Tag Archives: climate change

Climate Change and Ecological Science

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One dominant paradigm of the ecological literature at the present time is what I would like to call the Climate Change Paradigm. Stated in its clearest form, it states that all temporal ecological changes now observed are explicable by climate change. The test of this hypothesis is typically a correlation between some event like a population decline, an invasion of a new species into a community, or the outbreak of a pest species and some measure of climate. Given clever statistics and sufficient searching of many climatic measurements with and without time lags, these correlations are often sanctified by p< 0.05. Should we consider this progress in ecological understanding?

An early confusion in relating climate fluctuations to population changes was begun by labelling climate as a density independent factor within the density-dependent model of population dynamics. Fortunately, this massive confusion was sorted out by Enright (1976) but alas I still see this error repeated in recent papers about population changes. I think that much of the early confusion of climatic impacts on populations was due to this classifying all climatic impacts as density-independent factors.

One’s first response perhaps might be that indeed many of the changes we see in populations and communities are indeed related to climate change. But the key here is to validate this conclusion, and to do this we need to talk about the mechanisms by which climate change is acting on our particular species or species group. The search for these mechanisms is much more difficult than the demonstration of a correlation. To become more convincing one might predict that the observed correlation will continue for the next 5 (10, 20?) years and then gather the data to validate the correlation. Many of these published correlations are so weak as to preclude any possibility of validation in the lifetime of a research scientist. So the gold standard must be the deciphering of the mechanisms involved.

And a major concern is that many of the validations of the climate change paradigm on short time scales are likely to be spurious correlations. Those who need a good laugh over the issue of spurious correlation should look at Vigen (2015), a book which illustrates all too well the fun of looking for silly correlations. Climate is a very complex variable and a nearly infinite number of measurements can be concocted with temperature (mean, minimum, maximum), rainfall, snowfall, or wind, analyzed over any number of time periods throughout the year. We are always warned about data dredging, but it is often difficult to know exactly what authors of any particular paper have done. The most extreme examples are possible to spot, and my favorite is this quotation from a paper a few years ago:

“A total of 864 correlations in 72 calendar weather periods were examined; 71 (eight percent) were significant at the p< 0.05 level. …There were 12 negative correlations, p< 0.05, between the number of days with (precipitation) and (a demographic measure). A total of 45- positive correlations, p<0.05, between temperatures and (the same demographic measure) were disclosed…..”

The climate change paradigm is well established in biogeography and the major shifts in vegetation that have occurred in geological time are well correlated with climatic changes. But it is a large leap of faith to scale this well established framework down to the local scale of space and a short-term time scale. There is no question that local short term climate changes can explain many changes in populations and communities, but any analysis of these kinds of effects must consider alternative hypotheses and mechanisms of change. Berteaux et al. (2006) pointed out the differences between forecasting and prediction in climate models. We desire predictive models if we are to improve ecological understanding, and Berteaux et al. (2006) suggested that predictive models are successful if they follow three rules:

(1) Initial conditions of the system are well described (inherent noise is small);

(2) No important variable is excluded from the model (boundary conditions are defined adequately);

(3) Variables used to build the model are related to each other in the proper way (aggregation/representation is adequate).

Like most rules for models, whether these conditions are met is rarely known when the model is published, and we need subsequent data from the real world to see if the predictions are correct.

I am much less convinced that forecasting models are useful in climate research. Forecasting models describe an ecological situation based on correlations among the measurements available with no clear mechanistic model of the ecological interactions involved. My concern was highlighted in a paper by Myers (1998) who investigated for fish populations the success of published juvenile recruitment-environmental factor (typically temperature) correlations and found that very few forecasting models were reliable when tested against additional data obtained after publication. It would be useful for someone to carry out a similar analysis for bird and mammal population models.

Small mammals show some promise for predictive models in some ecosystems. The analysis by Kausrud et al. (2008) illustrates a good approach to incorporating climate into predictive explanations of population change in Norwegian lemmings that involve interactions between climate and predation. The best approach in developing these kinds of explanations and formulating them into models is to determine how the model performs when additional data are obtained in the years to follow publication.

