Monthly Archives: February 2016

On Caribou and the Conservation Conundrum

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

On Gravity Waves and the 1%

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.)

Hypothesis testing using field data and experiments is definitely NOT a waste of time

At the ESA meeting in 2014 Greg Dwyer (University of Chicago) gave a talk titled “Trying to understand ecological data without mechanistic models is a waste of time.” This theme has recently been reiterated on Dynamic Ecology Jeremy Fox, Brian McGill and Megan Duffy’s blog (25 January 2016 https://dynamicecology.wordpress.com/2016/01/25/trying-to-understand-ecological-data-without-mechanistic-models-is-a-waste-of-time/).  Some immediate responses to this blog have been such things as “What is a mechanistic model?” “What about the use of inappropriate statistics to fit mechanistic models,” and “prediction vs. description from mechanistic models”.  All of these are relevant and interesting issues in interpreting the value of mechanistic models.

The biggest fallacy however in this blog post or at least the title of the blog post is the implication that field ecological data are collected in a vacuum.  Hypotheses are models, conceptual models, and it is only in the absence of hypotheses that trying to understand ecological data is a “waste of time”. Research proposals that fund field work demand testable hypotheses, and testing hypotheses advances science. Research using mechanistic models should also develop testable hypotheses, but mechanistic models are certainly are not the only route to hypothesis creation of testing.

Unfortunately, mechanistic models rarely identify how the robustness and generality of the model output could be tested from ecological data and often fail comprehensively to properly describe the many assumptions made in constructing the model. In fact, they are often presented as complete descriptions of the ecological relationships in question, and methods for model validation are not discussed. Sometimes modelling papers include blatantly unrealistic functions to simplify ecological processes, without exploring the sensitivity of results to the functions.

I can refer to my own area of research expertise, population cycles for an example here.  It is not enough for example to have a pattern of ups and downs with a 10-year periodicity to claim that the model is an acceptable representation of cyclic population dynamics of for example a forest lepidopteran or snowshoe hares. There are many ways to get cyclic dynamics in modeled systems. Scientific progress and understanding can only be made if the outcome of conceptual, mechanistic or statistical models define the hypotheses that could be tested and the experiments that could be conducted to support the acceptance, rejection or modification of the model and thus to inform understanding of natural systems.

How helpful are mechanistic models – the gypsy moth story

Given the implication of Dwyer’s blog post (or at least blog post title) that mechanistic models are the only way to ecological understanding, it is useful to look at models of gypsy moth dynamics, one of Greg’s areas of modeling expertise, with the view toward evaluating whether model assumptions are compatible with real-world data Dwyer et al.  2004  (http://www.nature.com/nature/journal/v430/n6997/abs/nature02569.html)

Although there has been considerable excellent work on gypsy moth over the years, long-term population data are lacking.  Population dynamics therefore are estimated by annual estimates of defoliation carried out by the US Forest Service in New England starting in 1924. These data show periods of non-cyclicity, two ten-year cycles (peaks in 1981 and 1991 that are used by Dwyer for comparison to modeled dynamics for a number of his mechanistic models) and harmonic 4-5 year cycles between 1943 and1979 and since the 1991 outbreak. Based on these data 10-year cycles are the exception not the rule for introduced populations of gypsy moth. Point 1. Many of the Dwyer mechanistic models were tested using the two outbreak periods and ignored over 20 years of subsequent defoliation data lacking 10-year cycles. Thus his results are limited in their generality.

As a further example a recent paper, Elderd et al. (2013)  (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3773759/) explored the relationship between alternating long and short cycles of gypsy moth in oak dominated forests by speculating that inducible tannins in oaks modifies the interactions between gypsy moth larvae and viral infection. Although previous field experiments (D’Amico et al. 1998) http://onlinelibrary.wiley.com/doi/10.1890/0012-9658(1998)079%5b1104:FDDNAW%5d2.0.CO%3b2/abstract concluded that gypsy moth defoliation does not affect tannin levels sufficiently to influence viral infection, Elderd et al. (2013) proposed that induced tannins in red oak foliage reduces variation in viral infection levels and promotes shorter cycles. In this study, an experiment was conducted using jasmonic acid sprays to induce oak foliage. Point 2 This mechanistic model is based on experiments using artificially induced tannins as a mimic of insect damage inducing plant defenses. However, earlier fieldwork showed that foliage damage does not influence virus transmission and thus does not support the relevance of this mechanism.

In this model Elderd et al. (2013) use a linear relationship for viral transmission (transmission of infection on baculovirus density) based on two data points and the 0 intercept. In past mechanistic models and in a number of other systems the relationship between viral transmission and host density is nonlinear (D’Amico et al. 2005, http://onlinelibrary.wiley.com/doi/10.1111/j.0307-6946.2005.00697.x/abstract;jsessionid=D93D281ACD3F94AA86185EFF95AC5119.f02t02?userIsAuthenticated=false&deniedAccessCustomisedMessage= Fenton et al. 2002, http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2656.2002.00656.x/full). Point 3. Data are insufficient to accurately describe the viral transmission relationship used in the model.

Finally the Elderd et al. (2013) model considers two types of gypsy moth habitat – one composed of 43% oaks that are inducible and the other of 15% oaks and the remainder of the forest composition is in adjacent blocks of non-inducible pines. Data show that gypsy moth outbreaks are limited to areas with high frequencies of oaks. In mixed forests, pines are only fed on by later instars of moth larvae when oaks are defoliated. The pines would be interspersed amongst the oaks not in separate blocks as in the modeled population. Point 4.  Patterns of forest composition in the models that are crucial to the result are unrealistic and this makes the interpretation of the results impossible.

