Tag Archives: boreal forest ecology

The Problem of Time in Ecology

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There is a problem in doing ecological studies that is too little discussed – what is the time frame of a good study? The normal response would be that the time frame varies with each study so that no guidelines can be provided. There is increasing recognition that more long-term studies are needed in ecology (e.g. Hughes et al. 2017) but the guidelines remain unclear.

The first issue is usually to specify a time frame, e.g. 5 years, 10 years. But this puts the cart before the horse, as the first step ought to be to define the hypothesis being investigated. In practice hypotheses in many ecological papers are poorly presented and there should not be one hypothesis but a series of alternative hypotheses. Given that, the question of time can be given with more insight. How many replicated time periods do you need to measure the ecological variables in the study? If your time scale unit is one year, 2 or 3 years is not enough to come to any except very tentative conclusions. We have instantly fallen into a central dilemma of ecology – studies are typically planned and financed on a 3–5-year time scale, the scale of university degrees.

Now we come up against the fact of climate change and the dilemma of trying to understand a changing system when almost all field work assumes an unchanging environment. Taken to some extreme we might argue that what happens in this decade tells us little about what will happen in the next decade. The way around this problem is to design experiments to test the variables that are changing ahead of time, e.g., what a 5⁰C temperature increase will do to the survival of your corals. To follow this approach, which is the classic experimental approach of science, we must assume we know the major variables affecting our population or community changes. At present we do not know the answer to this question, and we rely on correlations of a few variables as predictors of how large a change to expect.

There is no way out of this empirical box, which defines clearly how physics and chemistry differ from ecology and medicine. There are already many large-scale illustrations of this problem. Forest companies cut down old-growth timber on the assumption that they can get the forest back by replanting seedlings in the harvested area. But what species of tree seedlings should we replant if we are concerned that reforestation often operates on a 100–500-year time scale? And in most cases, there is no consideration of the total disruption of the ecosystem, and we ignore all the non-harvestable biodiversity. Much research is now available on reforestation and the ecological problems it produces. Hole-nesting birds can be threatened if old trees with holes are removed for forestry or agricultural clearing (Saunders et al. 2023). Replanting trees after fire in British Columbia did not increase carbon storage over 55 years of recovery when compared with unplanted sites (Clason et al. 2022). Consequently, in some forest ecosystems tree planting may not be useful if carbon storage is the desired goal.

At the least we should have more long-term monitoring of the survival of replanted forest tree seedlings so that the economics of planting could be evaluated. Short-term Australian studies in replanted agricultural fields showed over 4 years differences in survival of different plant species (Jellinek et al. 2020). For an on-the-ground point of view story about tree planting in British Columbia see:
https://thetyee.ca/Opinion/2023/11/02/Dont-Thank-Me-Being-Tree-Planter/. But we need longer-term studies on control and replanted sites to be more certain of effective restoration management. Gibson et al. (2022) highlighted the fact that citizen science over a 20-year study could make a major contribution to measuring the effectiveness of replanting. Money is always in short supply in field ecology and citizen science is one way of achieving goals without too much cost. 

Forest restoration is only one example of applied ecology in which long-term studies are too infrequent. The scale of restoration of temperate and boreal ecosystems is around 100 years, and this points to one of the main failures of long-term studies, that they are difficult to carry on after the retirement of the principal investigators who designed the studies.

The Park Grass Experiment begun in 1856 on 2.8 ha of grassland in England is the oldest ecological experiment in existence (Silvertown et al. 2006). As such it is worth a careful evaluation for the questions it asked and did not ask, for the scale of the experiment, and for the experimental design. It raises the question of generality for all long-term studies and cautions us about the utility and viability of many of the large-scale, long-term studies now in progress or planned for the future.

The message of this discussion is that we should plan for long-term studies for most of our critical ecological problems with clear hypotheses of how to conserve biodiversity and manage our agricultural landscapes and forests. We should move away from 2–3-year thesis projects on isolated issues and concentrate on team efforts that address critical long-term issues with specific hypotheses. Which says in a nutshell that we must develop a vision that goes beyond our past practices in scatter-shot, short-term ecology and at the same time avoid poorly designed long-term studies of the future.

