Tag Archives: mechanistic hypotheses

On Assumptions in Ecology Papers

What can we do as ecologists to improve the publishing standards of ecology papers? I suggest one simple but bold request. We should require at the end of every published paper a annotated list of the assumptions made in providing the analysis reported in the paper. A tabular format could be devised with columns for the assumption, the perceived support of and tests for the assumption, and references for this support or lack thereof. I can hear the screaming already, so this table could be put in the Supplementary Material which most people do not read. We could add to each paper in the final material where there are statements of who did the writing, who provided the money, and add a reference to this assumptions table in the Supplementary Material or a statement that no assumptions about anything were made to reach these conclusions.

The first response I can detect to this recommendation is that many ecologists will differ in what they state are assumptions to their analysis and conclusions. As an example, in wildlife studies, we commonly make the assumption that an individual animal having a radio collar will behave and survive just like another animal with no collar. In analyses of avian population dynamics, we might commonly assume that our visiting nests does not affect their survival probability. We make many such assumptions about random or non-random sampling. My question then is whether or not there is any value in listing these kinds of assumptions. My response is that this approach of listing what the authors think they are assuming should alert the reviewers to the elephants in the room that have not been listed.

My attention was called to this general issue by the recent paper of Ginzburg and Damuth (2022) in which they contrasted the assumptions of two general theories of functional responses of predators to prey – “prey dependence” versus “ratio dependence”. We have in ecology many such either-or discussions that never seem to end. Consider the long-standing discussion of whether populations can be regulated by factors that are “density dependent” or “density independent”, a much-debated issue that is still with us even though it was incisively analyzed many years ago.  

Experimental ecology is not exempt from assumptions, as outlined in Kimmel et al. (2021) who provide an incisive review of cause and effect in ecological experiments. Pringle and Hutchinson (2020) discuss the failure of assumptions in food web analysis and how these might be resolved with new techniques of analysis. Drake et al. (2021) consider the role of connectivity in arriving at conservation evaluations of patch dynamics, and the importance of demographic contributions to connectivity via dispersal. The key point is that, as ecology progresses, the role of assumptions must be continually questioned in relation to our conclusions about population and community dynamics in relation to conservation and landscape management.

Long ago Peters (1991) wrote an extended critique of how ecology should operate to avoid some of these issues, but his 1991 book is not easily available to students (currently available on Amazon for about $90). To encourage more discussion of these questions from the older to the more current literature, I have copied Peters Chapter 4 to the bottom of my web page at https://www.zoology.ubc.ca/~krebs/books.html for students to download if they wish to discuss these issues in more detail.

Perhaps a possible message in all this has been that ecology has always wished to be “physics-in-miniature” with grand generalizations like the laws we teach in the physical sciences. Over the last 60 years the battle in the ecology literature has been between this model of physics and the view that every population and community differ, and everything is continuing to change under the climate emergency so that we can have little general theory in ecology. There are certainly many current generalizations, but they are relatively useless for a transition from the general to the particular for the development of a predictive science. The consequence is that we now bounce from individual study to individual study, typically starting from different assumptions, with very limited predictability that is empirically testable. And the central issue for ecological science is how can we move from the present fragmentation in our knowledge to a more unified science. Perhaps starting to examine the assumptions of our current publications would be a start in this direction.  

Drake, J., Lambin, X., and Sutherland, C. (2021). The value of considering demographic contributions to connectivity: a review. Ecography 44, 1-18. doi: 10.1111/ecog.05552.

Ginzburg, L.R. and Damuth, J. (2022). The Issue Isn’t Which Model of Consumer Interference Is Right, but Which One Is Least Wrong. Frontiers in Ecology and Evolution 10, 860542. doi: 10.3389/fevo.2022.860542.

Kimmel, K., Dee, L.E., Avolio, M.L., and Ferraro, P.J. (2021). Causal assumptions and causal inference in ecological experiments. Trends in Ecology & Evolution 36, 1141-1152. doi: 10.1016/j.tree.2021.08.008.