The bottom line is to avoid spurious climatic correlations by describing and evaluating mechanistic models that are based on observable biological factors. And then make predictions that can be tested in a realistic time frame. If we cannot do this, we risk publishing fairy tales rather than science.

Berteaux, D., et al. (2006) Constraints to projecting the effects of climate change on mammals. Climate Research, 32, 151-158. doi: 10.3354/cr032151

Enright, J. T. (1976) Climate and population regulation: the biogeographer’s dilemma. Oecologia, 24, 295-310.

Kausrud, K. L., et al. (2008) Linking climate change to lemming cycles. Nature, 456, 93-97. doi: 10.1038/nature07442

Myers, R. A. (1998) When do environment-recruitment correlations work? Reviews in Fish Biology and Fisheries, 8, 285-305. doi: 10.1023/A:1008828730759

Vigen, T. (2015) Spurious Correlations, Hyperion, New York City. ISBN: 978-031-633-9438

Fishery Models and Ecological Understanding

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Anyone interested in population dynamics, fisheries management, or ecological understanding in general will be interested to read the exchanges in Science, 23 April 2016 on the problem of understanding stock changes in the northern cod (Gadus morhua) fishery in the Gulf of Maine. I think this exchange is important to read because it illustrates two general problems with ecological science – how to understand ecological changes with incomplete data, and how to extrapolate what is happening into taking some management action.

What we have here are sets of experts promoting a management view and others contradicting the suggested view. There is no question but that ecologists have made much progress in understanding both marine and freshwater fisheries. Probably the total number of person-years of research on marine fishes like the northern cod would dwarf that on all other ecological studies combined. Yet we are still arguing about fundamental processes in major marine fisheries. You will remember that the northern cod in particular was one of the largest fisheries in the world when it began to be exploited in the 16th century, and by the 1990s it was driven to about 1% of its prior abundance, almost to the status of a threatened species.

Pershing et al. (2015) suggested, based on data on a rise in sea surface temperature in the Gulf of Maine, that cod mortality had increased with temperature and this was causing the fishery management model to overestimate the allowable catch. Palmer et al. (2016) and Swain et al. (2016) disputed their conclusions, and Pershing et al. (2016) responded. The details are in these papers and I do not pretend to know whose views are closest to be correct.

But I’m interested in two facts. First, Science clearly thought this controversy was important and worth publishing, even in the face of a 99% rejection rate for all submissions to that journal. Second, it illustrates that ecology faces a lot of questions when it makes conclusions that natural resource managers should act upon. Perhaps it is akin to medicine in being controversial, even though it is all supposed to be evidence based. It is hard to imagine physical scientists or engineers arguing so publically over the design of a bridge or a hydroelectric dam. Why is it that ecologists so often spend time arguing with one another over this or that theory or research finding? If we admit that our conclusions about the world’s ecosystems are so meager and uncertain, does it mean we have a very long way to go before we can claim to be a hard science? We would hope not but what is the evidence?

One problem so well illustrated here in these papers is the difficulty of measuring the parameters of change in marine fish populations and then tying these estimates to models that are predictive of changes required for management actions. The combination of less than precise data and models that are overly precise in their assumptions could be a deadly combination in the ecological management of natural resources.

Palmer, M.C., Deroba, J.J., Legault, C.M., and Brooks, E.N. 2016. Comment on “Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery”. Science 352(6284): 423-423. doi:10.1126/science.aad9674.

Pershing, A.J., Alexander, M.A., Hernandez, C.M., Kerr, L.A., Le Bris, A., Mills, K.E., Nye, J.A., Record, N.R., Scannell, H.A., Scott, J.D., Sherwood, G.D., and Thomas, A.C. 2016. Response to Comments on “Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery”. Science 352(6284): 423-423. doi:10.1126/science.aae0463.