Clason, A.J., Farnell, I. & Lilles, E.B. (2022) Carbon 5–60 Years After Fire: Planting Trees Does Not Compensate for Losses in Dead Wood Stores. Frontiers in Forests and Global Change, 5, 868024. doi: 10.3389/ffgc.2022.868024.

Gibson, M., Maron, M., Taws, N., Simmonds, J.S. & Walsh, J.C. (2022) Use of citizen science datasets to test effects of grazing exclusion and replanting on Australian woodland birds. Restoration Ecology, 30, e13610. doi: 10.1111/rec.13610.

Hughes, B.B.,et al. (2017) Long-term studies contribute disproportionately to ecology and policy. BioScience, 67, 271-281. doi.: 10.1093/biosci/biw185.

Jellinek, S., Harrison, P.A., Tuck, J. & Te, T. (2020) Replanting agricultural landscapes: how well do plants survive after habitat restoration? Restoration Ecology, 28, 1454-1463. doi: 10.1111/rec.13242.

Saunders, D.A., Dawson, R. & Mawson, P.R. (2023) Artificial nesting hollows for the conservation of Carnaby’s cockatoo Calyptorhynchus latirostris: definitely not a case of erect and forget. Pacific Conservation Biology, 29, 119-129. doi: 10.1071/PC21061.

Silvertown, J., Silvertown, J., Poulton, P. & Biss, P.M. (2006) The Park Grass Experiment 1856–2006: its contribution to ecology. Journal of Ecology, 94, 801-814. doi: 10.1111/j.1365-2745.2006.01145.x.

On A Global Agenda for Ecology

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Reading the ecology literature now I am excited by the papers that are filling in small gaps in our understanding of population and community ecology. Good work indeed. But I am concerned more about the big picture – what would we like ecological science to show to the world in 50 years as our achievements? There are two aspects of this question. At present the findings of ecological research are presented in the media mostly as what could be coarsely described as ecological trivia, light entertainment. We must continue to do this as it is an important part of keeping the public aware of environmental issues. The second aspect of our public face is the bigger issue of how we can make the future world a better place. This part is a global agenda for ecology that should be the background focus of all our research. So what should be our global agenda?

We could call it global change. Specifically, how will our ecological systems change as a joint consequence of climate change and human disturbances? So look out the window to any natural landscape where you live and ask how much we now know that will allow you to predict what that scene will be like in a century or so. We should be able to make this prediction more easily with human disturbed landscapes that with those driven by environmental change, but I am not sure everyone would agree with this hypothesis. We will probably know that if we continue to overgraze a grassland, we will end with a weed infested wasteland or even bare soil. Consequently, a rational management agency should be able to prevent this degradation. These kinds of change should be easy to manage yet we as a society continue to degrade ecosystems all over the globe. Is there an general index for degradation for the countries of the world, so we could add it to Greenhouse Gas Emissions, freshwater contamination, overharvesting of fish and timber, and a host of other environmental indicators that are useful to the public?

The consequences of climate change are the most difficult to understand and possibly manage. We have lived in a dream world of a stable environment, and the mathematical gurus focus on stability as a sine qua non. Change in a system that is well understood should be predictable both in the short term of 50 years and in the long term of 500 years. But we are not there yet. We work hard on the pieces – is the bird population of this particular national park going up or down?, how rapidly are peat bogs releasing CO2 under current changing climate? – but these details while important do not allow one to predict whole ecosystem shifts. more rapidly. What do we need to do as ecologists to achieve a broad consensus on global issues?

Sutherland et al. (2013, 2018) have made a heroic attempt both to recognize fundamental ecological questions and to identify emerging issues in a broader societal framework. This helps us to focus on both specific ecological issues as well as emerging global problems. One useful recommendation that could proceed from these reviews would be a specific journal that would review each year a small number of these questions or issues that would serve as a progress bar on increasing understanding of ecological unknowns.

