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Ecology as a Contingent Science

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The Northern Hemisphere is working through a summer of very warm weather, often temperatures 10ºC above ‘normal’. Climate change should in these conditions be obvious to all. Yet despite these clear changes, all the governments of developed countries – including Canada, USA, Australia, Britain – are doing next to nothing about the causes of climate change. This bald statement will lead to a lot of noise about “all we are now doing…”, a carbon tax promoted loudly but that is so low it can have little effect on emissions, and endless talk in the media about “sustainable practices” that are far from sustainable. Why should this be? There are many reasons and I want to discuss just one that pertains to the science of ecology.

Imagine that you are a physicist or chemist and are studying a physical or chemical problem in a lab in Germany and one in Canada. You would expect to get exactly the same experimental results in the two labs. The laws of chemistry and physics are universal and there would be consternation if results differed by geographical locations. Now transform this thought experiment to ecology. You might expect the converse for ecological experiments in the field, and there is much discussion of why this occurs (Brudvig et al. 2017, Marino et al. 2018, Zhou and Ning 2017). We need to think more about why this should be.

First, we might suspect that the ecological conditions are variable by place. The soils of Germany or France or New York or Vietnam differ in composition. The flora and fauna vary dramatically by site even within the same country. The impacts of human activities such as agriculture on the landscape vary by area. Climates are regional as well as local. Dispersal of seeds is not a uniform process. All these things ecologists know a great deal about, and they provide a rich source of post-hoc explanations for any differences. But the flip side is that ecology does not then produce general laws or principles except very general ones that provide guidance but not predictive models useful for management.

This thought leads me back to the general feeling that ecology is not categorized as a hard science and is thus often ignored. Ecologist have been pointing out many of the consequences of climate change for at least 30-40 years with few people in business or local political power listening. This could simply be a consequence of the public caring about the present but not about the future of the Earth. But it might be partly the result of ecology having produced no generality that the public appreciates, except for the most general ecological ‘law’ that “Mother Nature takes care of itself”, so we the public have little to be concerned about.

The paradigm of stability is deeply embedded in most people (Martin et al. 2016), and we are in the process of inventing a non-equilibrium ‘theory’ of ecology in which the outcome of ecological processes leads us into new communities and ecosystems we can only scarcely imagine and certainly not predict clearly. Physicists can predict generally what a future Earth climate with +2ºC or + 4ºC will entail (IPCC 2013, Lean 2018), but we cannot do this so readily with our ecological knowledge.

Where does this get us? Ecology is not appreciated as a science, and thus in the broad sense not funded properly. Ecologists fight over crumbs of funding even to monitor the changes that are occurring, and schemes that might alleviate some of the major effects of climate change are not tested because they are expensive and long-term. Ecology is a long-term science in a world that is increasingly short-term in thinking and in action. Perhaps this will change but no politician wants to wait 10-20 years to see if some experimental procedure works. Funding that is visionary is stopped after 4 years by politicians who know nothing about the problems of the Earth and sustainability. We should demand a politics of sustainability for our future and that of following generations. Thinking long-term should be a requirement not an option.

Brudvig, L.A., Barak, R.S., Bauer, J.T., Caughlin, T.T., and Laughlin, D.C. (2017). Interpreting variation to advance predictive restoration science. Journal of Applied Ecology 54, 1018-1027. doi: 10.1111/1365-2664.12938.

Chapman, M., LaValle, A., Furey, G., and Chan, K.M.A. (2017). Sustainability beyond city limits: can “greener” beef lighten a city’s Ecological Footprint? Sustainability Science 12, 597-610. doi: 10.1007/s11625-017-0423-7.

IPCC (2013) ‘IPCC Fifth Assessment Report: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.’ (Cambridge University Press: Cambridge, U.K.) http://www.climatechange2013.org/images/report/WG1AR5_ALL_FINAL.pdf

Lean, J.L. (2018). Observation-based detection and attribution of 21st century climate change. Wiley Interdisciplinary Reviews. Climate Change 9, e511. doi: 10.1002/wcc.511.

Marino, N.A.C., Romero, G.Q., and Farjalla, V.F. 2018. Geographical and experimental contexts modulate the effect of warming on top-down control: a meta-analysis. Ecology Letters 21, 455-466. doi: 10.1111/ele.12913.

