Tag Archives: climate change

On Mauna Loa and Long-Term Studies

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

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

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

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

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

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

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

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

On Caribou and Hypothesis Testing

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Mountain caribou populations in western Canada have been declining for the past 10-20 years and concern has mounted to the point where extinction of many populations could be imminent, and the Canadian federal government is asking why this has occurred. This conservation issue has supported a host of field studies to determine what the threatening processes are and what we can do about them. A recent excellent summary of experimental studies in British Columbia (Serrouya et al. 2017) has stimulated me to examine this caribou crisis as an illustration of the art of hypothesis testing in field ecology. We teach all our students to specify hypotheses and alternative hypotheses as the first step to solving problems in population ecology, so here is a good example to start with.

From the abstract of this paper, here is a statement of the problem and the major hypothesis:

“The expansion of moose into southern British Columbia caused the decline and extirpation of woodland caribou due to their shared predators, a process commonly referred to as apparent competition. Using an adaptive management experiment, we tested the hypothesis that reducing moose to historic levels would reduce apparent competition and therefore recover caribou populations. “

So the first observation we might make is that much is left out of this approach to the problem. Populations can decline because of habitat loss, food shortage, excessive hunting, predation, parasitism, disease, severe weather, or inbreeding depression. In this case much background research has narrowed the field to focus on predation as a major limitation, so we can begin our search by focusing on the predation factor (review in Boutin and Merrill 2016). In particular Serrouya et al. (2017) focused their studies on the nexus of moose, wolves, and caribou and the supposition that wolves feed preferentially on moose and only secondarily on caribou, so that if moose numbers are lower, wolf numbers will be lower and incidental kills of caribou will be reduced. So they proposed two very specific hypotheses – that wolves are limited by moose abundance, and that caribou are limited by wolf predation. The experiment proposed and carried out was relatively simple in concept: kill moose by allowing more hunting in certain areas and measure the changes in wolf numbers and caribou numbers.

The experimental area contained 3 small herds of caribou (50 to 150) and the unmanipulated area contained 2 herds (20 and 120 animals) when the study began in 2003. The extended hunting worked well, and moose in the experimental area were reduced from about 1600 animals down to about 500 over the period from 2003 to 2014. Wolf numbers in the experimental area declined by about half over the experimental period because of dispersal out of the area and some starvation within the area. So the two necessary conditions of the experiment were satisfied – moose numbers declined by about two-thirds from additional hunting and wolf numbers declined by about half on the experimental area. But the caribou population on the experimental area showed mixed results with one population showing a slight increase in numbers but the other two showing a slight loss. On the unmanipulated area both caribou populations showed a continuing slow decline. On the positive side the survival rate of adult caribou was higher on the experimental area, suggesting that the treatment hypothesis was correct.

From the viewpoint of caribou conservation, the experiment failed to change the caribou population from continuous slow declines to the rapid increase needed to recover these populations to their former greater abundance. At best it could be argued that this particular experiment slowed the rate of caribou decline. Why might this be? We can make a list of possibilities:

  1. Moose numbers on the experimental area were not reduced enough (to 300 instead of to 500 achieved). Lower moose would have meant much lower wolf numbers.
  2. Small caribou populations are nearly impossible to recover because of chance events that affect small numbers. A few wolves or bears or cougars could be making all the difference to populations numbering 10-20 individuals.
  3. The experimental area and the unmanipulated area were not assigned treatments at random. This would mean to a pure statistician that you cannot make statistical comparisons between these two areas.
  4. The general hypothesis being tested is wrong, and predation by wolves is not the major limiting factor to mountain caribou populations. Many factors are involved in caribou declines and we cannot determine what they are because they change for area to area, year to year.
  5. It is impossible to do these landscape experiments because for large landscapes it is impossible to find 2 or more areas that can be considered replicates.
  6. The experimental manipulation was not carried out long enough. Ten years of manipulation is not long for caribou who have a generation time of 15-25 years.

Let us evaluate these 6 points.

#1 is fair enough, hard to achieve a population of moose this low but possible in a second experiment.

#2 is a worry because it is difficult to deal experimentally with small populations, but we have to take the populations as a given at the time we do a manipulation.

#3 is true if you are a purist but is silly in the real world where treatments can never be assigned at random in landscape experiments.

#4 is a concern and it would be nice to include bears and other predators in the studies but there is a limit to people and money. Almost all previous studies in mountain caribou declines have pointed the finger at wolves so it is only reasonable to start with this idea. The multiple factor idea is hopeless to investigate or indeed even to study without infinite time and resources.

