Category Archives: Biology Education

On Ecological Models and the Coronavirus

We are caught up now in a coronavirus pandemic with an unknown end point. There is a great deal now available about COVID-19, and I want to concentrate on the models of this pandemic that currently fill our media channels. In particular I want to use the current situation to reflect on the role of mathematical models in helping to solve ecological problems and make predictions of future trends. To oversimplify greatly, the scientific world is aligned along an axis from those supporting simple models to those tied up in complex multifactor models. To make this specific, the simple epidemic model approach provides us with a coronavirus model that has three classes of actors – susceptible, infected, and recovered individuals, and one key parameter, the relative infection rate of one person to another. If you as an infected person pass on the disease to more than one additional person, the pandemic will grow. If you pass the disease on to less than one person (on average), the pandemic will collapse. Social distancing will flip us into the favourable state of declining infections. There is a similar sort of model in ecology for predator-prey interactions, called the Lotka-Volterra model, in which one predator eating one prey species will change the population size of both depending on the rate of killing of the predator and the rate of reproduction of the prey.

So far so good. We can all have an intuitive understanding of such simple models, but of course the critics rise up in horror with the cry that “the devil is in the details”. And indeed this is also a universal truth. All humans are not equally affected by COVID-19. Older people do poorly, young children appear to be little bothered by the virus. All prey individuals in nature are also not equally susceptible to being caught by a predator. Young prey may not run as fast as adults, poorly fed prey in winter may run more slowly than well fed animals. The consequences of this ‘inequality’ is what leads to the need for an increasing investment in scientific research. We can pretend the world is simple and the virus will just “go away”, and a simple view of predation that “larger animals eat smaller animals” could fail to recognize that a small predator might drive a dinosaur species extinct if the small predator eats only the eggs of the prey and avoids the big adults. The world is complicated, and that is what makes it both interesting to many and infuriating to some who demand simplicity.

One of the purposes of a mathematical model is to allow predictions of coming events, and we hear much of this with the COVID-19 models currently in circulation. A simple principle is “all models are wrong’ but this must be matched with the corollary that in general “the simpler the model the more likely it is to provide poor forecasts. But there is a corollary that might be called the “Carl Walters’ Law” that there is some optimal level of complexity for a good result, and too much complexity is also a recipe for poor projections. The difficulty is that we can often only find this optimal point after the fact, so that we learn by doing. This does not sit well with politicians and business-people who demand “PRECISE PRECISION PROMPTLY!” 

These uncertainties reflect on to our current decision making in the coronavirus pandemic, in issues to fight climate change, and in the conservation of threatened species and ecosystems. Our models, our scientific understanding, and our decisions are never perfect or complete, and as we see so clearly with COVID-19 the science in particular can be pushed but cannot be rushed, even when money is not limiting. The combination of planning, judgement and knowledge that we call wisdom may come more slowly than we wish. Meanwhile there are many details that need investigation.  

Adam, D. (2020) Modelling the Pandemic: The simulations driving the world’s response to COVID-19. Nature, 580, 316-318. Doi: 10.1038/d41586-020-01003-6 

Neher, R.A., Dyrdak, R., Druelle, V., Hodcroft, E.B. & Albert, J. (2020) Potential impact of seasonal forcing on a SARS-CoV-2 pandemic. Swiss Medical Weekly 150, w20224. Doi: 10.4414/smw.2020.20224.

Xu, B., Cai, J., He, D., Chowell, G. & Xu, B. (2020) Mechanistic modelling of multiple waves in an influenza epidemic or pandemic. Journal of Theoretical Biology, 486, 110070. Doi: 10.1016/j.jtbi.2019.110070.

On Fires in Australia

The fires of Australia in their summer 2019-20 are in the news constantly, partly because the media survive on death and destruction and partly because to date we have never seen a whole continent burn up. It is hardly a ‘Welcome to the Anthropocene”  kind of event to celebrate, and the northern media display the fires as nearly all news of the Southern Hemisphere is treated, something unusual, often bad, but of no general importance to the real world of the Northern Hemisphere.

What do we hear from a cacophony of public opinion?

“Nothing unusual. We have always had fires in the past. Why in 1863…..”
“Nothing to do with climate change. Climate has always been changing….(see point 1)
“Main cause had been Green Policies. If we had more forestry, there would have been many fewer trees to burn….”
“Inadequate controlled burning because of the Greens’ policies….(see point 3)
“Why doesn’t the Government do something about this?”
“Fortunately these fires are a rare event and not likely to occur again…….

In reply an ecologist might offer these facts:

  1. Much research by plant geographers and ecologists have shown how many plant communities are dominated by fire. The boreal forest is one, the chaparral of Southern California is another, the grasslands of Africa and the Great Plains of the USA are yet more.
  2. By preventing fire in these communities over time the fuel load builds up so that, should there be a subsequent fire, the fire severity would be very high.
  3. By building houses, towns, and cities in these plant communities fire danger increases, and an active plan of fire management must be implemented. Most of these plans are effective for normal fires but for extreme conditions no fire management plan is effective.
  4. Climate change is now producing extreme conditions that were once very rare but are now commonly achieved. With no rainfall, high winds, and temperatures over 40-45ºC fires cannot be contained. Severe fires generate their own weather that accelerates fire spread with embers being blown kilometers ahead of the active fire front.
  5. The long-term plan to have controlled patch burns to relieve these fire conditions are impossible to implement because they require no wind, low temperatures, and considerable person-power to prevent controlled burns getting away from containment lines should the weather change.