Peters, R.H. (1991) ‘A Critique for Ecology.’ (Cambridge University Press: Cambridge, England.) ISBN:0521400171 (Chapter 4 pdf available at https://www.zoology.ubc.ca/~krebs/books.html)

Pringle, R.M. and Hutchinson, M.C. (2020). Resolving Food-Web Structure. Annual Review of Ecology, Evolution, and Systematics 51, 55-80. doi: 10.1146/annurev-ecolsys-110218-024908.

On Evolution and Ecology and Climate Change

If ecology can team up with evolution to become a predictive science, we can all profit greatly since it will make us more like physics and the hard sciences. It is highly desirable to have a grand vision of accomplishing this, but there could be a few roadblocks on the way. A recent paper by Bay et al. (2018) illustrates some of the difficulties we face.

The yellow warbler (Setophaga petechia) has a broad breeding range across the United States and Canada, and could therefore be a good species to survey because it inhabits widely different climatic zones. Bay et al. (2018) identified genomic variation associated with climate across the breeding range of this migratory songbird, and concluded that populations requiring the greatest shifts in allele frequencies to keep pace with future climate change have experienced the largest population declines, suggesting that failure to adapt may have already negatively affected population abundance. This study by Bay et al. (2018) sampled 229 yellow warblers from 21 locations across North America, with an average of 10 birds per sample area (range n = 6 to 21). They examined 104,711 single-nucleotide polymorphisms. They correlated genetic structure to 19 climate variables and 3 vegetation indices, a measure of surface moisture, and average elevation. This is an important study claiming to support an important conclusion, and consequently it is also important to break it down into the three major assumptions on which it rests.

First, this study is a space for time analysis, a subject of much discussion already in plant ecology (e.g. Pickett 1989, Blois et al. 2013). It is an untested assumption that you can substitute space for time in analyzing for future evolutionary changes.

Second, the conclusions of the Bay et al. paper rest on an assumption that you have adequate data on the genetics involved in change and on the demography of the species. A clear understanding of the ecology of the species and what limits its distribution and abundance would seem to be prerequisites for understanding the mechanisms of how evolutionary changes might occur.

The third assumption is that, if there is a correlation between the genetic measures and the climate or vegetation indices, one can identify the precise ‘genomic vulnerability’ of the local population. Genomic variation was most closely related to precipitation variables at each site. The geographic area with one of the highest scores in genomic vulnerability was in the desert area of the intermountain west (USA). As far as I can determine from their Figure 1, there was only one sampling site in this whole area of the intermountain west. Finally Bay et al. (2018) compared the genomic vulnerability data to the population changes reported for each site. Population changes for each sampled site were obtained from the North American Breeding Bird Survey data from 1996 to 2012.

The genetic data and its analysis are more impressive, and since I am not a genetics expert I will simply give it a A grade for genetics. It is the ecology that worries me. I doubt that the North American Breeding Bird Survey is a very precise measure of population changes in any particular area. But following the Bay et al. paper, assume that it is a good measure of changing abundance for the yellow warbler. From the Bay et al. paper abstract we see this prediction:

“Populations requiring the greatest shifts in allele frequencies to keep pace with future climate change have experienced the largest population declines, suggesting that failure to adapt may have already negatively affected populations.”

The prediction is illustrated in Figure 1 below from the Bay et al. paper.

Figure 1. From Bay et al. (2018) Figure 2C. (Red dot explained below).

Consider a single case, the Great Basin, area S09 of the Sauer et al. (2017) breeding bird surveys. From the map in Bay et al. (2018) Figure 2 we get the prediction of a very high genomic vulnerability (above 0.06, approximate red dot in Figure 1 above) for the Great Basin, and thus a strongly declining population trend. But if we go to the Sauer et al. (2017) database, we get this result for the Great Basin (Figure 2 here), a completely stable yellow warbler population for the last 45 years.