A personal example might focus the problem. My colleagues, students, and I have been working in the Yukon boreal forest at Kluane for 46 years now, trying to understand community dynamics. The ecosystem moves slowly because of the cold climate, so in the short term of 50 years we cannot see there will be much significant change. But this is more of a guess than a solid prediction because a catastrophe – fire, insect attacks – could reset the system on a different pathway. The long term (500 year) trajectory for this ecosystem is much harder to predict, except to say that it will be driven largely by the climate-vegetation axis, and this is the link in ecosystem dynamics that we understand least. We cannot assume stability or equilibrium dynamics in boreal forests, and while paleo-ecologists have given us a good understanding of past changes in similar ecosystems, the past is not necessarily a good guide to future long-term changes. So I think a critic could well say that we have failed our attempt to understand our boreal forest ecosystem and be able to predict its trajectory, even though we have more than 300 papers describing how parts of this system interact.

My concern is that as we make progress with the pieces of the ecology puzzle we more and more lose sight of the final goals, and we are lost in the details of local ecosystems. Does this simply mean that we have an ecological ‘Red Queen’ that we will forever be chasing? Perhaps that is both the fundamental joy and the fundamental frustration of working on changing ecological systems. In the meantime, enjoy slaying the unknowns of local, specific ecosystems and on occasion look back to see how far we have come.

Sutherland, W.J.et al. (2013). Identification of 100 fundamental ecological questions. Journal of Ecology 101(1): 58-67. doi: 10.1111/1365-2745.12025.

Sutherland, W.J.et al. (2018). A 2018 Horizon Scan of Emerging Issues for Global Conservation and Biological Diversity. Trends in Ecology & Evolution 33(1): 47-58. doi: 10.1016/j.tree.2017.11.006.

 

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 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

On Caribou and the Conservation Conundrum

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The central conundrum of conservation is the conflict between industrial development and the protection of biodiversity. And the classic example of this in Canada is the conservation of caribou. Caribou in the millions have ranged over almost all of Canada in the past. They are now retreating in much of the southern part of their range, have nearly gone extinct in the High Arctic, and are extinct on Haida Gwaii (Queen Charlotte Islands). The majority of populations with adequate data are dropping in numbers rapidly. The causes of their demise point to human habitat destruction from forestry, mining, oil and gas developments and roads (Festa-Bianchet et al. 2011). We march on with economic development, and caribou are in the way of progress.

The nexus of interactions underlying this crisis is reasonably well understood for boreal caribou and there is an extensive literature on the topic (Bergerud et al. 2007; Hervieux et al. 2013; Hervieux et al. 2014; Schaefer and Mahoney 2013; Wittmer et al. 2007). Caribou avoid human constructions like pipelines, mines, forestry operations, and roads. Forestry in particular opens up habitat that tends to favor deer and moose. Climate change makes winters less severe for deer. More prey makes more predators, and caribou are typically accidental, secondary prey from wolves that live largely off moose and deer. The habitats that humans open up with roads, seismic lines, and wellheads provide superhighways for wolves and other predators, so that predator access is greatly improved. Such access roads also allow hunters to access ungulates and potentially increase the harvest rate.

If predators are the key immediate factor reducing caribou populations, there seem to be two general solutions. Killing wolves is the most obvious management action, and much of wildlife management in North America has historically been based on the simple paradigm: “killing wolves is the answer, now what is the question?” But two problems arise. There are more predators than wolves (e.g. bears) and secondly killing wolves does not work very well (Hayes 2010). At best it seems to slow down the caribou decline at great expense, and it has to be continuous year after year because killing wolves increases the reproductive rate of those left behind and migration of wolves into the “control” area is rapid. So this management action becomes too expensive in the long run to work well and most people don’t want to see bears killed wholesale either. So the next option is to use fencing to protect caribou from contact with all predators. These fences could be on small areas into which pregnant female caribou are put in the spring to have their calves, and then released when the calves are a few months old and have a better chance of avoiding predators. Or the ultimate fence would be around hundreds of square kilometers to enclose a permanent caribou population with all the predators removed inside the fenced area. This would require continuous maintenance and is very costly. It turns caribou into a zoo animal, albeit on a large scale.