Since a sizeable fraction of dangerous fires are deliberately set by humans, methods to detect and prevent this behaviour could help in some cases. Infrastructure such as power lines could be upgraded to reduce the likelihood of falling power poles and lines shorting out. All this will cost money, and the less the fire frequency, the fewer the people willing to pay more taxes to reduce public risk. Some serious thinking is needed now because Australia 2020 is just the start of a century of fire, drought, floods, and high winds. We do not need the politicians of 2050 telling us “why didn’t someone warn us?

There is a very large literature on fire in human landscapes (e.g. Gibbons et al. 2012), and I include only a few references here. They illustrate that the landscape effects of fire are multiple and area specific. Much more field research is needed, and landscape ecology has a vital role to play in understanding and managing the interface of humans and fire.

Badia, A. et al. (2019). Wildfires in the wildland-urban interface in Catalonia: Vulnerability analysis based on land use and land cover change. Science of The Total Environment 673, 184-196. doi: 10.1016/j.scitotenv.2019.04.012.

Gibbons, P, et. al. (2012) Land management practices associated with house loss in wildfires. PLoS ONE 7(1): e29212.

Gustafsson, L. et al. (2019). Rapid ecological response and intensified knowledge accumulation following a north European mega-fire. Scandinavian Journal of Forest Research 34, 234-253. doi: 10.1080/02827581.2019.1603323.

Minor, J. and Boyce, G.A. (2018). Smokey Bear and the pyropolitics of United States forest governance. Political Geography 62, 79-93. doi: 10.1016/j.polgeo.2017.10.005.

Ramage, B.S., O’Hara, K.L., and Caldwell, B.T. (2010). The role of fire in the competitive dynamics of coast redwood forests. Ecosphere 1(6), art20. doi: 10.1890/ES10-00134.1.

The Central Predicament of Ecological Science

Ecology like all the hard sciences aims to find generalizations that are eternally true. Just as physicists assume that the universal law of gravitation will still be valid 10,000 years from now, so do ecologists assume that we can find laws or generalizations for populations and ecosystems that will be valid into the future. But the reality for ecological science is quite different. If the laws of ecology depend on the climate being stable, soil development being ongoing, evolution being optimized, and extinction being slow in human-generation time, we are in serious trouble.

Paleoecology is an important subdiscipline of ecology because, like human history, we need to understand the past. But the generalizations of paleoecology may be of little use to understand the future changes the Earth faces for one major reason – human disturbance of both climate and landscapes. Climates are changing due to rising greenhouse gases that have a long half-life. Land and water are being appropriated by a rising human population that is very slow to stabilize, so natural habitats are continually lost. There is little hope in the absence of an Apocalypse that these forces will alleviate during the next 200 years. Given these changes in the Anthropocene where does ecology sit and what can we do about it?

If climate is a major driver of ecological systems, as Andrewartha and Birch (1954) argued (to the scorn of the Northern Hemisphere ecologists of the time), the rules of the past will not necessarily apply to a future in which climate is changing. Plant succession, that slow and orderly process we now use to predict future communities, will change in speed and direction under the influence of climatic shifts and the introduction of new plant species, plant pests, and diseases that we have little control over. Technological optimists in agriculture and forestry assume that by genetic manipulations and proper artificial selection we can outwit climate change and solve pest problems, and we can only hope that they are successful. Understanding all these changes in slow-moving ecosystems depends on climate models that are accurate in projecting future climate changes. Success to date has been limited because of both questionable biology and poor statistical procedures in climate models (Frank 2019; Kumarathunge et al. 2019; Yates et al. 2018).

If prediction is the key to ecological understanding, as Houlahan et al. (2017) have cogently argued, we are in a quandary if the models that provide predictions wander with time to become less predictive. Yates et al. (2018) have provided an excellent review of the challenges of making good models for ecological prediction. As such their review is either encouraging – ‘here are the challenges in bold type’ – or terribly depressing – ‘where are the long-term, precise data for predictive model evaluation?’ My colleagues and I have spent 47 years trying to provide reliable data on one small part of the boreal forest ecosystem, and the models we have developed to predict changes in this ecosystem are probably still too imprecise to use for management. Additional years of observations produce some ecosystem states that have been predictable but other changes that we have never seen before over this time frame of nearly 50 years.

In contrast to the optimism of Yates et al. (2018), Houlahan et al. (2017) state that:

Ecology, with a few exceptions, has abandoned prediction and therefore the ability to demonstrate understanding. Here we address how this has inhibited progress in ecology and explore how a renewed focus on prediction would benefit ecologists. The lack of emphasis on prediction has resulted in a discipline that tests qualitative, imprecise hypotheses with little concern for whether the results are generalizable beyond where and when the data were collected.  (page 1)

I see this difference in views as a dilemma because despite much talk, there is little money or interest in the field work that would deliver reliable data for models in order to test their accuracy in predictions at small and large scales. An example this year is the failure of the expected large salmon runs to the British Columbia fishery, with model failure partly due to the lack of monitoring in the North Pacific (; , and in contrast with Alaska runs: ). Whatever the cause of the failure of B.C. salmon runs in 2019, the lack of precision in models of a large commercial fishery that has been studied for at least 65 yeas is not a vote of confidence in our current ecological modelling.