#5 is like #3 and it is an impossible constraint on field studies. It is a common statistical fallacy to assume that replicates must be identical in every conceivable way. If this were true, no one could do any science, lab or field.

#6 is correct but was impossible in this case because the management agencies forced this study to end in 2014 so that they could conduct another different experiment. There is always a problem deciding how long a study is sufficient, and the universal problem is that the scientists or (more likely) the money and the landscape managers run out of energy if the time exceeds about 10 years or more. The result is that one must qualify the conclusions to state that this is what happened in the 10 years available for study.

This study involved a heroic amount of field work over 10 years, and is a landmark in showing what needs to be done and the scale involved. It is a far cry from sitting at a computer designing the perfect field experiment on a theoretical landscape to actually carrying out the field work to get the data summarized in this paper. The next step is to continue to monitor some of these small caribou populations, the wolves and moose to determine how this food chain continues to adjust to changes in prey levels. The next experiment needed is not yet clear, and the eternal problem is to find the high levels of funding needed to study both predators and prey in any ecosystem in the detail needed to understand why prey numbers change. Perhaps a study of all the major predators – wolves, bears, cougars – in this system should be next. We now have the radio telemetry advances that allow satellite locations, activity levels, timing of mortality, proximity sensors when predators are near their prey, and even video and sound recording so that more details of predation events can be recorded. But all this costs money that is not yet here because governments and people have other priorities and value the natural world rather less than we ecologists would prefer. There is not yet a Nobel Prize for ecological field research, and yet here is a study on an iconic Canadian species that would be high up in the running.

What would I add to this paper? My curiosity would be satisfied by the number of person-years and the budget needed to collect and analyze these results. These statistics should be on every scientific paper. And perhaps a discussion of what to do next. In much of ecology these kinds of discussions are done informally over coffee and students who want to know how science works would benefit from listening to how these informal discussions evolve. Ecology is far from simple. Physics and chemistry are simple, genetics is simple, and ecology is really a difficult science.

Boutin, S. and Merrill, E. 2016. A review of population-based management of Southern Mountain caribou in BC. {Unpublished review available at: http://cmiae.org/wp-content/uploads/Mountain-Caribou-review-final.pdf

Serrouya, R., McLellan, B.N., van Oort, H., Mowat, G., and Boutin, S. 2017. Experimental moose reduction lowers wolf density and stops decline of endangered caribou. PeerJ  5: e3736. doi: 10.7717/peerj.3736.

 

On Immigration – An Ecological Perspective

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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 Ecology and Economics

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Economics has always been a mystery to me, so if you are an economist you may not like this blog. Many ecologists and some economists have written elegantly about the need for a new economics that includes the biosphere and indeed the whole world rather than just Wall Street and brings together ecology and the social sciences (e.g. Daily et al. 1991, Haly and Farley 2011, Brown et al. 2014, Martin et al. 2016). Several scientists have proposed measures that indicate how our current usage of natural resources is unsustainable (Wackernagel and Rees 1996, Rees and Wackernagel 2013). But few influential people and politicians appear to be listening, or if they are listening they are proceeding at a glacial pace at the same time as the problems that have been pointed out are racing at breakneck speed. The operating paradigm seems to be ‘let the next generation figure it out’ or more cynically ‘we are too busy buying more guns to worry about the environment’.

Let me discuss Canada as a model system from the point of view of an ecologist who thinks sustainability is something for the here and now. Start with a general law. No country can base its economy on non-renewable resources. Canada subsists by mining coal, oil, natural gas, and metals that are non-renewable. It also makes ends meet by logging and agricultural production. And we have done well for the last 200 years doing just that. Continue on, and to hell with the grandkids seems to be the prevailing view of the moment. Of course this is ecological nonsense, and, as many have pointed out, not the path to a sustainable society. Even Canada’s sustainable industries are unsustainable. Forestry in Canada is a mining operation in many places with the continuing need to log old growth forest to be a viable industry. Agriculture is not sustainable if soil fertility is continually falling so that there is an ever-increasing need for more fertilizer, and if more agricultural land is being destroyed by erosion and shopping malls. All these industries persist because of a variety of skillful proponents who dismiss long-term problems of sustainability. The oil sands of Alberta are a textbook case of a non-renewable resource industry that makes a lot of money while destroying both the Earth itself and the climate. Again, this makes sense short-term, but not for the grandkids.