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, 6105-6112. doi: 10.1073/pnas.1525003113.

Zhou, J. and Ning, D. (2017). Stochastic community assembly: Does it matter in microbial ecology? Microbiology and Molecular Biology Reviews 81, e00002-00017. doi: 10.1128/MMBR.00002-17.

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.

 

Fire and Fury and the Environment

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The media at present is full of comments about having a war that will stimulate the economy, at least in reconstruction. And this concern over war and the costs of war prompted me to investigate the relative costs of military funding and environmental funding. So here is a very coarse look at the relative positions of military funding and environmental funding in a few western countries. All the numbers are approximate and refer to 2016 and possibly 2017 budgets, and all are in billions of dollars.

Military expenditures by countries are easiest to obtain, and here are a few for the most recent years I could find:

United States:         $ 611 billion
China:                       $ 216
Russia:                      $ 69
Saudi Arabia:           $ 64
Australia:                  $ 24
Canada:                    $ 15.5

Environmental funding is much more difficult to decompose because different countries amalgamate different agencies into one Department. Consequently, comparisons are best made within one country rather than between countries. Here are a few details for particular agencies:

USA            Department of the Interior     $ 13.4            1 military year = 46 Dept. years
NOAA                                                             $ 5.77             1 military year = 106 NOAA years

Canada      Environment Canada              $ 0.987            1 military year = 16 EC years

Australia     CSIRO                                       $ 0.803            1 military year = 30 CSIRO years

Clearly there are many problems with these simple comparisons. NOAA for example includes agencies covering Marine Fisheries, Weather Service, Environmental Satellites, Aviation Operations, and Oceanic Research among other responsibilities. CSIRO includes divisions dealing with agriculture, climate change, and mining research. I am sure that someone has done a more detailed analysis of these comparisons, but the general message is very clear: the environment is a low priority among western nations, and if you want a rough number one might say the military is about 30 times more “important” than the environment when it comes to funding. If you look for example at the Australian budget for 2017 (http://budget.gov.au/2017-18/content/glossies/overview/download/Budget2017-18-Overview.pdf ) and search for the word ‘environment’ as in the real biophysical environment, you will find not a single case of this word appearing. It is as though the biophysical environment does not exist as a problem in 2017.

I am not clear if anyone worries about these simple facts. The general problem is that federal government budgets are made so complex and presented so poorly that it is nearly impossible to separate out different equivalent expenditures. Thus for example the military argues that it does scientific research with part of its funding, and universities fail to point out that some of their basic research focuses on military questions rather than questions that might benefit humanity (Smart 2016).

I hope that others might look into these expenditures in more detail, and that in the long run we might be more aware of where our tax dollars go. The simple suggestion that the last page of our tax file should give us a choice of what general areas we would like to support with our taxes would be a start. On the last list I saw of 25 ‘items of interest’ to taxpayers who might like more information, the words ‘environment’, ‘conservation’, or ‘sustainability’ never appeared. We should demand this be changed.
Smart, B. (2016). Military-industrial complexities, university research and neoliberal economy. Journal of Sociology 52, 455-481. doi: 10.1177/1440783316654258

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

University Conundrums

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Universities in Canada and the United States and probably in Australia as well are bedeviled by not knowing what they should be doing. In general, they all want to be ‘excellent’ but this is largely an advertising gimmick unless one wishes to be more specific about excellent in what? Excellent in French literature? Probably not. Excellent in the engineering that facilitates the military-industrial complex? Probably yes, but with little thought of the consequences for universities or for Planet Earth (Smart 2016). Excellence in medicine? Certainly, yes. But much of the advertisement about excellence is self aggrandisement, and one can only hope that underneath the adverts there is some good planning and thinking of what a university should be (Lanahan et al. 2016).

There are serious problems in the world today and the question is what should the universities be doing about these long-term, difficult problems. There are two polar views on this question. At one extreme, universities can say it is our mandate to educate students and not our mandate to solve environmental or social problems. At the other extreme, universities can devote their resources to solving problems, and thereby educate students in problem analysis and problem solving. But these universities will not be very popular since for any serious issue like climate change, many voters are at odds over what can and should be done, Governments do not like universities that produce scholarship that challenges their policies. So we must always remember the golden rule – “she that has the gold, makes the rules”.