Andrewartha, H.G. and Birch, L.C. (1954) ‘The Distribution and Abundance of Animals.’ University of Chicago Press: Chicago. 782 pp.

Frank, P. (2019). Propagation of error and the reliability of global air temperature projections. Frontiers in Earth Science 7, 223. doi: 10.3389/feart.2019.00223.

Houlahan, J.E., McKinney, S.T., Anderson, T.M., and McGill, B.J. (2017). The priority of prediction in ecological understanding. Oikos 126, 1-7. doi: 10.1111/oik.03726.

Kumarathunge, D.P., Medlyn, B.E., Drake, J.E., Tjoelker, M.G., Aspinwall, M.J., et al. (2019). Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale. New Phytologist 222, 768-784. doi: 10.1111/nph.15668.

Yates, K.L., Bouchet, P.J., Caley, M.J., Mengersen, K., Randin, C.F., Parnell, S., Fielding, A.H., Bamford, A.J., et al. (2018). Outstanding challenges in the transferability of ecological models. Trends in Ecology & Evolution 33, 790-802. doi: 10.1016/j.tree.2018.08.001.

On Random Sampling and Generalization in Ecology

Virtually every introduction to statistics book makes the point that random sampling is a critical assumption that underlies all statistical inferences. It is assumption #1 of statistical inference and it carries with it an often-hidden assumption that in trying to make your inference, you have clearly defined what the statistical population is that you are sampling. Defining the ‘population’ under consideration should perhaps be rule # 1, but that is usually left as a vague understanding in many statistical studies. As an exercise consult a series of papers on ecological field studies and see if you can find a clear statement of what the ‘population’ under consideration is. An excellent example of this kind of analysis is given by Ioannidis (2003, 2005).

The problem of random sampling does not occur in theoretical statistics and all effort is concentrated on mathematical correctness. This is illustrated well in the polls we are subjected to on political or social issues, and in the medical studies that we hear about daily. The social sciences have considered sampling for polls in much more detail that have biologists. In a historical overview (Lusinchi 2017) provides an interesting and useful analysis of how pollsters have over the years bent the science of statistical inference to their methods of polling to provide an unending flow of conclusions about who will be elected, or which coffee is better tasting. By confounding sample size with an approach to Truth and ignoring the problem of random sampling, the public has been brainwashed to believe what should be properly labeled as ‘fake news’.

What has all of this got to do with the science of ecology? Much of the data we accumulate is uncertain when we ask what is the ‘population’ to which it applies. If you are concerned about the ecology of sharks, you face the problem that most species of shark have never been studied (Ducatez 2019). If you are interested in fish populations, for example, you may find that the fish you catch with hooks are not a random sample of the fish population (Lennox et al. 2017). If you are studying the trees in a large woodlot, that may be your universe for statistical purposes. Interest then shifts to the question of how much you will generalize to other woodlots over what geographical space, a question too rarely discussed in data papers. In an ideal world we would sample several woodlots randomly selected from a larger sample of similar woodlots, so that we could infer processes that were common to woodlots in general.

There are a couple of problems that confound ecologists at this point. No series of woodlots or study sites in general are identical, so we assume they are a collective of ‘very similar’ woodlots about which we could make an inference. Alternatively, we can simply state that we wish to make inferences about only this single woodlot, it is our total population. At this point your supervisor/boss will say that he or she is not interested only in this one woodlot but much more general conclusions, so you will be cut from research funding for having too narrow an interest.

The solution is in general to study one ‘woodlot’ and then generalize to all ‘woodlots’ with no further study on your part, so that it will be up to the next generation to find out if your generalization is right or wrong. While this way of proceeding will perhaps not matter to people interested in ‘woodlots’, it might well matter greatly if your ‘population of interest’ was composed of humans considering a drug for disease treatment. We are further confounded in this era of climate change in dealing with changing ecosystems, so that a study in 2000 about coral reef fish communities could be completely different if it were repeated in 2040 as oceans warm.

Back to random sampling. I would propose that random sampling in ecological systems is impossible and cannot be achieved in a global sense. Be concerned about local processes and sample accordingly. Descriptive ecology must come to the rescue here, so that we know as background information (for example) that trees grow slower as they age, that tree growth varies from year to year, that insect attacks vary with summer temperature, and so on, and sample accordingly following your favourite statistician. There are many very useful statistical techniques and sampling designs you can use as an ecologist to achieve random sampling on a local scale, and statisticians are most useful to consult to validate the design of your field studies.

But it is important to remember that your results and conclusions even though carried out with a perfect statistical design cannot ensure that your generalizations are correct in time or in space. The use of meta-analysis can assist in validating generalizations when enough replicated studies are available, but there are problems even with this approach (Siontis and Ioannidis 2018). Continued discussion of p-values in ecology could benefit much from similar discussions in medicine where funding is higher, and replication is more common (Ioannidis 2019b; Ioannidis 2019a).

All these statistical issues provide a strong argument as to why ecological field studies and experiments should never stop, and all our studies and conclusions are temporary signposts along a path that is never ending.

Ducatez, S. (2019). Which sharks attract research? Analyses of the distribution of research effort in sharks reveal significant non-random knowledge biases. Reviews in Fish Biology and Fisheries 29, 355-367. doi: 10.1007/s11160-019-09556-0.