So, we see a variety of decisions that are great in the short term but a disaster in the long term. Politicians will not move now unless the people lead them and there is little courage shown and only slight discussion of the long-term issues. The net result is that it is most difficult now to be an ecologist and be optimistic of the future even for relatively rich countries. Global problems deserve global solutions yet we must start with local actions and hope that they become global. We push ahead but in every case we run into the roadblocks of exponential growth. We need jobs, we need food and water and a clean atmosphere, but how do we get from A to B when the captains of industry and the public at large have a focus on short-term results? As scientists we must push on toward a sustainable future and continue to remind those who will listen that the present lack of action is not a wise choice for our grandchildren.

Brown, J.H. et al. 2014. Macroecology meets macroeconomics: Resource scarcity and global sustainability. Ecological Engineering 65(1): 24-32. doi: 10.1016/j.ecoleng.2013.07.071.

Daily, G.C., Ehrlich, P.R., Mooney, H.A., and Erhlich, A.H. 1991. Greenhouse economics: learn before you leap. Ecological Economics 4: 1-10.

Daly, H.E., and Farley, J. 2011. Ecological Economics: Principles and Applications. 2nd ed. Island Press, Washington, D.C.

Martin, J.-L., Maris, V., and 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(22): 6105-6112. doi: 10.1073/pnas.1525003113.

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

Wackernagel, M., and W. E. Rees. 1996. Our Ecological Footprint: Reducing Human Impact on the Earth. New Society Publishers, Gabriola Island, B.C. 160 p.

On Post-hoc Ecology

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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.

On Ecological Predictions

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The gold standard of ecological studies is the understanding of a particular ecological issue or system and the ability to predict the operation of that system in the future. A simple example is the masting of trees (Pearse et al. 2016). Mast seeding is synchronous and highly variable seed production among years by a population of perennial plants. One ecological question is what environmental drivers cause these masting years and what factors can be used to predict mast years. Weather cues and plant resource states presumably interact to determine mast years. The question I wish to raise here, given this widely observed natural history event, is how good our predictive models can be on a spatial and temporal scale.

On a spatial scale masting events can be widespread or localized, and this provides some cues to the important weather variables that might be important. Assuming we can derive weather models for prediction, we face two often unknown constraints – space and time. If we can derive a weather model for trees in New Zealand, will it also apply to trees in Australia or California? Or on a more constrained geographical view, if it applied on the South Island of New Zealand will it also apply on the North Island? At the other extreme, must we derive models for every population of particular plants in different areas, so that predictability is spatially limited? We hope not and work on the assumption of more spatial generality than what we can measure on our particular small study areas.

The temporal stability of our explanations is now particularly worrisome because of climate change. If we have a good model of masting for a particular tree species in 2017, will it still be working in 2030, 2050 or 2100? A physicist would never ask such a question since a “scientific law” is independent of time. But biology in general and ecology in particular is not time independent both because of evolution and now in particular because of changing climate. But we have not faced up to whether or not we must check our “ecological laws” over and over again as the environment changes, and if we have to do this what must the time scale of rechecking be? Perhaps this question can be answered by determining the speed of potential evolutionary change in species groups. If virus diseases can evolve quickly in terms of months or years, we must be eternally vigilant to consider if the flu virus of 2017 is going to be the same as that of 2016. We should not stop virus research and say that we have sorted out some universal model that will become an equivalent of the laws of physics.

The consequences of these simple observations are not simple. One consequence is the implication that monitoring is an essential ecological activity. But in most ecological funding agencies monitoring is thought to be unscientific, not leading to progress, and more stamp collecting. So we have to establish that, like the Weather Bureau every country supports, we must have an equivalent ecological monitoring bureau. We do have these bureaus for some ecological systems that make money, like marine fisheries, but most other ecosystems are left in limbo with little or no funding on the generalized assumption that “mother or father nature will take care of itself” or expressed more elegantly by a cabinet minister who must be nameless, “there is no need for more forestry research, as we know everything we need to know already”. The urge by politicians to cut research funding lives too much in environmental research.

