecology, volume 84, issues 1 and 2 in 2015. I obtained these results for the 51 articles in these two issues: (number of times the word appeared per article, averaged over all articles)

Explicit hypothesis and alternative hypotheses

“Hypothesis”

“Test”

“Prediction”

“Model”

Yes

22%

Mean

3.1

7.9

6.5

32.5

No

78%

Median

1

6

4

20

No. articles

51

Range

0-23

0-37

0-27

0-163

There are lots of problems with a simple analysis like this and perhaps its utility may lie in stimulating a more sophisticated analysis of a wider variety of journals. It is certainly not a random sample of the ecology literature. But maybe it gives us a few insights into ecology 2015.

I found the results quite surprising in that many papers failed Platt’s Test for strong inference. Many papers stated hypotheses but failed to state alternative hypotheses. In some cases the implied alternative hypothesis is the now-discredited null hypothesis (Johnson 2002). One possible reason for the failure to state hypotheses clearly was discussed by Medawar many years ago (Howitt and Wilson 2014; Medawar 1963). He pointed out that most scientific papers were written backwards, analysing the data, finding out what it concluded, and then writing the introduction to the paper knowing the results to follow. A significant number of papers in these issues I have looked at here seem to have been written following Medawar’s “fraud model”.

But make of such data as you will, and I appreciate that many people write papers in a less formal style than Medawar or Platt would prefer. And many have alternative hypotheses in mind but do not write them down clearly. And perhaps many referees do not think we should be restricted to using the hypothetical deductive approach to science. All of these points of view should be discussed rather than ignored. I note that some ecological journals now turn back papers that have no clear statement of a hypothesis in the introduction to the submitted paper.

The word ‘model’ is the most common word to appear in this analysis, typically in the case of a statistical model evaluated by AIC kinds of statistics. And the word ‘test’ was most commonly used in statistical tests (‘t-test’) in a paper. Indeed virtually all of these paper overflow with statistical estimates of various kinds. Few however come back in the conclusions to state exactly what progress has been made by their paper and even less make statements about what should be done next. From this small survey there is considerable room for improvement in ecological publications.

Chamberlin, T.C. 1897. The method of multiple working hypotheses. Journal of Geology 5: 837-848 (reprinted in Science 148: 754-759 in 1965). doi:10.1126/science.148.3671.754

Howitt, S.M., and Wilson, A.N. 2014. Revisiting “Is the scientific paper a fraud?”. EMBO reports 15(5): 481-484. doi:10.1002/embr.201338302

Johnson, D.H. (2002) The role of hypothesis testing in wildlife science. Journal of Wildlife Management 66(2): 272-276. doi: 10.2307/3803159

Medawar, P.B. 1963. Is the scientific paper a fraud? In “The Threat and the Glory”. Edited by P.B. Medawar. Harper Collins, New York. pp. 228-233. (Reprinted by Harper Collins in 1990. ISBN: 9780060391126.)

Platt, J.R. 1964. Strong inference. Science 146: 347-353. doi:10.1126/science.146.3642.347

Demography Made Simple

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I have grown weary of listening to radio and TV new announcers discuss the human population problem. I think a primer of a few principles of population arithmetic might be useful to remind us where we ecologists sit in these discussions. The problem centres on the issue of eternal growth and then the transition of any population from a growing one to a stable one. I concentrate here on human populations but the results apply to any long-lived species.

I list four empirical principles of demography.

  1. No population can continue growing without limit. This generalization is rock solid, so it would be good to keep mentioning it to sceptics of the following generalizations.
  2. Populations grow when births and immigration exceed deaths and emigration. If we consider the entire global human population, emigration and immigration disappear since we have not yet colonized space. Populations stabilize when births equal deaths.
  3. A population that moves from a growth phase to a stable phase must change in age structure. Every stable population must contain fewer young persons and more older persons.
  4. These changes in age structure have enormous implications for our requirements for hospitals, doctors, schools, teachers, and social support agencies. These changes are almost completely predicable for humans and should not come as a surprise to politicians.
  5. Pushing the panic button because a particular population like that of Japan is stabilizing and could even decline slightly may be useful for economists wishing for infinite growth but should be recognized as an expected event for every country in the future.

The bottom line is that we have the knowledge and the ability to plan for the cessation of human population growth. Many good books have been written to make these points and we need to keep repeating them. That many people do not understand the simple arithmetic of population change is a worry, and we should all try to communicate these 5 simple principles to all who will listen.

Cafaro, P., and Crist, E. 2012. Life on the Brink: Environmentalists Confront Overpopulation. University of Georgia Press, Athens, Georgia. 342 pp. ISBN: 978-0-8203-4385-3

Daly, H.E., and Farley, J. 2011. Ecological Economics: Principles and Applications. 2nd ed. Island Press, Washington, D.C. 509 pp. ISBN: 978-1-5972-6681-9

Washington, H. 2015. Demystifying Sustainability: Towards Real Solutions. Routledge, New York. 222 pp. ISBN: 978-1138812697