There is one other solution and that is to set aside very large areas of habitat that are not invaded by the forestry, mining, and oil industries, and to monitor the dynamics of caribou in these large reserves. Manitoba is apparently doing this, with reported success in stopping caribou declines.

Beyond these southern populations of caribou in the boreal forest zone, the problems of caribou population trends on the tundra are difficult to unravel, partly because of a lack of data arising from a shortage of funds (Gunn et al. 2011). Climate change is happening and the exact effects on tundra populations is unclear. Many barren-ground caribou herds show fluctuations in abundance with a period of about 50 years. Food supply exhaustion may be one factor in the fluctuations but harvesting is also involved. Local harvest data are often not recorded and with poor population data and poor harvest data we can rarely determine the trajectories of the herds or explain why they are changing in abundance. Peary caribou in the far north are suffering from climate change, rain events in winter that freezes their food supply of lichens under ice so they starve. No one knows how to alleviate the weather, and we only add to the problem with our greenhouse gas emissions. Peary caribou now survive in very low numbers but we cannot be sure that will continue.

All in all, we work hard to conserve large mammal ecosystems in tropical countries but seem far too unconcerned about our Canadian caribou heritage. To inform conservation actions, serious long-term population studies are sorely needed, including more frequent aerial census estimates for all the caribou herds, radio-collaring individuals for demographic data and movements, and complete harvesting data from all sources.

 

Bergerud, A.T., Dalton, W.J., Butler, H., Camps, L., and Ferguson, R. 2007. Woodland caribou persistence and extirpation in relic populations on Lake Superior. Rangifer 27(4): 57-78 (Special Issue No. 17). doi: http://dx.doi.org/10.7557/2.27.4.321

Festa-Bianchet, M., Ray, J.C., Boutin, S., Côté, S.D., and Gunn, A. 2011. Conservation of caribou (Rangifer tarandus) in Canada: an uncertain future. Canadian Journal of Zoology 89(5): 419-434. doi:10.1139/z11-025 .

Gunn, A., Russell, D., and Eamer, J. 2011. Northern caribou population trends in Canada. Canadian Biodiversity: Ecosystem Status and Trends 2010, Technical Thematic Report No. 10. Canadian Councils of Resource Ministers. Ottawa, ON. iv + 71 p. http://www.biodivcanada.ca/default.asp?lang=En&n=137E1147-1

Hayes, B. (2010) Wolves of the Yukon. Wolves of the Yukon Publishing, Smithers, B.C. ISBN: 978-1-4566-1047-0

Hervieux, D., Hebblewhite, M., DeCesare, N.J., Russell, M., Smith, K., Robertson, S., and Boutin, S. 2013. Widespread declines in woodland caribou (Rangifer tarandus caribou) continue in Alberta. Canadian Journal of Zoology 91(12): 872-882. doi:10.1139/cjz-2013-0123.

Hervieux, D., Hebblewhite, M., Stepnisky, D., Bacon, M., and Boutin, S. 2014. Managing wolves (Canis lupus) to recover threatened woodland caribou (Rangifer tarandus caribou) in Alberta. Canadian Journal of Zoology 92(12): 1029-1037. doi:10.1139/cjz-2014-0142 .

Schaefer, J.A., and Mahoney, S.P. 2013. Spatial dynamics of the rise and fall of caribou (Rangifer tarandus) in Newfoundland. Canadian Journal of Zoology 91(11): 767-774. doi:10.1139/cjz-2013-0132 .