But ecologists are not just ‘stamp collectors’ as some might think. We need to develop generality but at a time scale and a spatial scale that is reliable and useful for the resolution of the problem that gave rise to the research. Typically for ecological issues this time scale would be 10-25 years, and a rule of thumb might be for 10 generations of the organisms being studied. For many of our questions an annual scale might be most useful, but for long-lived plants and animals we must be thinking of decades or even centuries. Some practical examples from Pacifici et al. (2013): If you study field voles (Microtus spp.) typically you can complete your studies of 10 generations in 3.5 years (on average). If you study red squirrels (Tamiasciurus hudsonicus), the same 10 generations will cost you 39 years, and if red foxes (Vulpes vulpes) 58 years. If wildebeest (Connochaetes taurinus) in the Serengeti, 10 generations will take you 80 years, and if you prefer red kangaroos (Macropus rufus) it will take about 90 years. All these estimates are very approximate but they give you an idea of what the time scale of a long-term study might be. Except for the rodent example, all these study durations are nearly impossible to achieve, and the question for ecologists is this: Should we be concerned about these time scales, or should we scale everything to the human research time scale?

The spatial scale has expanded greatly for ecologists with the advent of radio transmitters and the possibility of satellite tracking. These technological advances allow many conservation questions regarding bird migration to be investigated (e.g. Oppel et al. 2015). But no matter what the spatial scale of interest in a research or management program, variation among individuals and sites must be analyzed by means of the replication of measurements or manipulations at several sites. The spatial scale is dictated by the question under investigation, and the issue of fragmentation has focused attention on the importance of spatial movements both for ecological and evolutionary questions (Betts et al. 2014).

And the major question remains: can we construct an adequate theory of ecology from a series of short-term, small area or small container studies?

Betts, M.G., Fahrig, L., Hadley, A.S., Halstead, K.E., Bowman, J., Robinson, W.D., Wiens, J.A. & Lindenmayer, D.B. (2014) A species-centered approach for uncovering generalities in organism responses to habitat loss and fragmentation. Ecography, 37, 517-527. doi: 10.1111/ecog.00740

Oppel, S., Dobrev, V., Arkumarev, V., Saravia, V., Bounas, A., Kret, E., Velevski, M., Stoychev, S. & Nikolov, S.C. (2015) High juvenile mortality during migration in a declining population of a long-distance migratory raptor. Ibis, 157, 545-557. doi: 10.1111/ibi.12258

Pacifici, M., Santini, L., Di Marco, M., Baisero, D., Francucci, L., Grottolo Marasini, G., Visconti, P. & Rondinini, C. (2013) Database on generation length of mammals. Nature Conservation, 5, 87-94. doi: 10.3897/natureconservation.5.5734

Pearse, I.S., Koenig, W.D. & Kelly, D. (2016) Mechanisms of mast seeding: resources, weather, cues, and selection. New Phytologist, 212 (3), 546-562. doi: 10.1111/nph.14114

Ecological Alternative Facts

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It has become necessary to revise my recent ecological thinking about the principles of ecology along the lines now required in the New World Order. I list here the thirteen cardinal principles of the new ecology 2017:

  1. Population growth is unlimited and is no longer subject to regulation.
  2. Communities undergo succession to the final equilibrium state of the 1%.
  3. Communities and ecosystems are resilient to any and all disturbances and operate best when challenged most strongly, for example with oil spills.
  4. Resources are never limiting under any conditions for the 1% and heavy exploitation helps them to trickle down readily to assist the other 99%.
  5. Overexploiting populations is good for the global ecosystem because it gets rid of the species that are wimps.
  6. Mixing of faunas and floras have been shown over the last 300 years to contribute to the increasing ecological health of Earth.
  7. Recycling is unnecessary in view of recent advances in mining technology.
  8. Carbon dioxide is a valuable resource for plants and we must increase its contribution to atmospheric chemistry.
  9. Climate change is common and advantageous since it occurs from night to day, and has always been with us for many millions of years.
  10. Evolution maximizes wisdom and foresight, especially in mammals.
  11. Conservation of less fit species is an affront to alternative natural laws that were recognized during the 18th century and are now mathematically defined in the new synthetic theory of economic and ecological fitness.
  12. Scientific experiments are no longer necessary because we have computers and technological superiority.
  13. Truth in science is no longer necessary and must be balanced against equally valid post-truth beliefs.

The old ecology, now superseded, was illustrated in Krebs (2016), and is already out of date. Recommendations for other alternative ecological facts will be welcome. Please use the comments.

Krebs, C.J. (2016) Why Ecology Matters. University of Chicago Press, Chicago. 208 pp.