But there are constraints no matter what policies a university adopts, and there is an extensive literature on these constraints. I want to focus on one overarching constraint for biodiversity research in universities – graduate students have a very short time to complete their degrees. Given a 2-year or 3-year time horizon, the students must focus on a short-term issue with a very narrow focus. This is good for the students and cannot be changed. But it is potentially lethal for ecological studies that are long-term and do not fit into the demands of thesis writing. A basic assumption I make is that the most important ecological issues of our day are long-term problems, at least in the 20-year time frame and more likely in the 50 to 100-year time frame. The solution most prevalent in the ecology literature now is to use short term data to produce a model to extrapolate short term data into the indefinite future by use of a climate model or any other model that will allow extrapolation. The result of this conundrum is that the literature is full of studies making claims about ecological processes that are based on completely inadequate time frames (Morrison 2012). If this is correct, at least we ought to have the humility to point out the potential errors of extrapolation into the future. We make a joke about this situation in our comical advice to graduate students: “If you get an exciting result from your thesis research in year 1, stop and do no more work and write your thesis lest you get a different result if you continue in year 2.”

The best solution for graduate students is to work within a long-term project, so that your 2-3 years of work can build on past progress. But long-term projects are difficult to carry forward in universities now because research money is in short supply (Rivero and Villasante 2016). University faculty can piggy-back on to government studies that are well funded and long-term, but again this is not always possible. Conservation ecology is not often well funded by governments either, so we keep passing the buck. Collaboration here between governments and universities is essential, but is not always strong at the level of individual projects. Some long-term ecological studies are led by federal and regional government research departments directly, but more seem to be led by university faculty. And the limiting resource is typically money. There are a set of long-term problems in ecology that are ignored by governments for ideological reasons. Some politicians work hard to avoid the many ecological problems that are ‘hot potatoes’ and are best left unstudied. Any competent ecologist can list for you 5 or more long-term issues in conservation biology that are not being addressed now for lack of money. I doubt that ideas are the limiting resource in ecology, as compared with funding.

And this leads us back in a circle to the universities quest for ‘excellence’. Much here depends on the wisdom of the university’s leaders and the controls on university funding provided by governments for research. In Canada for example, funding constraints for research excellence exist based on university size (Murray et al. 2016). How then can we link the universities’ quest for excellence to the provision of adequate funding for long-term ecological issues? As one recommendation to the directors of funding programs within the universities, I suggest listing the major problems of your area and of the world at large, and then fund the research within your jurisdiction by how well the proposed research matches the major problems we face today.

Lanahan, L., Graddy-Reed, A. & Feldman, M.P. (2016) The Domino Effects of Federal Research Funding. PLoS ONE, 11, e0157325. doi: 10.1371/journal.pone.0157325

Morrison, M.L. (2012) The habitat sampling and analysis paradigm has limited value in animal conservation: A prequel. Journal of Wildlife Management, 76, 438-450. doi: 10.1002/jwmg.333

Murray, D.L., Morris, D., Lavoie, C., Leavitt, P.R. & MacIsaac, H. (2016) Bias in research grant evaluation has dire consequences for small universities. PLoS ONE, 11, e0155876.doi: 10.1371/journal.pone.0155876

Rivero, S. & Villasante, S. (2016) What are the research priorities for marine ecosystem services? Marine policy, 66, 104-113. doi: 10.1016/j.marpol.2016.01.020

Smart, B. (2016) Military-industrial complexities, university research and neoliberal economy. Journal of Sociology, 52, 455-481. doi: 10.1177/1440783316654258

Biodiversity Conundrums

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Conservation ecologists face a conundrum, as many have pointed out before. As scientists we do not make policy. Most conservation problems are essentially a moral issue of dealing with conflicts in goals and allowable actions. Both the United States and Canada have endangered species legislation in which action plans are written for species of concern. In the USA species of concern are allotted some funding and more legal protection than in Canada, where much good material is written but funding for action or research is typically absent. What is interesting from an ecological perspective is the list of species that are designated as endangered or threatened. Most of them can be described colloquially as the “charismatic megafauna”, species that are either large or beautiful or both. There are exceptions of course for some amphibians and rare plants, but by and large the list of species of concern is a completely non-random collection of organisms that people see in their environment. Birds and butterflies and large mammals are at the head of the list.