Pershing, A.J., Alexander, M.A., Hernandez, C.M., Kerr, L.A., Le Bris, A., Mills, K.E., Nye, J.A., Record, N.R., Scannell, H.A., Scott, J.D., Sherwood, G.D., and Thomas, A.C. 2015. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350(6262): 809-812. doi:10.1126/science.aac9819.

Swain, D.P., Benoît, H.P., Cox, S.P., and Cadigan, N.G. 2016. Comment on “Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery”. Science 352(6284): 423-423. doi:10.1126/science.aad9346.

Reducing Greenhouse Gases at the Local Scale

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This blog is devoted to the simple question of what we might do about climate change at a very small scale. As individuals we can do little directly about the big issues of fracking and oil extraction from tar sands and shale deposits. Of course we can and should vote about these large issues, but my question is what can we do at a local level to support the Paris Agreement?

This was all brought to my attention on a recent drive of 50 km from Haines Junction in the southern Yukon north to Kluane Lake. Along the side of the Alaska Highway on each side of the road for a perpendicular distance of perhaps 20 m the highways department had mowed down all the vegetation to the ground level along a stretch of about 25 km. Willows 1-2 m in height, small aspen, spruce and poplar trees up to 3 m in height were all mowed down and chopped into small pieces. This observation gave rise to two thoughts. First, highways departments have always done this kind of mowing so why worry about it? But second, why should we keep doing now what we always did in the past?

We have just signed the Paris Agreement to try to stop the increases of greenhouse gases in the atmosphere. This means we should be looking at everything we do to see if it is generating more greenhouse gases than necessary. Mowing down the edges of highways has two detrimental effects on our environment. First, diesel or petrol is used to run the machines that do the mowing, Second, we have now lost the only mechanism we currently have for taking CO2 out of the atmosphere, plant growth via photosynthesis. We mow down the plants, thus making compost that releases CO2 as it decays, and we lose the structure of the road edge that captures CO2 at no cost to us. But of course new plants will now start to colonize and re-grow along the road edge, capturing CO2, but the key point here is that in northern Canada it will take probably 20-30 years to have the vegetation recover to the point that it was before mowing. Thus on every score this mowing along the highway is a direct affront to the Paris Agreement.

The argument about road edges is always to protect vehicles from wildlife suddenly coming out of the forest on to the road and causing a collision. The frequency of this for the larger species would have to be measured, and the assumption that a mowed strip reduces collisions with wildlife would have to be quantified. In my observations a nicely mowed strip in northern Canada becomes green early in the spring and in fact then attracts large and small herbivores like moose, bison, and hares to the edges of the road. Thus mowing might actually increase the probability of collisions with wildlife. In Newfoundland Tanner and Leroux (2015) showed that moose browsed less in recently cut highway edges but this effect might be lost within a few years. The critical question if moose-vehicle collisions are to be reduced is to know exactly what actions produce fewer accidents (Jägerbrand and Antonson, 2016). Reducing speed limits has a strong effect on accident rates. No one has looked into the greenhouse gas issue regarding the cost and benefit of roadside clearing. Many authors (e.g. Meisingset et al. 2014) point out how little serious experimental work has been done on the wildlife collision issue, another opportunity for adaptive management.

The highways department would probably consider this all very silly to worry about. But it does raise the more general issue that many others have pointed out: should we somehow have a greenhouse gas indicator app that would tell us for our own houses, vehicles, and property what we are doing in our everyday actions to assist the reduction of greenhouse gases as mandated in the Paris Agreement. It cannot simply be business as usual.

Jägerbrand, A.K., and Antonson, H. 2016. Driving behaviour responses to a moose encounter, automatic speed camera, wildlife warning sign and radio message determined in a factorial simulator study. Accident Analysis & Prevention 86(1): 229-238. doi:10.1016/j.aap.2015.11.004.

Meisingset, E.L., Loe, L.E., Brekkum, Ø., and Mysterud, A. 2014. Targeting mitigation efforts: The role of speed limit and road edge clearance for deer–vehicle collisions. Journal of Wildlife Management 78(4): 679-688. doi:10.1002/jwmg.712.