On Conservation

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The question of how ecology can guide decisions about conservation actions is a vexed one of which much has already been written with respect to conservation triage (Bottrill et al. 2009, Gerber 2016). The global question – what should we do now? – produces two extreme answers: (1) do nothing. The biodiversity on earth has gone through many climatic fluctuations imposed by geology and planetary physics and these forces are out of our hands. Or (2) we must protect all species because we do not know if they are important for ecosystem function. The government recognizes that (2) is impossible, and either reflects back to answer (1) or politely asks scientists to suggest what is possible to achieve with limited funding. John Wiens (2016) in an interesting discussion in the British Ecological Society Bulletin (December 2016, pp 38-39) suggests that two possible solutions to this conundrum are to get more funding for conservation to reduce this clear financial limitation, or secondly to move from the conservation of individual species to that of ecosystems. The problem he and many others recognize is that the public at large fall in love with individual species much more readily than with ecosystems. It is the same problem medical science often faces with contributions from wealthy people – attack individual diseases with my funding, not public health in general.

Ecologists face this dilemma with respect to their research agenda and research grants in general – what exactly can you achieve in 3-5 years with a small amount of money? If your research is species-specific, something useful can often be studied particularly if the threatening processes are partly understood and you adopt an experimental approach. If your research is ecosystem oriented and your funds are limited you must generally go to the computer and satellite ecology to make any short term research possible. This problem of larger scale = larger costs can be alleviated if you work in a group of scientists all addressing the same ecosystem issue. This still requires large scale funding which is not as easily obtained as ecologists might like. The government by contrast wishes more and more to see results even after only a few years, and asks whether you have answered your original question. The result is a patchwork of ecological data which too often makes no one happy.

If you want a concrete example, consider the woodland caribou of western Canada (Schneider et al. 2010). For these caribou Hebblewhite (2017) has clearly outlined a case in which the outcomes of any particular action are difficult to predict with the certainty that governments and business would be happy with. Many small herds are decreasing in size, and one path is to triage them, leaving many small herds to go extinct and trying to focus financial resources to save larger herds in larger blocks of habitat for future generations. The problem is the oil and gas industry in western Canada, and hence the battle between resources that are worth billions of dollars and a few caribou. Wolf control can serve as a short term solution, but it is expensive and temporary. Governments like action even if it is of no use in the long term; it makes good media coverage. None of these kinds of conservation decisions are scientific in nature, and must be policy decisions by governments. It flips us back into the continuum between options (1) and (2) in the opening paragraph above. And for governments policy decisions are more about jobs and money than about conservation.

The list of threatened and endangered species that make our newspapers are a tiny fraction of the diversity of species in any ecosystem. There is no question but that many of these charismatic species are declining in numbers, but the two larger questions are: will this particular species go extinct? And if this happens will this make any difference to ecosystem function? There is scarcely a single species of all that are listed as threatened and endangered for which ecologists have a good answer to either of these questions. So the fallback position to option (1) is that we have a moral obligation to protect all species. But this fallback position leads us even further out of science.

In the end we must ask as scientists what we can do with the understanding we have, and what more needs to be done to improve this understanding. Behind all this scientific research looms the elephant of climate change which we either ignore or build untestable computer models to make ‘predictions’ which may or may not occur, if only because of the time scales involved.

None of these problems prevents us from taking actions on conservation on the ground (Wiens 2016a). We know that, if we take away all the habitat, species abundances will decline and some will go extinct. Protecting habitat is the best course of action now because it needs little research to guide action. There is much to know yet about the scale of habitats that need preservation, and about how the present scale of climate change is affecting protected areas now. Short term research can be most useful for these issues. Long-term research needs to follow.

Bottrill, M.C., et al. (2009) Finite conservation funds mean triage is unavoidable. Trends in Ecology & Evolution, 24, 183-184. doi: 10.1016/j.tree.2008.11.007

Gerber, L.R. (2016) Conservation triage or injurious neglect in endangered species recovery. Proceedings of the National Academy of Sciences USA, 113, 3563-3566. doi: 10.1073/pnas.1525085113

Hebblewhite, M. (2017) Billion dollar boreal woodland caribou and the biodiversity impacts of the global oil and gas industry. Biological Conservation, 206, 102-111. doi: 10.1016/j.biocon.2016.12.014

Schneider, R.R., Hauer, G., Adamowicz, W.L. & Boutin, S. (2010) Triage for conserving populations of threatened species: The case of woodland caribou in Alberta. Biological Conservation, 143, 1603-1611. doi: 10.1016/j.biocon.2010.04.002

Wiens, J.A. (2016) Is conservation a zero-sum game? British Ecological Society Bulletin 47(4): 38-39.