Ioannidis, J.P.A. (2005). Contradicted and initially stronger effects in highly cited clinical research. Journal of the American Medical Association 294, 218-228. doi: 10.1001/jama.294.2.218.

Ioannidis, J.P.A. (2005). Why most published research findings are false. PLOS Medicine 2, e124. doi: 10.1371/journal.pmed.0020124.

Ioannidis, J.P.A. (2019a). What have we (not) learnt from millions of scientific papers with p values? American Statistician 73, 20-25. doi: 10.1080/00031305.2018.1447512.

Ioannidis, J.P.A. (2019b). The importance of predefined rules and prespecified statistical analyses: do not abandon significance. Journal of the American Medical Association 321, 2067-2068. doi: 10.1001/jama.2019.4582.

Lennox, R.J., et al. (2017). What makes fish vulnerable to capture by hooks? A conceptual framework and a review of key determinants. Fish and Fisheries 18, 986-1010. doi: 10.1111/faf.12219.

Lusinchi, D. (2017). The rhetorical use of random sampling: crafting and communicating the public image of polls as a science (1935-1948). Journal of the History of the Behavioral Sciences 53, 113-132. doi: 10.1002/jhbs.21836.

Siontis, K.C. and Ioannidis, J.P.A. (2018). Replication, duplication, and waste in a quarter million systematic reviews and meta-analyses. Circulation Cardiovascular Quality and Outcomes 11, e005212. doi: 10.1161/circoutcomes.118.005212.

Why do Scientists Reinvent Wheels?

We may reinvent wheels by repeating research that has already been completed and published elsewhere. In one sense there is no great harm in this, and statisticians would call it replication of the first study, and the more replication the more we are convinced that the results of the study are robust. There is a problem when the repeated study reaches different results from the first study. If this occurs, there is a need to do another study to determine if there is a general pattern in the results, or if there are different habitats with different answers to the question being investigated. But after a series of studies is done, it is time to do something else since the original question has been answered and replicated. Such repeated studies are often the subject of M.Sc. or Ph.D. theses which have a limited 1-3-year time window to reach completion. The only general warning for these kinds of replicated studies is to read all the old literature on the subject. There is a failure too often on this and reviewers often notice missing references for a repeated study. Science is an ongoing process but that does not mean that all the important work has been carried out in the last 5 years.

There is a valid time and place to repeat a study when the habitat for example has been greatly fragmented or altered by human land use or when climate change has made a strong impact on the ecosystem under study. The problem in this case is to have an adequate background of data that allows you to interpret your current data. If there is a fundamental problem with ecological studies to date it is that we have an inadequate baseline for comparison for many ecosystems. We can conclude that a particular ecosystem is losing species (due to land use change or climate) only if we know what species comprised this ecosystem in past years and how much the species composition fluctuated over time. The time frame desirable for background data may be only 5 years for some species or communities but for many communities it may be 20-40 years or more. We are too often buried in the assumption that communities and ecosystems have been in equilibrium in the past so that any fluctuations now seen are unnatural. This time frame problem bedevils calls for conservation action when data are deficient.

The Living Planet Report of 2018 has been widely quoted as stating that global wildlife populations have decreased 60% in the last 4 decades. They base their analysis on the changes in 4000 vertebrate species. There are about 70,000 vertebrate species on Earth, so this statement is based on about 6% of the vertebrates. The purpose of the Living Planet Report is to educate us about conservation issues and encourage political action. No ecologist in his or her right mind would question this 60% quotation lest they be cast out of the profession, but it is a challenge to the graduate students of today to analyze this statistic to determine how reliable it is. We all ‘know’ that elephants and rhinos are declining but they are hardly a random sample. The problem in a nutshell is that we have reliable long-term data on perhaps 0.01% or less of all vertebrate species. By long term I suggest we set a minimal limit of 10 generations. As another sobering test of these kinds of statements I suggest picking your favorite animal and reading all you can on how to census the species and then locate how many studies of this species meet the criteria of a good census. The African elephant could be a good place to start, since everyone is convinced that it has declined drastically. The information in the Technical Supplement is a good starting point for a discussion about data accuracy in a conservation class.

My advice is that ecologists should not without careful thought repeat studies that have already been carried out many times on common species . Look for gaps in the current wisdom. Many of our species of concern are indeed declining and need action but we need knowledge of what kinds of management actions are helpful and possible. Many of our species have not been studied long enough to know if they are under threat or not. It is not helpful to ‘cry wolf’ if indeed there is no wolf there. We need precision and accuracy now more than ever.

World Wildlife Fund. 2018. Living Planet Report – 2018: Aiming Higher. Grooten, M. and Almond, R.E.A.(Eds). WWF, Gland, Switzerland. ISBN: 978-2-940529-90-2.

A Few Rules for Giving a Lecture

I’ve discussed some of the rules for graphics in publications and preparing posters before but I feel it’s time for a more general discussion of lecturing for scientists. All of us have suffered through at least one poor lecture at scientific meetings and some of us many more. If you are a scientist or educator and must give a short talk or a long lecture, you should not panic since there are just a few rules that can help in communication and reduce potential suffering for you and the audience.

First, let the audience know what precisely you will be discussing in your talk – what is the problem and what you are going to present about it. The opening 2 minutes of your talk is when you can lose two-thirds of your audience. If you are a politician, this may be what you wish to happen, but if you are a scientist do not go there. You do not need to begin by stating the obvious – we all know that the earth is round and biodiversity is under threat – but dive into the details of the particular problem you are going to resolve.