Figure 2. Data for the Great Basin populations of the Yellow Warbler from the North American Breeding Bird Survey, 1967 to 2015 (area S09). (From Sauer et al. 2017)

One clue to this discrepancy is shown in Figure 1 above where R2 = 0.10, which is to say the predictability of this genomic model is near zero.

So where does this leave us? We have what appears to be an A grade genetic analysis coupled with a D- grade ecological model in which explanations are not based on any mechanism of population dynamics, so that the model presented is useless for any predictions that can be tested in the next 10-20 years. I am far from convinced that this is a useful exercise. It would be a good paper for a graduate seminar discussion. Marvelous genetics, very poor ecology.

And as a footnote I note that mammalian ecologists have already taken a different but more insightful approach to this whole problem of climate-driven adaptation (Boutin and Lane 2014).

Bay, R.A., Harrigan, R.J., Underwood, V.L., Gibbs, H.L., Smith, T.B., and Ruegg, K. 2018. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359(6371): 83-86. doi: 10.1126/science.aan4380.

Blois, J.L., Williams, J.W., Fitzpatrick, M.C., Jackson, S.T., and Ferrier, S. 2013. Space can substitute for time in predicting climate-change effects on biodiversity. Proceedings of the National Academy of Sciences 110(23): 9374-9379. doi: 10.1073/pnas.1220228110.

Boutin, S., and Lane, J.E. 2014. Climate change and mammals: evolutionary versus plastic responses. Evolutionary Applications 7(1): 29-41. doi: 10.1111/eva.12121.

Pickett, S.T.A. 1989. Space-for-Time substitution as an alternative to long-term studies. In Long-Term Studies in Ecology: Approaches and Alternatives. Edited by G.E. Likens. Springer New York, New York, NY. pp. 110-135.

Sauer, J.R., Niven, D.K., Hines, J.E., D. J. Ziolkowski, J., Pardieck, K.L., and Fallon, J.E. 2017. The North American Breeding Bird Survey, Results and Analysis 1966 – 2015. USGS Patuxent Wildlife Research Center, Laurel, MD. https://www.mbr-pwrc.usgs.gov/bbs/

On Post-hoc Ecology

Back in the Stone Age when science students took philosophy courses, a logic course was a common choice for students majoring in science. Among the many logical fallacies one of the most common was the Post Hoc Fallacy, or in full “Post hoc, ergo propter hoc”, “After this, therefore because of this.” The Post Hoc Fallacy has the following general form:

  1. A occurs before B.
  2. Therefore A is the cause of B.

Many examples of this fallacy are given in the newspapers every day. “I lost my pencil this morning and an earthquake occurred in California this afternoon.” Therefore….. Of course, we are certain that this sort of error could never occur in the 21st century, but I would like to suggest to the contrary that its frequency is probably on the rise in ecology and evolutionary biology, and the culprit (A) is most often climate change.

Hilborn and Stearns (1982) pointed out many years ago that most ecological and evolutionary changes have multiple causes, and thus we must learn to deal with multiple causation in which a variety of factors combine and interact to produce an observed outcome. This point of view places an immediate dichotomy between the two extremes of ecological thinking – single factor experiments to determine causation cleanly versus the “many factors are involved” world view. There are a variety of intermediate views of ecological causality between these two extremes, leading in part to the flow chart syndrome of boxes and arrows aptly described by my CSIRO colleague Kent Williams as “horrendograms”. If you are a natural resource manager you will prefer the simple end of the spectrum to answer the management question of ‘what can I possibly manipulate to change an undesirable outcome for this population or community?’

Many ecological changes are going on today in the world, populations are declining or increasing, species are disappearing, geographical distributions are moving toward the poles or to higher altitudes, and novel diseases are appearing in populations of plants and animals. The simplest explanation of all these changes is that climate change is the major cause because in every part of the Earth some aspect of winter or summer climate is changing. This might be correct, or it might be an example of the Post Hoc Fallacy. How can we determine which explanation is correct?