All of this is fine and useful because it is largely political ecology, but it raises the question of what will happen should these rescue plans for threatened or endangered species fail. This question lands ecologists in a rather murky area of ecosystem function, which leads to the key question: how is ecosystem function affected by the loss of species X? The answer to this question depends very much on how you define ecosystem function. If species X is a plant and the ecosystem function measured is the uptake of CO2 by the plant community, the answer could be a loss of function, no change, or indeed an increase in CO2 uptake if species X for example is replaced by a weed that is more productive that species X. The answer to this simple question is thus very complicated and requires much research. For a hypothetical example, plant X may be replaced by a weed that fixes more CO2, and thus ecosystem function is improved as measured by carbon uptake from the atmosphere. But the weed may deplete soil nitrogen which could adversely affect other plants and soil quality. Again more data are needed to decide this. If the effect size is small, much research could provide an ambiguous answer to the original question, since all measurement involves errors.

So now we are in a box, a biodiversity conundrum. The simplest escape is to say that all species loss is undesirable in any ecosystem, a pontification that is more political than scientific. And, for a contrary view, if the species lost is a disease organism, or an insect that spreads human diseases, we will not mourn its passing. In practice we seem to agree with the public that the species under concern are not all of equal value for conservation. The most serious outcome of this consideration is that where the money goes for conservation is highly idiosyncratic. There are two major calls for funding that perhaps should not be questioned: first, for land (and water) acquisition and protection, and second, for providing compensation for the people whose livelihoods are affected by protected areas with jobs and skills that improve their lives. The remaining funds need to be used for scientific research that will further the cause of conservation in the broad sense. The most useful principle at this stage is that all research has a clear objective and a clear list of what outcomes can be used to judge its success. For conservation outcomes this judgement should be clear cut. Currently they are not.

When Caughley (1994) described the declining population paradigm and the small population paradigm he clearly felt that the small population paradigm, while theoretically interesting, had little to contribute to most of the real world problems of biodiversity conservation. He could not have imagined at the time how genetics would develop into a powerful set of methods of analysis of genomes. But with a few exceptions the small population paradigm and all the elegant genetic work that has sprung from it has delivered a mountain of descriptive information with only a molehill of useful management options for real world problems. Many will disagree with my conclusion, and it is clear that conservation genetics is a major growth industry. That is all well and good but my question remains as to its influence on the solution of current conservation problems (Caro 2008; Hutchings 2015; Mattsson et al. 2008). Conservation genetic papers predicting extinctions in 100 years or more based on low levels of genetic variation are not scientifically testable and rely on a law of conservation genetics that is riddled with exceptions (Nathan et al. 2015; Robinson et al. 2016). Do we need more untestable hypotheses in conservation biology?

Caro, T. 2008. Decline of large mammals in the Katavi-Rukwa ecosystem of western Tanzania. African Zoology 43(1): 99-116. doi:10.3377/1562-7020(2008)43[99:dolmit]2.0.co;2.

Caughley, G. 1994. Directions in conservation biology. Journal of Animal Ecology 63: 215-244. doi: 10.2307/5542

Hutchings, J.A. 2015. Thresholds for impaired species recovery. Proceedings of the Royal Society. B, Biological sciences 282(1809): 20150654. doi:10.1098/rspb.2015.0654.

Mattsson, B.J., Mordecai, R.S., Conroy, M.J., Peterson, J.T., Cooper, R.J., and Christensen, H. 2008. Evaluating the small population paradigm for rare large-bodied woodpeckers, with Implications for the Ivory-billed Woodpecker. Avian Conservation and Ecology 3(2): 5. http://www.ace-eco.org/vol3/iss2/art5/

Nathan, H.W., Clout, M.N., MacKay, J.W.B., Murphy, E.C., and Russell, J.C. 2015. Experimental island invasion of house mice. Population Ecology 57(2): 363-371. doi:10.1007/s10144-015-0477-2.

Robinson, J.A., Ortega-Del Vecchyo, D., Fan, Z., Kim, B.Y., and vonHoldt, B.M. 2016. Genomic flatlining in the endangered Island Fox. Current Biology 26(9): 1183-1189. doi:10.1016/j.cub.2016.02.062.