Second, if you are showing powerpoints, follow a few simple rules or again you will lose your audience. Do not put more than a few dot points on a slide, or more than 1 or at most 2 graphs or maps. You must not spend more than 1-2 minutes on each slide or those of us with a sleep deficit will have a power nap instead of listening. Use writing in large letters only so they can be read from the back of the room.

Thirdly, do not use acronyms anywhere. Most of us do not know that DAE means ‘demographic Allee effect’ or that RR means ‘log response ratio’ so if your slides contain GDD or DOC or HBL or ODE you may be losing your audience. In most cases it is possible to write out the meaning of these acronyms without crowding the slide.

Finally, sum up at the end of your talk what you have achieved and what more might be required to completely answer your opening question or problem. The audience will typically take home one or two points you have raised in your talk. Do not expect miracles.

There is an enormous literature on powerpoints and lecturing, much of it more relevant to medical education than to biology. I have put together 8 specific rules for powerpoints and I list these here:

  1. Never use a dark background for your slides. The reason is that in rooms that have too much light, the audience will be unable to read white printing on a dark background. It is best to use black printing on a white or pastel background.
  2. Use at least 28 point font on every slide. If you think this is too large a font, project your lecture and go back 10 meters in a not-too-dark room, and see if you can read what you have written.
  3. Never have more than one graph on a slide. It is impossible to digest 4 or 8 graphs on one slide, and the audience can never read the labels on the axes.
  4. If you use colour on your slide for different lines or points, make the colours strong and check that you can distinguish them from 10 meters.
  5. Never use a table on a slide with more than 4 columns and 4 rows. No one can read most tables used in most talks because the font size is typically too small.
  6. Allow at least one minute to talk about what is the message on each slide. If you are giving a 15-minute talk, you should have no more than 12 slides.
  7. Do not use a photo as a background for a slide. Use photos as photos to make a particular point, and text as text. Do not in general put several photos on one slide.
  8. Do not use animation in your powerpoints unless you have already gotten an Academy Award for your work. If you need to use a short video, imbed it properly and test that it really works and is clear.

I think these two papers make additional points that are useful in developing lectures. Good luck and an early thank you from your audiences.

Blome, C., H. Sondermann, and M. Augustin. 2017. Accepted standards on how to give a Medical Research Presentation: a systematic review of expert opinion papers. GMS Journal for Medical Education 34: doc11. doi: 10.3205/zma001088

Harolds, J. A. 2012. Tips for giving a memorable presentation, Part IV: Using and composing PowerPoint slides. Clinical Nuclear Medicine 37:977-980. doi: 10.1097/RLU.0b013e3182614219

On Immigration – An Ecological Perspective

There is a great deal of discussion in the news about immigration into developed countries like Canada, USA, and Europe. The perspective on this important issue in the media is virtually entirely economic and social, occasionally moral, but in my experience almost never ecological. There are two main aspects of immigration that are particularly ecological – defining sustainable populations and protecting ecosystems from biodiversity loss. These ecological concerns ought to be part of the discussion.

Sustainability is one of the sciences current buzz words. As I write this, in the Web of Science Core Collection I can find 9218 scientific papers published already in 2017 that appear under the topic of ‘sustainability’. No one could read all these, and the general problem with buzz words like ‘sustainability’ is that they tend to be used so loosely that they verge on the meaningless. Sustainability is critical in this century, but as scientists we must specify the details of how this or that public policy really does increase some metric of sustainability.

There have been several attempts to define what a sustainable human population might be for any country or the whole Earth (e.g. Ehrlich 1996, Rees and Wackernagel 2013) and many papers on specific aspects of sustainability (e.g. Hilborn et al. 2015, Delonge et al. 2016). The controversy arises in specifying the metric of sustainability. The result is that there is no agreement particularly among economists and politicians about what to target. For the most part we can all agree that exponential population growth cannot continue indefinitely. But when do we quit? In developed countries the birth rate is about at equilibrium, and population growth is achieved in large part by immigration. Long term goals of achieving a defined sustainable population will always be trumped in the short term by changes in the goal posts – long term thinking seems almost impossible in our current political systems. One elephant in the room is that what we might define now as sustainable agriculture or sustainable fisheries will likely not be sustainable as climates change. Optimists predict that technological advances will greatly relieve the current limiting factors so all will be well as populations increase. It would seem to be conservative to slow our population growth, and thus wait to see if this optimism is justified (Ehrlich and Ehrlich 2013).

Few developed countries seem to have set a sustainable population limit. It is nearly impossible to even suggest doing this, so this ecological topic disappears in the media. One possible way around this is to divert the discussion to protecting ecosystems from biodiversity loss. This approach to the overall problem might be an easier topic to sell to the public and to politicians because it avoids the direct message about population growth. But too often we run into a brick wall of economics even when we try this approach to sustainability because we need jobs for a growing population and the holy grail of continued economic growth is a firm government policy almost everywhere (Cafaro 2014, Martin et al. 2016). At present this biodiversity approach seems to be the best chance of convincing the general public and politicians that action is needed on conservation issues in the broad sense. And by doing this we can hopefully obtain action on the population issue that is blocked so often by political and religious groups.