First, for any ecological change it is important to identify a mechanism of change. Climate, or more properly weather, is itself a complex factor of temperature, humidity, and rainfall, and for climate to be considered a proper cause you must advance some information on physiology or behaviour or genetics that would link some specific climate parameter to the changes observed. Information on possible mechanisms makes the potential explanation more feasible. A second step is to make some specific predictions that can be tested either by experiments or by further observational data. Berteaux et al. (2006) provided a careful list of suggestions on how to proceed in this manner, and Tavecchia et al. (2016) have illustrated how one traditional approach to studying the impact of climate change on population dynamics could lead to forecasting errors.

A second critical focus must be on long-term studies of the population or community of interest. In particular, 3-4 year studies common in Ph.D. theses must make the assumption that the results are a random sample of annual ecological changes. Often this is not the case and this can be recognized when longer term studies are completed or more easily if an experimental manipulation can be carried out on the mechanisms involved.

The retort to these complaints about ecological and evolutionary inference is that all investigated problems are complex and multifactorial, so that after much investigation one can conclude only that “many factors are involved”. The application of AIC analysis attempts to blunt this criticism by taking the approach that, given the data (the evidence), what hypothesis is best supported? Hobbs and Hilborn (2006) provide a guide to the different methods of inference that can improve on the standard statistical approach. The AIC approach has always carried with it the awareness of the possibility that the correct hypothesis is not present in the list being evaluated, or that some combination of relevant factors cannot be tested because the available data does not cover a wide enough range of variation. Burnham et al. (2011) provide an excellent checklist for the use of AIC measures to discriminate among hypotheses. Guthery et al. (2005) and Stephens et al. (2005) carry the discussion in interesting ways. Cade (2015) discusses an interesting case in which inappropriate AIC methods lead to questionable conclusions about habitat distribution preferences and use by sage-grouse in Colorado.

If there is a simple message in all this it is to think very carefully about what the problem is in any investigation, what the possible solutions or hypotheses are that could explain the problem, and then utilize the best statistical methods to answer that question. Older statistical methods are not necessarily bad, and newer statistical methods not automatically better for solving problems. The key lies in good data, relevant to the problem being investigated. And if you are a beginning investigator, read some of these papers.

Berteaux, D., et al. 2006. Constraints to projecting the effects of climate change on mammals. Climate Research 32(2): 151-158. doi: 10.3354/cr032151.

Burnham, K.P., Anderson, D.R., and Huyvaert, K.P. 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behavioral Ecology and Sociobiology 65(1): 23-35. doi: 10.1007/s00265-010-1029-6.

Guthery, F.S., Brennan, L.A., Peterson, M.J., and Lusk, J.J. 2005. Information theory in wildlife science: Critique and viewpoint. Journal of Wildlife Management 69(2): 457-465. doi: 10.1890/04-0645.

Hilborn, R., and Stearns, S.C. 1982. On inference in ecology and evolutionary biology: the problem of multiple causes. Acta Biotheoretica 31: 145-164. doi: 10.1007/BF01857238

Hobbs, N.T., and Hilborn, R. 2006. Alternatives to statistical hypothesis testing in ecology: a guide to self teaching. Ecological Applications 16(1): 5-19. doi: 10.1890/04-0645

Stephens, P.A., Buskirk, S.W., Hayward, G.D., and Del Rio, C.M. 2005. Information theory and hypothesis testing: a call for pluralism. Journal of Applied Ecology 42(1): 4-12. doi: 10.1111/j.1365-2664.2005.01002.x

Tavecchia, G., et al. 2016. Climate-driven vital rates do not always mean climate-driven population. Global Change Biology 22(12): 3960-3966. doi: 10.1111/gcb.13330.