Wittmer, H.U., McLennan, B.N., Serrouya, R., and Apps, C.D. 2007. Changes in landscape composition influence the decline of a threatened caribou population. Journal of Animal Ecology 76: 568-579. doi: 10.1111/j.1365-2656.2007.01220.x

The Snowshoe Hare 10-year Cycle – A Cautionary Tale

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We have been working on the ten-year cycle of snowshoe hares (Lepus americanus) in the southwest Yukon since 1975 trying to answer the simple question of what causes these cyclic fluctuations. I think that we now understand the causes of the cyclic dynamics, which is not to say all things are known but the broad picture is complete. But some misunderstanding persists, hence this one page summary. Some biology first.

The snowshoe hare cycle has been known from Canada lynx fur return data for more than 100 years, and of course known to First Nations people much before that. Hares are herbivores of small trees and shrubs, they reproduce at age 1 and rarely live more than 1-2 years. They have 2-4 litters in a summer, with litter size around 4-6. Juvenile losses are high and at best populations increase about three-to-four-fold per year. Almost everything eats them – lynx, coyotes, great-horned owls, goshawks, a long list of predators on the young. Reproduction collapses with rising density and females reduce their output from 4 litters to 2 in the peak and decline phase.

The obvious driving factors when Lloyd Keith and his students began working on the hare cycle in Alberta in the 1960s were winter food shortage and predation. When there is a high hare peak, damage to shrubs and small trees is obvious. But it was quite clear in Keith’s studies that the decline phase continued well after the vegetation recovered, and so he postulated a two-factor explanation, winter food shortage followed by high predation losses. He looked for disease and parasite problems in hares but found nothing.

Testing the winter food limitation would appear to be simple but is fraught with problems. Everyone believes that food is an ultimate limiting factor, so that it must be involved in the cyclic dynamics. We began testing food limitation in the mid-1970s and found that one could add natural food or artificial food (rabbit chow) and apparently have no effect on cyclic dynamics. Hares came to the food grids so the density increased by immigration, but the decline started at the same time and at the same rate as on control grids. So what is the role of food?

Our next attempt was to do a factorial experiment adding food, reducing predation, and doing both together. The details are important, replication was never enough for the manipulated treatments, we did it only for 10 years rather than 20 or 30. What we found was that there was an interaction between food addition and mammal predator exclusion so that the combined treatment increased to a much higher density than any single treatment. But this result came with a puzzle. What is the role of food? Hares showed no evidence of malnutrition in the peak or decline, fed hares did not increase their reproductive output. What produced the strong interaction between food addition and predator reduction?

The next breakthrough came when Rudy Boonstra suggested that predator-caused stress might underlie these strange dynamics. Because we could now measure stress with faecal cortisol measures we could test for stress directly in free-ranging hares. The surprise was that this idea worked and Michael Sheriff capped off the stress hypothesis by showing that not only does predator-induced stress reduce reproductive rates, but the stress effect is inherited maternally in the next generation.

The bottom line: the whole dynamics of the snowshoe hare cycle are predator-induced. All the changes in mortality and reproduction are direct and indirect effects of predators chasing and eating hares. The experimental food/predator interaction was mechanistically wrong in targeting food as a major limiting factor.

This of course does not mean that food is irrelevant as an important factor to study in hare cycles. In particular very high peak populations damage shrubs and small trees and we do not yet have the details of how this works out in time. Secondary chemicals are certainly involved here.

Why does all this matter? Two points. First, the hare cycle is often trumpeted as an example of a tri-trophic interaction of food – hares – predators, when in fact it seems to be a simple predator-prey system, as Lotka suggested in 1925. Models of the hare cycle have proliferated over time, and there are far more models of the cycle in existence than there are long-term field studies or field experiments. It is possible to model the hare cycle as a predator-prey oscillation, as a food plant-hare oscillation, as a parasite-hare interaction, as a cosmic particle – hare oscillation, as an intrinsic social – maternal effects interaction, and I have probably missed some other combinations of delayed-density dependent factors that have been discussed. That one can produce a formal mathematical model of the hare cycle does not mean that the chosen factor is the correct one.