Wiens, J.A. (2016a) Ecological Challenges and Conservation Conundrums: Essays and Reflections for a Changing World. John Wiley and Sons, Hoboken, New Jersey. 344 pp. ISBN: 9781118895108

On Mushrooms, Monitoring, and Prediction

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Mushrooms probably run the world but we do not know this yet. My old friend Jim Trappe from Oregon State told me this long ago, and partly as a result of this interaction we began counting mushrooms at our boreal forest sites near Kluane, Yukon in 1993, long ago and even before the iPhone was invented. Being zoologists, we never perhaps appreciated mushrooms in the forest, but we began counting and measuring mushrooms appearing above ground on circular plots of 28m2. With the help of many students, we have counted about 12,000 plots over 24 years, even after being told by one Parks Canada staff member that they could not assist us because “real men do not count mushrooms”. At least we know our position in life.

At any rate the simple question we wanted to ask is whether we can predict mushroom crops one year ahead. We know that many species eat these mushrooms, from red squirrels (who dry mushrooms on spruce tree branches so they can be stored for later consumption), to moose (Alice Kenney has photographed them kneeling down to munch mushrooms), to caribou (Art Rodgers has videoed) to small rodents and insects, not to mention Yukon residents. We know from natural history observations that mushroom crops in the boreal forest are highly variable from year to year, ranging from 0.1 to 110 g/10m2 wet weight, for a CV of 138% (Krebs et al. 2008). The question is how best to predict what the crop will be next year.  Why do we want to know next year’s crop? Two reasons are that large crops provide food for many animals and thus affect overall ecosystem dynamics, and secondly that the essence of understanding in science is the ability to understand why changes occur and if possible to be able to predict them.

We assume it has to be driven by climate, so we can gather together climate data and it is here that the questions arise as to how to proceed. At one extreme we can gather annual temperatures and annual rainfall, and at the other extreme we can gather daily rainfall. We first make the assumption that it is only the weather during the summer from May to August that is relevant for our statistical model, so annual data are not useful. But then we are faced with a nearly infinite number of possible weather variables. We have chosen months as the relevant weather grouping and so we tally May temperature averages, May rainfall totals, growing degree days above 5°C, etc. for all the years involved. This leads us into a statistical nightmare of having far more independent variables than measurements of mushroom crops. If we have, for example, 15 possible measures of temperature and rainfall we can generate 32,768 models ignoring all the interactive models. There are several standard ways of dealing with this statistical dilemma, with stepwise regression being the old fashioned approach. But new methods and advice continue to appear (e.g. Elith et al. 2008, Ives 2015). The ability to compare different regression models with the AIC approach helps (Anderson 2008) as long as there is some biological basis to the models.

We adopted a natural history approach, given that many people believe that large mushroom crops are associated with above average rainfall. We are blessed in the Yukon with only one possible crop of mushrooms per year (at least for the present), so that also simplifies the kinds of models one might use. At any rate (as of 2016) the simplest regression model to predict mushroom biomass in a particular year turned out to involve only rainfall from May (early spring) of the previous year, with R2 = 0.55. But this success has just led us into more questions of why we cannot find a model that will explain the remaining 45% of the variance in annual crops. Should one just give up at this point and be happy that we can explain a large part of the annual variation, or should one press on doing more modelling and looking for other variables? Data dredging is more and more becoming an issue in the ecological literature, and in particular in ecological events likely to be at least partly associated with climate (Norman 2014).

Another ecological problem has been that we do not identify the species of mushrooms involved and deal only in biomass. It may be that species identification would help us to improve predictability. But there are perhaps 40 or more species of mushrooms in our part of the boreal forest, and so we now have to become mycologists. And then as Jim Trappe would tell me, all of this ignores the important questions of what is going on with these fungi underground, so we have only scratched the surface.

The next question is how long a predictive model based on weather will continue to hold in an area subject to rapid climate change. Climate change in the southern Yukon is relatively rapid but highly variable from year to year, and only continuing monitoring will keep us informed about how the physical measurements of temperature and rainfall translate into events in the biological world.