A more purely scientific issue is the question why the concept of a sustainable population is thought to be off limits for a symposium at a scientific meeting? In recent years attempts to organize symposia on sustainable population concepts at scientific conferences have been denied by the organizers because the topic is not considered a scientific issue. Many ecologists would deny this because without a sustainable population, however that is defined, we may well face social collapse (Ehrlich and Ehrlich 2013).

What can we do as ecologists? I think shying away from these population issues is impossible because we need to have a good grounding in population arithmetic to understand the consequences of short-term policies. It is not the ecologist’s job to determine public policy but it is our job to question much of the pseudo-scientific nonsense that gets repeated in the media every day. At least we should get the arithmetic right.

Cafaro, P. (2014) How Many Is Too Many? The Progressive Argument for Reducing Immigration into the United States. University of Chicago Press, Chicago. ISBN: 9780226190655

DeLonge, M.S., Miles, A. & Carlisle, L. (2016) Investing in the transition to sustainable agriculture. Environmental Science & Policy, 55, 266-273. doi: 10.1016/j.envsci.2015.09.013

Ehrlich, A.H. (1996) Towards a sustainable global population. Building Sustainable Societies (ed. D.C. Pirages), pp. 151-165. M. E. Sharpe, London. ISBN: 1-56324-738-0, 978-1-56324-738-5

Ehrlich, P.R. & Ehrlich, A.H. (2013) Can a collapse of global civilization be avoided? Proceedings of the Royal Society B: Biological Sciences, 280, 20122845. doi: 10.1098/rspb.2012.2845

Hilborn, R., Fulton, E.A., Green, B.S., Hartmann, K. & Tracey, S.R. (2015) When is a fishery sustainable? Canadian Journal of Fisheries and Aquatic Sciences, 72, 1433-1441. doi: 10.1139/cjfas-2015-0062

Hurlbert, S.H. (2013) Critical need for modification of U.S. population policy. Conservation Biology, 27, 887-889. doi: 10.1111/cobi.12091

Martin, J.-L., Maris, V. & Simberloff, D.S. (2016) The need to respect nature and its limits challenges society and conservation science. Proceedings of the National Academy of Sciences, 113, 6105-6112. doi: 10.1073/pnas.1525003113

Rees W.E. &, Wackernagel, M. (2013). The shoe fits, but the footprint is larger than Earth. PLOS Biology 11, e1001701. doi: 10.1371/journal.pbio.1001701

On Defining a Statistical Population

The more I do “field ecology” the more I wonder about our standard statistical advice to young ecologists to “random sample your statistical population”. Go to the literature and look for papers on “random environmental fluctuations”, or “non-random processes”, or “random mating” and you will be overwhelmed with references and biology’s preoccupation with randomness. Perhaps we should start with the opposite paradigm, that nothing in the biological world is random in space or time, and then the corollary that if your data show a random pattern or random mating or whatever random, it means you have not done enough research and your inferences are weak.

Since virtually all modern statistical inference rests on a foundation of random sampling, every statistician will be outraged by any concerns that random sampling is possible only in situations that are scientifically uninteresting. It is nearly impossible to find an ecological paper about anything in the real world that even mentions what their statistical “population” is, what they are trying to draw inferences about. And there is a very good reason for this – it is quite impossible to define any statistical population except for those of trivial interest. Suppose we wish to measure the heights of the male 12-year-olds that go to school in Minneapolis in 2017. You can certainly do this, and select a random sample, as all statisticians would recommend. And if you continued to do this for 50 years, you would have a lot of data but no understanding of any growth changes in 12-year-old male humans because the children of 2067 in Minneapolis would be different in many ways from those of today. And so, it is like the daily report of the stock market, lots of numbers with no understanding of processes.

Despite all these ‘philosophical’ issues, ecologists carry on and try to get around this by sampling a small area that is considered homogeneous (to the human eye at least) and then arm waving that their conclusions will apply across the world for similar small areas of some ill-defined habitat (Krebs 2010). Climate change may of course disrupt our conclusions, but perhaps this is all we can do.

Alternatively, we can retreat to the minimalist position and argue that we are drawing no general conclusions but only describing the state of this small piece of real estate in 2017. But alas this is not what science is supposed to be about. We are supposed to reach general conclusions and even general laws with some predictive power. Should biologists just give up pretending they are scientists? That would not be good for our image, but on the other hand to say that the laws of ecology have changed because the climate is changing is not comforting to our political masters. Imagine the outcry if the laws of physics changed over time, so that for example in 25years it might be that CO2 is not a greenhouse gas. Impossible.

These considerations should make ecologists and other biologists very humble, but in fact this cannot be because the media would not approve and money for research would never flow into biology. Humility is a lost virtue in many western cultures, and particularly in ecology we leap from bandwagon to bandwagon to avoid the judgement that our research is limited in application to undefined statistical populations.

One solution to the dilemma of the impossibility of random sampling is just to ignore this requirement, and this approach seems to be the most common solution implicit in ecology papers. Rabe et al. (2002) surveyed the methods used by management agencies to survey population of large mammals and found that even when it was possible to use randomized counts on survey areas, most states used non-random sampling which leads to possible bias in estimates even in aerial surveys. They pointed out that ground surveys of big game were even more likely to provide data based on non-random sampling simply because most of the survey area is very difficult to access on foot. The general problem is that inference is limited in all these wildlife surveys and we do not know the ‘population’ to which the numbers derived are applicable.