The other point I would leave you with is the large amount of field work needed to sort out the mechanisms driving the population dynamics of hares. Ecology is not simple. This enigma of the ten-year cycle has always been a classic example in ecology and perhaps it is now solved. Or perhaps not?

Boonstra, R., D. Hik, G. R. Singleton, and A. Tinnikov. 1998. The impact of predator-induced stress on the snowshoe hare cycle. Ecological Monographs 68:371-394.

Boutin, S., C. J. Krebs, R. Boonstra, M. R. T. Dale, S. J. Hannon, K. Martin, A. R. E. Sinclair, J. N. M. Smith, R. Turkington, M. Blower, A. Byrom, F. I. Doyle, C. Doyle, D. Hik, L. Hofer, A. Hubbs, T. Karels, D. L. Murray, V. Nams, M. O’Donoghue, C. Rohner, and S. Schweiger. 1995. Population changes of the vertebrate community during a snowshoe hare cycle in Canada’s boreal forest. Oikos 74:69-80.

Keith, L. B., and L. A. Windberg. 1978. A demographic analysis of the snowshoe hare cycle. Wildlife Monographs 58:1-70.

Keith, L. B. 1990. Dynamics of snowshoe hare populations. Current Mammalogy 4:119-195.

Krebs, C. J., S. Boutin, R. Boonstra, A. R. E. Sinclair, J. N. M. Smith, M. R. T. Dale, K. Martin, and R. Turkington. 1995. Impact of food and predation on the snowshoe hare cycle. Science 269:1112-1115.

Krebs, C. J., S. Boutin, and R. Boonstra, editors. 2001. Ecosystem Dynamics of the Boreal Forest: the Kluane Project. Oxford University Press, New York.

Sheriff, M. J., C. J. Krebs, and R. Boonstra. 2009. The sensitive hare: sublethal effects of predator stress on reproduction in snowshoe hares. Journal of Animal Ecology 78:1249-1258.

Yan, C., N. C. Stenseth, C. J. Krebs, and Z. Zhang. 2013. Linking climate change to population cycles of hares and lynx. Global Change Biology 19:3263-3271.

On Understanding the Boreal Forest Ecosystem

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I have spent the last 40 years studying the Yukon boreal forest. When I tell this to my associates I get two quite different reactions. First, on the positive side they are impressed with the continuity of effort and the fact that we have learned a great deal about the interactions of species in the Canadian boreal forest (Krebs, Boutin, and Boonstra 2001). Alternatively, on the negative side, I am told I am at fault for doing something of no practical management importance for so long when there are critical conservation problems in our Canadian backyard. Clearly I prefer the positive view, but everyone can decide these issues for themself. What I would like to do here is to lay out what I think are the critical issues in the Canadian boreal forest that have not been addressed so far. I do this in the hope that someone will pick up the torch and look into some of them.

The first issue is that ecological studies of the boreal ecosystem are completely fractionated. The most obvious division is that we have studied the boreal forest in the southwest Yukon with few concurrent studies of the alpine tundra that rises above the forest in every range of mountains. The ecotone between the forest and the tundra is not a strict boundary for many plant species or for many of the vertebrate species we have studied. On a broader scale, there are few studies of aquatic ecosystems within the boreal zone, either in lakes or streams, another disconnect. The wildlife management authorities are concerned with the large vertebrates – moose, bears, caribou, mountain sheep – and this work tends not to tie in with other work on the smaller species in the food web. Interests in the carbon dynamics of the boreal zone have greatly increased but these studies in Canada are also completely disconnected from all other ecological studies that consider population and community dynamics. I think it is fair to say that carbon dynamics in the boreal forest could turn out to be a very local affair, and too much generalization has already been made with too little spatial and temporal data.

One could consider the ecology of the boreal zone like a puzzle, with bits of the puzzle being put together well by researches in one particular area, but with no view of the major dimensions of the total puzzle. This is readily understood when much of the research is done as part of graduate thesis work that has a limit of 4-5 years before researchers move on to another position. It is also a reflection of the low funding that ecology receives.