Point 5 and conclusion. Because it can be very difficult to critically review someone else’s mechanistic model as model assumptions are often hidden in supplementary material and hard to interpret, and because relationships used in models are often arbitrarily chosen and not based on available data, it could be easy to conclude that “mechanistic models are misleading and a waste of time”. But of course that wouldn’t be productive. So my final point is that closer collaboration between modelers and data collectors would be the best way to ensure that the models are reasonable and accurate representations of the data.  In this way understanding and realistic predictions would be advanced. Unfortunately the great push to publish high profile papers works against this collaboration and manuscripts of mechanistic models rarely include data savvy referees.

D’Amico, V., J. S. Elkinton, G. Dwyer, R. B. Willis, and M. E. Montgomery. 1998. Foliage damage does not affect within-season transmission of an insect virus. Ecology 79:1104-1110.

D’Amico, V. D., J. S. Elkinton, P. J.D., J. P. Buonaccorsi, and G. Dwyer. 2005. Pathogen clumping: an explanation for non-linear transmission of an insect virus. Ecological Entomology 30:383-390.

Dwyer, G., F. Dushoff, and S. H. Yee. 2004. The combined effects of pathogens and predators on insect outbreaks. Nature 430:341-345.

Elderd, B. D., B. J. Rehill, K. J. Haynes, and G. Dwyer. 2013. Induced plant defenses, host–pathogen interactions, and forest insect outbreaks. Proceedings of the National Academy of Sciences 110:14978-14983.

Fenton, A., J. P. Fairbairn, R. Norman, and P. J. Hudson. 2002. Parasite transmission: reconciling theory and reality. Journal of Animal Ecology 71:893-905.

On Conservation Dilemmas

Conservation is a strange mix of science and politics. What exactly the fraction of the mix is I would not hazard a guess, but probably the science of conservation biology is a small part of the total. That is not an excuse for anyone not to go into conservation as a career but you need to realize what you are walking into.

Many people have written about this but the latest radio announcements about wolf killing in western Canada got me thinking again about the problem of killing one native species to possibly protect another native species. Wolves eat caribou, mountain caribou are endangered, wolves are not (at the moment) endangered, therefore a simple solution: shoot A to save B. But think about this a bit and first of all realize that this is certainly not a scientific decision. Science tests hypotheses but it does not decree policies of action. The scientific issue buried in this controversy is whether or not shooting wolves will save the mountain caribou. How far, as a conservation scientist, do you trace the causality of a problem like this? Wolves eat a lot of moose as well as caribou. Oil and gas companies make roads to their wells and gas fields, paving the way for easy wolf dispersal to catch more moose or caribou. Moose love successional landscapes, and forestry companies love to make moonscapes by logging, generating successional landscapes. Deer also love farmland and successional landscapes, and mountain lions increase when deer increase. Mountain lions also take the occasional jogger. Where do we stop the causal chain?

If causality stops at the farm gate, wolves eat caribou therefore shoot them, life is simple. But to an ecologist this is missing the elephant in the room, our human use of landscapes. We make landscapes better for some species and worse for others, but we typically refuse to bear any responsibility for these landscape changes. How many logging companies or oil companies have been prosecuted for making wolves more abundant? So we go back to the farm gate and argue that killing wolves will have no effect on dwindling caribou because there are other predators out there – bears for example – that also eat caribou. And an honoured law of conservation biology is that once you get to a low population for the most part you are doomed no matter what happens. You cannot in a limiting case save a caribou herd of n = 1. But let us be optimistic as ecologists and argue that killing wolves will save the caribou. We have to add “this year” to that statement because, as Bob Hayes (2010) so elegantly argued in his book, once you start killing wolves you can never stop if that is your management solution. Caribou are caught in a nexus of wolves, bears, moose, deer, and elk in parts of western North America, and there is as yet no clear way of analyzing this nexus in a predictive manner. Killing wolves is the answer, but what is the question?

Money for management is yet another matter that enters the picture. Dollars spent on helicopter gunships cannot be spent on habitat improvements for other less charismatic species. So one needs value judgements here also, and this is not a scientific question but a policy one.

I think these conservation dilemmas are a general problem, and no doubt much is written about them. Do we kill an introduced species to save a native one? Do we forget about an introduced pest because a threatened bird species feeds on the pest? Do we get rid of an introduced weed that is poisonous to cattle but provides nectar for bees? Or in the present case do we kill one native species to potentially save another native species? Few of these questions are scientific questions and few can ever be sorted out by getting more data. So this is the problem I am not sure how to face. We go into conservation ecology to do science, but in the end we become a policy advisor that can be easily dismissed for political, social, or budget reasons. There is no way around this as far as I can see. If you think wolves are a valuable part of biodiversity, agitate not to kill them. If you think caribou will be preserved by killing wolves, go for the guns. All the arguments about the role of top predators in ecosystems (Ordiz et al. 2013, Ripple et al. 2014) can fall on deaf ears if society has a different value system than conservation biologists have.

Hayes, B. (2010) Wolves of the Yukon. Wolves of the Yukon Publishing, Smithers, B.C.

Ordiz, A., Bischof, R. & Swenson, J.E. (2013) Saving large carnivores, but losing the apex predator? Biological Conservation, 168, 128-133. doi: 10.1016/j.biocon.2013.09.024

Ripple, W.J., Estes, J.A., Beschta, R.L., Wilmers, C.C., Ritchie, E.G., Hebblewhite, M., Berger, J., Elmhagen, B., Letnic, M., Nelson, M.P., Schmitz, O.J., Smith, D.W., Wallach, A.D. & Wirsing, A.J. (2014) Status and ecological effects of the world’s largest carnivores. Science, 343, 1241484. doi: 10.1126/science.1241484