Tanner, A.L., and Leroux, S.J. 2015. Effect of roadside vegetation cutting on moose browsing. PloS One 10(8): e0133155. doi:10.1371/journal.pone.0133155.

On Critical Questions in Biodiversity and Conservation Ecology

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Biodiversity can be a vague concept with so many measurement variants to make one wonder what it is exactly, and how to incorporate ideas about biodiversity into scientific hypotheses. Even if we take the simplest concept of species richness as the operational measure, many questions arise about the importance of the rare species that make up most of the biodiversity but so little of the biomass. How can we proceed to a better understanding of this nebulous ecological concept that we continually put before the public as needing their attention?

Biodiversity conservation relies on community and ecosystem ecology for guidance on how to advance scientific understanding. A recent paper by Turkington and Harrower (2016) articulates this very clearly by laying out 7 general questions for analyzing community structure for conservation of biodiversity. As such these questions are a general model for community and ecosystem ecology approaches that are needed in this century. Thus it would pay to look at these 7 questions more closely and to read this new paper. Here is the list of 7 questions from the paper:

  1. How are natural communities structured?
  2. How does biodiversity determine the function of ecosystems?
  3. How does the loss of biodiversity alter the stability of ecosystems?
  4. How does the loss of biodiversity alter the integrity of ecosystems?
  5. Diversity and species composition
  6. How does the loss of species determine the ability of ecosystems to respond to disturbances?
  7. How does food web complexity and productivity influence the relative strength of trophic interactions and how do changes in trophic structure influence ecosystem function?

Turkington and Harrower (2016) note that each of these 7 questions can be asked in at least 5 different contexts in the biodiversity hotspots of China:

  1. How do the observed responses change across the 28 vegetation types in China?
  2. How do the observed responses change from the low productivity grasslands of the Qinghai Plateau to higher productivity grasslands in other parts of China?
  3. How do the observed responses change along a gradient in the intensity of human use or degradation?
  4. How long should an experiment be conducted given that the immediate results are seldom indicative of longer-term outcomes?
  5. How does the scale of the experiment influence treatment responses?

There are major problems in all of this as Turkington and Harrower (2016) and Bruelheide et al. (2014) have discussed. The first problem is to determine what the community is or what the bounds of an ecosystem are. This is a trivial issue according to community and ecosystem ecologists, and all one does is draw a circle around the particular area of interest for your study. But two points remain. Populations, communities, and ecosystems are open systems with no clear boundaries. In population ecology we can master this problem by analyses of movements and dispersal of individuals. On a short time scale plants in communities are fixed in position while their associated animals move on species-specific scales. Communities and ecosystems are not a unit but vary continuously in space and time, making their analysis difficult. The species present on 50 m2 are not the same as those on another plot 100 m or 1000 m away even if the vegetation types are labeled the same. So we replicate plots within what we define to be our community. If you are studying plant dynamics, you can experimentally place all plant species selected in defined plots in a pre-arranged configuration for your planting experiments, but you cannot do this with animals except in microcosms. All experiments are place specific, and if you consider climate change on a 100 year time scale, they are also time specific. We can hope that generality is strong and our conclusions will apply in 100 years but we do not know this now.

But we can do manipulative experiments, as these authors strongly recommend, and that brings a whole new set of problems, outlined for example in Bruelheide et al. (2014, Table 1, page 78) for a forestry experiment in southern China. Decisions about how many tree species to manipulate in what size of plots and what planting density to use are all potentially critical to the conclusions we reach. But it is the time frame of hypothesis testing that is the great unknown. All these studies must be long-term but whether this is 10 years or 50 years can only be found out in retrospect. Is it better to have, for example, forestry experiments around the world carried out with identical protocols, or to adopt a laissez faire approach with different designs since we have no idea yet of what design is best for answering these broad questions.