On Disease Ecology

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One of the sleepers in population dynamics has always been the role of disease in population limitation and population fluctuations. Part of the reason for this is that disease studies need cooperation between skilled ecologists and skilled microbiologists. Another problem is the possibility of infinite regress in looking for disease agents as a cause of population change in natural populations – e.g. if it is not virus X, there are hundreds of other viruses that might be the culprit. In both North America and Europe one focus of concern has been on the hantavirus group (Luis et al. 2010; Mills et al. 2010, Davis et al. 2005, Mills et al. 1999). Hantaviruses come in many different forms and are typically carried by rodent species. Some varieties produce hemorrhagic fever with renal syndrome in Europe, Asia and Africa, but in the Americas the main disease of concern is HPS (hantavirus pulmonary syndrome). It is no surprise that often emerging diseases are studied only because some humans die from them. As of 2016, 690 cases of hantavirus pulmonary syndrome have been recorded in the USA, and 36% of these cases resulted in death. The reverse question of what the disease is doing to the animal population gets rather less attention typically than the human disease problem. The example I want to discuss here is the Sin Nombre virus (SNV) in deer mice (Peromyscus spp.), widespread rodents in North America.

The hantavirus outbreak in the Southwestern USA in the 1990s caused numerous human deaths and produced a number of field studies that showed a patchy pattern of infection among deer mice in Arizona and Colorado (Mills et al. 1999). Male mice were infected more than females and the suggestion was that males fighting for territories were infecting one another directly when population densities were high. The call for long-term studies went out and several studies from 3-5 years were carried out in the late 1990s until the problem of infection in the human population became less of an issue compared with other diseases such as Ebola in other parts of the world. The shift in concern resulted in reduced funding for field studies in North America.

In 1994 Rick Douglass and his research team began long term studies on the Sin Nombre virus in deer mice using 18 live trapping areas of 1 ha each spread across Montana and placed in a variety of habitats (Douglass et al. 2001). Long-term for their study was 15 years, all this at a time when 2-3 year studies were thought to be sufficient to unravel the nexus of infection and transmission. The idea was to complement in Montana similar rodent research in Arizona, New Mexico, and Colorado. The results are fascinating and important because they illustrate the importance of long term research and the understanding of what a well designed field study can produce.

Rightfully many of the hantavirus studies were focused on the human connection, but what I want to emphasize here is the impact of this virus on the rodent populations. Luis et al. (2012) estimated that male Peromyscus had their monthly survival rate reduced from 0.67 to 0.58 if they were seropositive, a 13% reduction, but females showed no effect of hantavirus on survival so that infected and uninfected females survived equally well. Hantavirus does reduce body growth rates of infected male mice. One consequence of these findings should be that the growth rate of Peromyscus populations in Montana should be only slightly affected by hantavirus infections, since it is the female component of the population that drives numbers. There are limitations to these conclusions since juveniles too young to live trap could suffer mortality that at present cannot be measured. The threshold for hantavirus transmission in these Peromyscus populations was about 17 individuals per ha (Luis et al. 2015), implying that hantavirus would disappear in populations smaller than this because it would not transmit. The consequence for us is that human hantavirus infections in North America are much more likely when deer mouse populations are high, and by monitoring deer mice ecologists can broadcast warnings when there are increased possibilities of infection with this lethal disease.

The details about the Sin Nombre hantavirus in North America are well covered in these and other papers. The most important general message from this research has been the need for long term studies to get at what might initially seem to be a simple population problem (Carver et al. 2015). There are a host of other viruses that infect rodent species and many other mammals and birds about which we know very little. The path to understanding the effects of these viruses on the animals they infect and their potential for human transmission will require much detailed work over a longer time period than what is now the funding horizon of our granting agencies. The Montana studies on the Sin Nombre virus required ecologists to trap for 20 years with more than 851,000 trap nights to catch 16,608 deer mice, and collect 10,572 blood samples to assess infections and gain an understanding of this virus disease. The problem too often is that it is easy to find ecologists and virologists keen to cooperate in these studies of disease, but it is not easy to find the long term funding that looks at these ecological problems in the time scale of 10-20 years or more. We need much more long term thinking about ecological problems and the funding to support team efforts on difficult problems that are not soluble in a 3-year time frame.