In an interesting paper that could apply directly to ecology papers, Williamson (2003) analyzed research papers in a nursing journal to ask if random sampling was utilized in contrast to convenience sampling. He found that only 32% of the 89 studies he reviewed used random sampling. I suspect that this kind of result would apply to much of medical research now, and it might be useful to repeat his kind of analysis with a current ecology journal. He did not consider the even more difficult issue of exactly what statistical population is specified in particular medical studies.

I would recommend that you should put a red flag up when you read “random” in an ecology paper and try to determine how exactly the term is used. But carry on with your research because:

Errors using inadequate data are much less than those using no data at all.

Charles Babbage (1792–1871

Krebs CJ (2010). Case studies and ecological understanding. Chapter 13 in: Billick I, Price MV, eds. The Ecology of Place: Contributions of Place-Based Research to Ecological Understanding. University of Chicago Press, Chicago, pp. 283-302. ISBN: 9780226050430

Rabe, M. J., Rosenstock, S. S. & deVos, J. C. (2002) Review of big-game survey methods used by wildlife agencies of the western United States. Wildlife Society Bulletin, 30, 46-52.

Williamson, G. R. (2003) Misrepresenting random sampling? A systematic review of research papers in the Journal of Advanced Nursing. Journal of Advanced Nursing, 44, 278-288. doi: 10.1046/j.1365-2648.2003.02803.x


On Scientific Conferences

Should we ban scientific conferences and save the money for better science? What a terrible thought you would say if you were 25 years old, what a great idea you might say if you were 60 years old and have just come back from a conference with 9000 attendees and 30 concurrent sessions. So, there is no simple answer. Let us try to think of some rules of thumb if you are organizing a scientific conference. Since I am an ecologist I will talk largely about ecological meetings. There is already much interesting literature on this broad question (Zierath 2016, Blome et al. 2017, Hicke et al. 2017). For all I know conferences with 9000 registrants are ideal in neurobiology but in my opinion probably not useful in ecology.

Why have a conference? Simple, to transmit information among delegates. But you can do this more efficiently by reading current papers in the literature. So a conference is useful only if you get new insights that are not yet published, the cutting edge of science. Such insights are more likely to come from conferences that are spaced at 3-5 year intervals, a time frame in which some proper ecological research can be done. And insights are more likely to come from meetings that are narrow in scope to one’s immediate area of interest.

A second good reason for a conference is to meet people in your area of research. This is likely to be more successful if the meeting is small, perhaps a maximum of 150 attendees. This is the general approach of the Gordon Conferences. Meeting people is more difficult with larger conferences because, if there are multiple concurrent sessions, much time is spent moving among sessions and fewer people get the same view of scientific advances in an area. As one eminent ecologist pointed out to me, really important people do not go to any of the talks at conferences but rather socialize and conduct their own mini meetings near the coffee bar.

Organizing a conference is an exercise in utter frustration requiring the dictatorial behaviour of an army general. The general rule is the more talks the better, and never have a talk longer than 15 minutes lest someone get bored. In fact, speed talks are now the rage and you can have 3 minutes to tell the audience about what you are doing or have done. Perhaps if we are moving in this direction we should just have the conference via youtube so we could sit at home and see what parts of it we wanted to watch. If we add ‘tweets’ to conferences (Orizaola and Valdes 2015), we would certainly be following some of our world leaders for better or worse.

I have not been able to find anyone who would dare to calculate the financial cost of any conference and to try to construct a cost benefit ratio for a meeting. The argument would be that the costs can be calculated but the benefits are intangible, somewhat reminiscent of the arguments of our military leaders who demand more financial resources to achieve vague benefits. These concerns disappear if we consider a conference as a scientific tea party rather than an intellectual event. Perhaps we need a social science survey at the end of each conference with the attendees required to list the 5 major advances they obtained from the conference.

All these concerns convince me that we should restrict scientific conferences to small meetings on particular topics at relatively long intervals. Large conferences, should they seem desirable, should consist largely of longer plenary talks that synthesize the status of a specific area of ecology and provide a critique of current knowledge and suggestions of what to do next. These kinds of plenary talks are equivalent to synthesis papers in scientific journals, the kinds of papers that are all too rare in current journals.

One important consequence of scientific meetings can be to reach out to the public with evening lectures on topics of global concern (Hicke et al. 2017). Where it is feasible this recommendation can be an important way of extending information to the public on topics of concern like climate change or conservation management.

Whatever is decided by ecological societies about the structure of scientific conferences, some general rules about presentations ought to be written in large letters. If you are talking at a conservation ecology meeting, you should not spend half of your talk trying to convince the audience that there is a biodiversity crisis, or that climate change is happening. And for the details of a successful conference, read my earlier Blog ( or Blome et al. (2017). This is not rocket science.

Blome, C., Sondermann, H., and Augustin, M. 2017. Accepted standards on how to give a Medical Research Presentation: a systematic review of expert opinion papers. GMS Journal for Medical Education 34(1): Doc11. doi: 10.3205/zma001088.

Hicke, J.A., Abatzoglou, J.T., Daley-Laursen, S., Esler, J., and Parker, L.E. 2017. Using scientific conferences to engage the public on climate change. Bulletin of the American Meteorological Society 98(2): 225-230. doi: 10.1175/BAMS-D-15-00304.1.