All of this is to say that counting and measuring mushrooms is enjoyable and keeps one connected to the real world. It is also a free type of good exercise, and part of citizen science. Continued monitoring is necessary to see how the boreal ecosystem responds to changing climate and to see if good years for mushroom crops become more frequent. And in good years, many kinds of mushrooms are good to eat if you can beat the squirrels to them.

Anderson, D.R. (2008) Model Based Inference in the Life Sciences: A Primer on Evidence. Springer, New York. ISBN: 978-0-387-74073-7

Elith, J., Leathwick, J.R. & Hastie, T. (2008) A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802-813. doi: 10.1111/j.1365-2656.2008.01390.x

Ives, A.R. (2015) For testing the significance of regression coefficients, go ahead and log-transform count data. Methods in Ecology and Evolution, 6, 828-835. doi: 10.1111/2041-210X.12386

Krebs, C.J., Carrier, P., Boutin, S., Boonstra, R. & Hofer, E.J. (2008) Mushroom crops in relation to weather in the southwestern Yukon. Botany, 86, 1497-1502. doi: 10.1139/B08-094

Norman, G.G. (2014) Data dredging, salami-slicing, and other successful strategies to ensure rejection: twelve tips on how to not get your paper published. Advances in Health Sciences Education, 19, 1-5. doi: 10.1007/s10459-014-9494-8

What Can Ecologists Do?

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For about 40 years many ecologists as well as other scientists have reported on the consequences of climate change. In recent years there has been more and more public awareness of the problems associated with changing climate. But there it all seems to stop. Jobs and dollars trump everything in the western world. I sit today listening to the Federal Government in Canada approving a very large export agreement for liquefied natural gas (LNG) on the central west coast of British Columbia. The gas will be largely obtained by fracking and in spite of the fact that the shipping point is near the mouth of one of the largest salmon rivers on the west coast, and requires a long pipeline to deliver the gas with all its problems, the report of the government states that this development will have no harmful effects on the environment. The perception that burning natural gas is somehow good for the environment boggles my mind. You have heard all of this kind of discussion many times before I am sure.

Yet as far as we can tell these are not evil people who are approving these developments but their decisions are so far away from scientific reality that one can only wonder what drives this current economic system. There are several competing hypotheses. (1) Climate change is not a problem and is not caused by human actions releasing greenhouse gases. This is not believable if scientific evidence is given any credibility. So we need a better excuse for our current myopia. (2) The problems of climate change are so uncertain and far into the distant future so that it is not our job to be concerned about action now. (3) We should take action now but if we do it will disrupt the global economy too much to contemplate. Taxes will have to increase. (4) Much money can be made by these enterprises and this will allow western countries to develop technologies that will remove carbon from the atmosphere, so all will be well in the future. (5) A price can be set on carbon so that business as usual under a carbon price will take care of the problem. The market will take care of us.

Take your pick on these last 4 excuses, but as an ecologist I cannot buy any of them. Clearly I am not a social scientist or an economist, and consequently have little understanding of how all of this proceeds and how the continued nonsense of business as usual is reported on much of the media as though this is the only way forward. The disconnect between what the educated public believes and what the government and business economists push has never been more serious. Perhaps the dominant view of many people is that we have always managed to muddle through in the past, and so this is a minor issue that we will overcome as usual by some kind of technological fix. And it is a long term problem, and I will not be here in the long term.

What can we ecologists do? Teach, report, communicate to the wider public via social media or traditional media, and hope that progress in understanding will finally take hold. Set an example, and hope that we can turn this juggernaut around. David Suzuki and Bill McKibben and many others are doing this. As an army dedicated to peace we can move forward and hope for wisdom to prevail.

Ehrlich, P.R., and Ehrlich, A.H. 2013. Can a collapse of global civilization be avoided? Proceedings of the Royal Society B: Biological Sciences 280(1754): 20122845. doi: 10.1098/rspb.2012.2845.

Ehrlich, P.R., and Ehrlich, A.H. 2013. Future collapse: how optimistic should we be? Proceedings of the Royal Society B: Biological Sciences 280(1767): 20131373. doi: 10.1098/rspb.2013.1373.

Kelly, M.J. 2013. Why a collapse of global civilization will be avoided: a comment on Ehrlich & Ehrlich. Proceedings of the Royal Society B: Biological Sciences 280(1767). doi: 10.1098/rspb.2013.1193.

McKibben, B. 2013. Oil and Honey: The Education of an Unlikely Activist. Henry Holt and Company, New York. 257 pp.  ISBN: 978-08050-9284-4