Carver, S., Mills, J.N., Parmenter, C.A., Parmenter, R.R., Richardson, K.S., Harris, R.L., Douglass, R.J., Kuenzi, A.J., and Luis, A.D. 2015. Toward a mechanistic understanding of environmentally forced zoonotic disease emergence: Sin Nombre hantavirus. BioScience 65(7): 651-666. doi: 10.1093/biosci/biv047.

Davis, S., Calvet, E., and Leirs, H. 2005. Fluctuating rodent populations and risk to humans from rodent-borne zoonoses. Vector-Borne and Zoonotic Diseases 5(4): 305-314.

Douglass, R.J., Wilson, T., Semmens, W.J., Zanto, S.N., Bond, C.W., Van Horn, R.C., and Mills, J.N. 2001. Longitudinal studies of Sin Nombre virus in deer mouse-dominated ecosystems of Montana. American Journal of Tropical Medicine and Hygiene 65(1): 33-41.

Luis, A.D., Douglass, R.J., Hudson, P.J., Mills, J.N., and Bjørnstad, O.N. 2012. Sin Nombre hantavirus decreases survival of male deer mice. Oecologia 169(2): 431-439. doi: 10.1007/s00442-011-2219-2.

Luis, A.D., Douglass, R.J., Mills, J.N., and Bjørnstad, O.N. 2010. The effect of seasonality, density and climate on the population dynamics of Montana deer mice, important reservoir hosts for Sin Nombre hantavirus. Journal of Animal Ecology 79(2): 462-470. doi: 10.1111/j.1365-2656.2009.01646.x.

Luis, A.D., Douglass, R.J., Mills, J.N., and Bjørnstad, O.N. 2015. Environmental fluctuations lead to predictability in Sin Nombre hantavirus outbreaks. Ecology 96(6): 1691-1701. doi: 10.1890/14-1910.1.

Mills, J.N., Amman, B.R., and Glass, G.E. 2010. Ecology of hantaviruses and their hosts in North America. Vector-Borne and Zoonotic Diseases 10(6): 563-574. doi: 10.1089/vbz.2009.0018.

Mills, J.N., Ksiazek, T.G., Peters, C.J., and Childs, J.E. 1999. Long-term studies of hantavirus reservoir populations in the southwestern United States: a synthesis. Emerging Infectious Diseases 5(1): 135-142.

On Gravity Waves and the 1%

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The news this week has been all about the discovery of gravity waves and the great triumphs of modern physics to understand the origin of the universe. There is rather less news on the critical ecological problems of the Earth – of agricultural sustainability, biodiversity collapse, pollution, climate change – not to mention the long recognized economic problems of poverty and inequality, globally and within our own countries. All of these issues converge to the questions of resource allocations by our governments that have failed to assess priorities on many fronts. Many see this but have little power to change the system that is continually moving to save and improve the fortunes of the 1% to the detriment of most people.

In scientific funding there has always been a large bias in favor of the physical sciences, as I have commented on previously, and the question is how this might be publicized to produce  a better world. I suggest a few rules for scientific funding decisions both by governments and by private investors.

Rule 1: For maximizing scientific utility for the biosphere including humans, we require a mix of basic and applied science in every field. Whether this mix should be 50:50, 30:70, or 70:30 should be an item for extended discussion with the implicit assumption that it could differ in different areas of science.

Rule 2: Each major area of science should articulate its most important issues that must be addressed in the short term and the long term (>50 years). For biodiversity, as an example, the most important short term problem is to minimize extinctions while the most important long term problem might be to maintain genetic variability in populations.

Rule 3: The next step is most critical and perhaps most controversial: What are the consequences for the Earth and its human population if the most important issue in any particular science is not solved or achieved? If the required experiments or observations can be delayed for 30 (or 50) years, what will it matter?

If we could begin to lay out this agenda for science, we could start a process of ranking the importance of each of the sciences for funding in the present and in the long term. At the present time this ranking process is partly historical and partly based on extreme promises of future scenarios or products that are of dubious validity. There is no need to assume that all will agree, and I am sure that several steps would have to be designated to involve not only young and older scientists but also members of the business community and the public at large.