Climate Change and Ecological Science

One dominant paradigm of the ecological literature at the present time is what I would like to call the Climate Change Paradigm. Stated in its clearest form, it states that all temporal ecological changes now observed are explicable by climate change. The test of this hypothesis is typically a correlation between some event like a population decline, an invasion of a new species into a community, or the outbreak of a pest species and some measure of climate. Given clever statistics and sufficient searching of many climatic measurements with and without time lags, these correlations are often sanctified by p< 0.05. Should we consider this progress in ecological understanding?

An early confusion in relating climate fluctuations to population changes was begun by labelling climate as a density independent factor within the density-dependent model of population dynamics. Fortunately, this massive confusion was sorted out by Enright (1976) but alas I still see this error repeated in recent papers about population changes. I think that much of the early confusion of climatic impacts on populations was due to this classifying all climatic impacts as density-independent factors.

One’s first response perhaps might be that indeed many of the changes we see in populations and communities are indeed related to climate change. But the key here is to validate this conclusion, and to do this we need to talk about the mechanisms by which climate change is acting on our particular species or species group. The search for these mechanisms is much more difficult than the demonstration of a correlation. To become more convincing one might predict that the observed correlation will continue for the next 5 (10, 20?) years and then gather the data to validate the correlation. Many of these published correlations are so weak as to preclude any possibility of validation in the lifetime of a research scientist. So the gold standard must be the deciphering of the mechanisms involved.

And a major concern is that many of the validations of the climate change paradigm on short time scales are likely to be spurious correlations. Those who need a good laugh over the issue of spurious correlation should look at Vigen (2015), a book which illustrates all too well the fun of looking for silly correlations. Climate is a very complex variable and a nearly infinite number of measurements can be concocted with temperature (mean, minimum, maximum), rainfall, snowfall, or wind, analyzed over any number of time periods throughout the year. We are always warned about data dredging, but it is often difficult to know exactly what authors of any particular paper have done. The most extreme examples are possible to spot, and my favorite is this quotation from a paper a few years ago:

“A total of 864 correlations in 72 calendar weather periods were examined; 71 (eight percent) were significant at the p< 0.05 level. …There were 12 negative correlations, p< 0.05, between the number of days with (precipitation) and (a demographic measure). A total of 45- positive correlations, p<0.05, between temperatures and (the same demographic measure) were disclosed…..”

The climate change paradigm is well established in biogeography and the major shifts in vegetation that have occurred in geological time are well correlated with climatic changes. But it is a large leap of faith to scale this well established framework down to the local scale of space and a short-term time scale. There is no question that local short term climate changes can explain many changes in populations and communities, but any analysis of these kinds of effects must consider alternative hypotheses and mechanisms of change. Berteaux et al. (2006) pointed out the differences between forecasting and prediction in climate models. We desire predictive models if we are to improve ecological understanding, and Berteaux et al. (2006) suggested that predictive models are successful if they follow three rules:

(1) Initial conditions of the system are well described (inherent noise is small);

(2) No important variable is excluded from the model (boundary conditions are defined adequately);

(3) Variables used to build the model are related to each other in the proper way (aggregation/representation is adequate).

Like most rules for models, whether these conditions are met is rarely known when the model is published, and we need subsequent data from the real world to see if the predictions are correct.

I am much less convinced that forecasting models are useful in climate research. Forecasting models describe an ecological situation based on correlations among the measurements available with no clear mechanistic model of the ecological interactions involved. My concern was highlighted in a paper by Myers (1998) who investigated for fish populations the success of published juvenile recruitment-environmental factor (typically temperature) correlations and found that very few forecasting models were reliable when tested against additional data obtained after publication. It would be useful for someone to carry out a similar analysis for bird and mammal population models.

Small mammals show some promise for predictive models in some ecosystems. The analysis by Kausrud et al. (2008) illustrates a good approach to incorporating climate into predictive explanations of population change in Norwegian lemmings that involve interactions between climate and predation. The best approach in developing these kinds of explanations and formulating them into models is to determine how the model performs when additional data are obtained in the years to follow publication.