Orizaola, G., and Valdes, A.E. 2015. Free the tweet at scientific conferences. Science 350(6257): 170-171. doi: 10.1126/science.350.6257.170-c.

Zierath, J.R. 2016. Building bridges through scientific conferences. Cell 167(5): 1155-1158. doi: 10.1016/j.cell.2016.11.006.

The Conservative Agenda for Ecology

Many politicians that are conservative are true conservatives in the traditional meaning of the term. Many business people are conservative in the same way, and that is a good thing. But there exist in the world a set of conservatives that have a particularly destructive agenda based on a general belief that evidence, particularly scientific evidence, is not any more important as a basis for action than personal beliefs. Climate change is the example of the day, but there are many others from the utility of vaccinations for children, to items more to an ecologist’s interest like the value of biodiversity. In a sense this is a philosophical divide that is currently producing problems for ecologists in the countries I know most about, Canada and Australia, but possibly also in the USA and Britain.

The conservative political textbook says cut taxes and all will be well, especially for the rich and those in business, and then say ‘we have no money for ‘<fill in the blank here> ‘so we must cut funding to hospitals, schools, universities, and scientists’. The latest example I want to discuss is from the dismemberment of the Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia by the current conservative government.

CSIRO was sent up in the 1950s to do research for the betterment of the people of Australia. Throughout the 1960s, 1970s and 1980s it was one of the world premier research organizations. If you do not believe this you can look at how many important papers, awards, and the occasional Nobel Prize came out of this organization. It had at this time perhaps 8500 employees in more than 25 Divisions. Divisions varied in size but in general they would have about 200-300 scientists and technicians. Divisions were run by a Chief who was a scientist and who decided the important directions for research in his or her area, whether it be horticulture, wildlife, energy technology, animal science, or mathematics and statistics. CSIRO itself was led by eminent scientists who provided some guidance to the Divisions but left the directions of science to the Chiefs and their scientists. It was a golden development for Australian science and a model for science that was appreciated all around the world.

This of course is dreamland in today’s world. So by the late 1980s the Australian federal government began determining scientific priorities for CSIRO. We know what science is important, the new leaders said, so do this. This would work well if it was not guided by politicians and MBAs who had no scientific training and knew nothing about science past or present. Piled on this were two neo-conservative philosophies. First, science is important only if it generates money for the economy. Coal mining triumphs wildlife research. Second, science in the public interest is not to be encouraged but cut. The public interest does not generate money. Why this change happened can be declared a mystery but it seemed to happen all around the western world in the same time frame. Perhaps it had something to do with scientific research that had the obvious message that one ought to do something about climate change or protecting biodiversity, things that would cost money and might curtail business practices.

Now with the current 2014 budget in Australia we have a clear statement of this approach to ecological science. The word from on high has come down within CSIRO that, because of cuts to their budget, one goal is as follows: “Reduce terrestrial biodiversity research (“reduced investment in terrestrial biodiversity with a particular focus on rationalising work currently conducted across the “Managing Species and Natural Ecosystems in a Changing Climate” theme and the “Building Resilient Australian Biodiversity Assets” theme in these Divisions”).Translated, this means about 20% of the staff involved in biodiversity research will be retrenched and work will continue in some areas at a reduced level. At a time when rapid climate change is starting, it boggles the mind that some people at some high levels think that supporting the coal and iron ore industry with government-funded research is more important than studies on biodiversity. (If you appreciate irony, this decision comes in a week when it is discovered that the largest coal company in Australia, mining coal on crown land, had profits of $16 billion last year and paid not one cent of tax.)

So perhaps all this illustrates that ecological research and all public interest research is rather low on the radar of importance in the political arena in comparison with subsidizing business. I should note that at the same time as these cuts are being implemented, CSIRO is also cutting agricultural research in Australia so biodiversity is not the only target. One could obtain similar statistics for the Canadian scene.

There is little any ecologist can do about this philosophy. If the public in general is getting more concerned about climate change, the simplest way to deal with this concern for a politician is to cut research in climate change so that no data are reported on the topic. The same can be said about biodiversity issues. There is too much bad news that the environmental sciences report, and the less information that is available to the public the better. This approach to the biosphere is not very encouraging for our grandchildren.

Perhaps our best approach is to infiltrate at the grass roots level in teaching, tweeting, voting, writing letters, and attending political meetings that permit some discussion of issues. Someday our political masters will realize that the quality of life is more important than the GDP, and we can being to worry more about the future of biodiversity in particular and science in general.


Krebs, C.J. 2013. “What good is a CSIRO division of wildlife research anyway?” In Science under Siege: Zoology under Threat, edited by Peter Banks, Daniel Lunney and Chris Dickman, pp. 5-8. Mosman, N.S.W.: Royal Zoological Society of New South Wales.

Oreskes, Naomi, and Erik M.M. Conway. 2010. Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming. New York: Bloomsbury Press. 355 pp. ISBN 978-1-59691-610-4

Shaw, Christopher. 2013. “Choosing a dangerous limit for climate change: Public representations of the decision making process.” Global Environmental Change 23 (2):563-571. doi: 10.1016/j.gloenvcha.2012.12.012.

Wilkinson, Todd. 1998. Science Under Siege: The Politicians’ War on Nature and Truth. Boulder, Colorado: Johnson Books. 364 pp. ISBN 1-55566-211-0