I suspect that this outline of the broad questions given in Turkington and Harrower (2016) is at least a 100 year agenda, and we need to be concerned how we can carry this forward in a world where funding of research questions has a 3 or 5 year time frame. The only possible way forward, until we win the Lottery, is for all researchers to carry out short term experiments on very specific hypotheses within this framework. So every graduate student thesis in experimental community and ecosystem ecology is important to achieving the goals outlined in these papers. Even if this 100 year time frame is optimistic and achievable, we can progress on a shorter time scale by a series of detailed experiments on small parts of the community or ecosystem at hand. I note that some of these broad questions listed above have been around for more than 50 years without being answered. If we redefine our objectives more precisely and do the kinds of experiments that these authors suggest we can move forward, not with the solution of grand ideas as much as with detailed experimental data on very precise questions about our chosen community. In this way we keep the long-range goal posts in view but concentrate on short-term manipulative experiments that are place and time specific.

This will not be easy. Birds are probably the best studied group of animals on Earth, and we now have many species that are changing in abundance dramatically over large spatial scales (e.g. http://www.stateofcanadasbirds.org/ ). I am sobered by asking avian ecologists why a particular species is declining or dramatically increasing. I never get a good answer, typically only a generally plausible idea, a hand waving explanation based on correlations that are not measured or well understood. Species recovery plans are often based on hunches rather than good data, with few of the key experiments of the type requested by Turkington and Harrower (2016). At the moment the world is changing rather faster than our understanding of these ecological interactions that tie species together in communities and ecosystems. We are walking when we need to be running, and even the Red Queen is not keeping up.

Bruelheide, H. et al. 2014. Designing forest biodiversity experiments: general considerations illustrated by a new large experiment in subtropical China. Methods in Ecology and Evolution, 5, 74-89. doi: 10.1111/2041-210X.12126

Turkington, R. & Harrower, W.L. 2016. An experimental approach to addressing ecological questions related to the conservation of plant biodiversity in China. Plant Diversity, 38, 1-10. Available at: http://journal.kib.ac.cn/EN/volumn/current.shtml

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

On Funding for Agricultural Research

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One of the most important problems of our day is the interaction between human population growth and the maintenance of sustainable agriculture in the face of climate change. I am currently sitting at the International Rice Research Institute (IRRI) near Manila where I am told they are responding to a 15-20% reduction in funding for their work. I have found this funding situation to be so ridiculous that I have decided to write this blog. Please stop reading if you think agricultural research already has too much funding, or that climate change and sustainable agriculture are not very important issues in comparison to our need for economic growth and increased wealth.

The critical issues here in Southeast Asia are the increasing human population and the productivity of rice agriculture. IRRI has done and is doing outstanding research to raise production of rice with new varieties and to control pests of rice with clever techniques that minimize the spreading of poisons, which everyone agrees must be minimized to protect agricultural and natural ecosystems. Present research concentrates on the ‘yield gap’, the difference between the actual production from farmer’s fields and the maximum possible yield that can be achieved with the best farm practices. The yield gap can be closed with more research by both social and natural scientists, but that is what is under stress now. IRRI operates with funding from a variety of governments and from private donors. Research funds are now being reduced from many of these sources, and the usual explanation is the faltering global economy combined with the severe refugee problems in the Middle East.

Consequently we now do not have enough money to support the most important research on a crop – rice – that is the essential food of half of the Earth’s human population. And it is not just research on rice that is being reduced, but that on corn, wheat, and any other crop you wish to name. Governments of developed countries like Canada, Australia and the USA are reducing their funding of agricultural research. Anyone who likes to eat might think this is the most ridiculous decision of all because agricultural research is an essential part of poverty reduction in the world and overall human welfare. So I ask a simple question – Why? How is it that you can visit any city in a developed country and see obscene excesses of wealth defined in any way you wish? Yet our governments continue to tell us that we are taxed too much, and we cannot afford more foreign aid, and that if we raised the taxation rate to help the poor of the Earth, our countries would all collapse economically. Yet historically taxes have often been raised during World Wars with general agreement that we needed to do so to achieve society’s goals. The goal now must be poverty reduction and sustainability in agriculture as well as in population. Important efforts are being done on these fronts by many people, but we can and must do more if we wish to leave a suitable Earth for future generations.