The bottom line is to avoid spurious climatic correlations by describing and evaluating mechanistic models that are based on observable biological factors. And then make predictions that can be tested in a realistic time frame. If we cannot do this, we risk publishing fairy tales rather than science.

Berteaux, D., et al. (2006) Constraints to projecting the effects of climate change on mammals. Climate Research, 32, 151-158. doi: 10.3354/cr032151

Enright, J. T. (1976) Climate and population regulation: the biogeographer’s dilemma. Oecologia, 24, 295-310.

Kausrud, K. L., et al. (2008) Linking climate change to lemming cycles. Nature, 456, 93-97. doi: 10.1038/nature07442

Myers, R. A. (1998) When do environment-recruitment correlations work? Reviews in Fish Biology and Fisheries, 8, 285-305. doi: 10.1023/A:1008828730759

Vigen, T. (2015) Spurious Correlations, Hyperion, New York City. ISBN: 978-031-633-9438

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

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

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

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

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

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

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

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

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

In Defence of Hypothesis Testing in Ecology

In two recent scientific meetings I have attended (which must remain nameless to protect the innocent), I have found myself wondering about the state of hypothesis testing in ecological science. I have always assumed that science consists of testing hypotheses, yet I would estimate roughly that 75% of the talks I have been able to attend showed no sign of any hypothesis. I need to qualify that. Some of these studies are completely descriptive – what species of ferns occur in national park X? Much effort now is devoted to sequencing genomes, the ultimate in descriptive biology. This kind of research work can be classified as alpha-biology, basic description which is necessary before any problems can be formulated. In my particular specialty of population cycles in mammals, much descriptive work had to be carried out to recognize the phenomenon of “cycles”. But then the question arises – at what point should we stop simple descriptions of mammal populations rising and falling? Do we need to study the dynamics of every rodent species that exists? Or in genetics, is our objective to sequence the genome of every species on earth? My point is that after we have enough basic description, we should move into hypothesis testing, or asking why some phenomenon occurs, the mechanisms behind the simple observations. The important point here is that we should not have a single hypothesis or explanation for any set of observations but rather several alternative hypotheses. As a simple example, if we find our favourite plant species is declining in abundance, we should not simply try to connect this decline with climatic warming without having a series of alternative explanations with the emphasis that our observations or experiments should be capable of distinguishing among the alternative hypotheses.

The alternative argument is that we do not know enough about ecological systems to set up a series of credible alternative hypotheses. It is quite possible to go on describing events endlessly in science in the hope that some wisdom will emerge. I do not think this is a profitable use of time or money in science. In ecology in particular I would argue that there is not a single question one can ask that cannot be answered by at least 2 or 3 different mechanistic hypotheses. Our job is to articulate these alternatives and to do whatever studies or experiments are needed to distinguish among them. Of course it is always possible that the correct answer is not among the 2 or 3 hypotheses we suggest at the start of an investigation, and this is often why one study leads to a further one. Consequently we cannot accept statements like “I have no idea why this observation has occurred”. Such a statement means you have not thought deeply enough about what you are studying. Ecological surprises certainly occur while we study any particular community or ecosystem, but we know enough now to suggest several possible mechanisms by which any ecological surprise might be generated.

So I think it incumbent on every ecologist to ask (1) what is the problem or question my research is addressing? And (2) what probable mechanisms can be invoked as the cause of this problem or the answer to this question. Vagueness may be a virtue in politics but it is not a virtue in science. And I look forward to future conferences in which every paper specifies a precise hypothesis and alternative hypotheses. Chamberlin (1897) stated the case for multiple hypotheses, Karl Popper (1963) asked very specifically what your hypothesis forbids from happening, and John Platt (1964) pulled it together in a critical paper. There was important work done before the Iphone was invented. Good reading.

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

Platt, J. R. 1964. Strong inference. Science 146:347-353.

Popper, K. R. 1963. Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge and Kegan Paul, London.