If this agenda works, I doubt that we would spend quite so much money on nuclear physics and astronomy and we might spend more money on ocean science, carbon budgets, and sustainable agricultural research. This agenda would mean that powerful people could not push their point of view in science funding quite so freely without being asked for justification. And perhaps when budgets are tight for governments and businesses, the first people on the firing line for redundancy will not be environmental scientists trying their best to maintain the health of the Earth for future generations.

So I end with 2 simple questions: Could gravity waves have waited another 100 years for discovery? What is there that cannot wait?

(Finally, an apology. I failed to notice that on a number of recent blogs the LEAVE A REPLY option was not available to the reader. This was inadvertent and somehow got deleted with a new version of the software. I should have noticed it and it is now corrected on all blogs.)

On Philanthropic Investment in Conservation – Part 2

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Here is an optimistic thought for the day. After writing my previous blog on philanthropy and conservation, it occurred to me that a single scenario might focus the mind of ecologists and conservation biologists as we think about relevant research:

Suppose you are sitting in your office and someone comes in and tells you that they wish to donate one billion dollars to your research in ecology. What would you tell them you would like to do?

This is of course ridiculous but let us be optimistic and think it may happen. There are a lot of very rich people around the world and they will have to do something with all their money. Some of it will be wasted but some could do much good for the development of strong science. So let us pretend for the moment that this will happen sometime in the future.

We need to think clearly what this money entails. First, if we want to live off the interest and we expect 5% return on investment, we end up with $ 50 million to spend per year. What are we going to do with all this money? The two options would seem to be to buy land and maintain it for conservation, or to set up a foundation for conservation that would support graduate student and postdoctoral fellows. Let us check these options out with a broad brush.

The first option is based on the belief that habitat loss is the key process driving biodiversity decline so we should use part of the money for land purchase or marine rights to areas. But we note that land purchases are not very useful if the land is not managed and protected so that some group of people need to be in charge. So suppose we spend half immediately on land acquisition, and land costs are $100 per ha, we could purchase about 50,000 km2, an area approximately the size of Denmark, slightly smaller than Scotland, and about the size of West Virginia. Then we can employ about 250 people full time to do research or manage the protected landscape at an overall cost of $100 K per scientist including salary and operating research costs. This is an attractive option and the decision that would need to be made is what areas are most important to purchase for conservation in what part of the world.

The second option is to establish a permanent foundation for conservation that would be devoted to supporting graduates and postdoctoral fellows worldwide. I am not clear on the costs for a foundation to operate but let us assume $ 2 million a year for staff and operating costs. This would leave $ 48 million for operating costs, supporting 480 students or postdocs at $100,000 each per year or 320 students if you wished to give each an average $150,000 per year for research and salary. If these were spread out over the 196 countries on Earth, clearly there would be about 2 scientists per country. If we spread them out evenly over the 148 million km2 of land area over the whole Earth, we would require each student or scientist to be in charge of about 300,000 km2, an area about the size of Norway, or Poland or the Philippines. Clearly one would not operate in such a fashion, and would concentrate person-power in the areas of greatest need.

There is considerable literature discussing the issue of how philanthropy can augment conservation in the most effective manner, and a few papers are given here that further the discussion.

Where does this theoretical exercise leave us? Clearly there would be many other ways to utilize these hypothetical funds for conservation, but the point that shows clearly is that the funds needed to achieve conservation on a global scale are very large, and even a billion dollars disappears very quickly if we are attempting to achieve solid conservation outcomes. The costs of conservation are large and there is the need to recognize that government funding is critical, so that an additional billion dollars from a philanthropist will only add icing on the cake and not the whole cake.

Not that anyone I know would turn down a billion-dollar donation as too little.

Adams, W., and J. Hutton. 2007. People, parks and poverty: Political ecology and biodiversity conservation. Conservation and Society 5:147-183.

Diallo, R. 2015. Conservation philanthropy and the shadow of state power in Gorongosa National Park, Mozambique. Conservation and Society 13:119-128. doi: 10.4103/0972-4923.164188

Ferraro, P. J., and S. K. Pattanayak. 2006. Money for Nothing? A call for empirical evaluation of biodiversity conservation investments. PLoS Biology 4:e105.
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Jones, C. 2012. Ecophilanthropy, neoliberal conservation, and the transformation of Chilean Patagonia’s Chacabuco Valley. Oceania 82:250-263.
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