Within the Yukon boreal forest there are several areas of research that we have not been able to address in the time I and my many colleagues have worked there. Mushroom crops come and go in apparent response to rainfall (Krebs et al. 2008) but we do not know the species of above ground mushrooms and consequently do not know if their fluctuations are uniform or if some species have specialized requirements. Since fungi are probably the main decomposers in this ecosystem, knowing which species will do what as climate changes could be important. On a practical level, foresters are determined to begin logging more and more in the boreal zone but we have no clear understanding of tree regeneration or indeed any good studies of forest succession after fire or logging. Since logging in northern climates is more of a mining operation than a sustainable exercise, such information might be useful before we proceed too far. If the turnaround for a logged forest is of the order of 300 years, any kind of logging is unsustainable in the human time frame.

The list goes on. Snowshoe hare cycles vary greatly in amplitude and we suspect that this is due to predator abundance at the start of any 10 year cycle (Krebs et al. 2013).  The means to test this idea are readily available – satellite telemetry – but it would require a lot of money because these collars are expensive and need to be deployed on lynx, coyotes, and great-horned owls at least. And it needs to be done on a landscape scale with cooperating groups in Alaska, the Yukon, the Northwest Territories, and British Columbia at least. Large-scale ecology to be sure, but the results would be amazing. Radio-telemetry has the ability to interest the public, and each school in the region could have their tagged animals to follow every week. Physicists manage to convince the public that they need lots of money to do large experiments, but ecologists with down to earth questions are loath to ask for a lot of money to find out how the world works on a large scale.

Migratory songbirds have been largely ignored in the boreal forest, partly because they leave Canada after the summer breeding period but at least some of these songbirds appear to be declining in numbers with no clear reason. Yet studies on them are virtually absent, and we monitor numbers in imprecise ways, and continue to mark the position of the deck chairs on the Titanic with no understanding of why it is sinking.

Insect populations in the boreal forest are rarely studied unless they are causing immediate damage to trees, and consequently we have little information on their roles in ecosystem changes.

At the end of this list we can say in the best manner of the investigative reporter why did you not do these things already? The answer to that is also informative. It is because almost all this completed research has been done by university professors and their graduate students and postdocs. What has been done by all my colleagues is amazing because they are not in charge of the boreal forest. The people are, via their governments, provincial and federal. The main job of all of us when this research in the Yukon boreal forest was being done has been education –to teach and do research that will train students in the best methods available. So if you wish to be an investigative reporter, it is best to ask why governments across the board have not funded the federal and provincial research groups that had as their mandate to understand how this ecosystem operates. Because all these questions are about long-term changes, the research group must be stable in funding and person-power in the long term. There is nothing I have seen in my lifetime that comes close to this in government for environmental work except for weather stations. In the short term our governments work to the minute with re-election in sight, and long term vision is suppressed. The environment is seen as a source of dollars and as a convenient garbage can and science only gets in the way of exploitation. And in the end Mother Nature will take care of herself, so they hope. Perhaps we need a few Bill Gates’ types to get interested in funding long-term research.

But there remain for ecologists many interesting questions that are at present not answered, and will help us complete the picture of how this large ecosystem operates.

Krebs, C.J., S. Boutin, and R. Boonstra, editors. 2001. Ecosystem Dynamics of the Boreal Forest: the Kluane Project. Oxford University Press, New York.

Krebs, C.J., P. Carrier, S. Boutin, R. Boonstra, and E.J. Hofer. 2008. Mushroom crops in relation to weather in the southwestern Yukon. Botany 86:1497-1502.

Krebs, C.J., K. Kielland, J. Bryant, M. O’Donoghue, F. Doyle, C. McIntyre, D. DiFolco, N. Berg, S. Carrier, R. Boonstra, S. Boutin, A.J. Kenney, D.G. Reid, K. Bodony, J. Putera, and T. Burke. 2013. Synchrony in the snowshoe hare cycle in northwestern North America, 1970-2012. Canadian Journal of Zoology 91:562-572.