At the same time this shortage of funding should not all be laid at the feet of governments. Private wealth continues to increase in the world, and private gifts to research agencies like IRRI and to universities are substantial. But if we believe Piketty (2014), the rich will only get richer in the present economic climate and perhaps the message needs to be sent that donations are long overdue from the wealthy to establish foundations devoted to the problems of sustainability in agriculture, population, and society, as well as the protection of biodiversity. The inactions of people and governments in the past are well documented in books like Diamond (2005). Many scientific papers are mapping and have mapped the way forward to achieve a sustainable society (e.g. Cunningham et al. 2013). To make effective progress we must begin reinvestment in agriculture while not neglecting the human tragedies of our time. It can be both-and rather than either-or.

Cunningham, S.A., et al. (2013) To close the yield-gap while saving biodiversity will require multiple locally relevant strategies. Agriculture, Ecosystems & Environment, 173, 20-27. doi 10.1016/j.agee.2013.04.007

Diamond, J. (2005) Collapse: How Societies Choose to Fail or Succeed. Viking, New York. 575 pp. ISBN: 0670033375

Piketty, T. (2014) Capital in the Twenty-First Century. Belknap Press, Harvard University, Boston. 696 pp. ISBN 9780674430006

The Volkswagen Syndrome and Ecological Science

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We have all been hearing the reports that Volkswagen fixed diesel cars by some engineering trick to show low levels of pollution, while the actual pollution produced on the road is 10-100 times higher than the laboratory predicted pollution levels. I wonder if this is an analogous situation to what we have in ecology when we compare laboratory studies and conclusions to real-world situations.

The push in ecology has always been to simplify the system first by creating models full of assumptions, and then by laboratory experiments that are greatly oversimplified compared with the real world. There are very good reasons to try to do this, since the real world is rather complicated, but I wonder if we should call a partial moratorium on such research by conducting a review of how far we have been led astray by both simple models and simple laboratory population, community and ecosystem studies in microcosms and mesocosms. I can almost hear the screams coming up that of course this is not possible since graduate students must complete a degree in 2 or 3 years, and postdocs must do something in 2 years. If this is our main justification for models and microcosms, that is fair enough but we ought to be explicit about stating that and then evaluate how much we have been misled by such oversimplification.

Let me try to be clear about this problem. It is an empirical question of whether or not studies in laboratory or field microcosms can give us reliable generalizations for much more extensive communities and ecosystems that are not in some sense space limited or time limited. I have a personal view on this question, heavily influenced by studies of small mammal populations in microcosms. But my experience may be atypical of the rest of natural systems, and this is an empirical question, not one on which we can simply state our opinions.

If the world is much more complex than our current understanding of it, we must conclude that an extensive list of climate change papers should be moved to the fiction section of our libraries. If we assume equilibrial dynamics in our communities and ecosystems, we fly in violation of almost all long term studies of populations, communities, and ecosystems. The problem lies in the space and time vision of our science. Our studies are too short to show even a good representation of dynamics over a 100 year time scale, and the problems of landscape ecology highlight that what we see in patch A may be greatly influenced by whether patches B and C are close by or not. We see this darkly in a few small studies but are compelled to believe that such landscape effects are unusual or atypical. This may in fact be the case, but we need much more work to see if it is rare or common. And the broader issue is what use do we as ecologists have for ecological predictions that cannot be tested without data for the next 100 years?

Are all our grand generalizations of ecology falling by the wayside without us noticing it? Prins and Gordon (2014) in their overview seem to feel that the real world is poorly reflected in many of our beloved theories. I think this is a reflection of the Volkswagen Syndrome, of the failure to appreciate that the laboratory in its simplicity is so far removed from real world community and ecosystem dynamics that we ought to start over to build an ecological edifice of generalizations or rules with a strong appreciation of the limited validity of most generalizations until much more research has been done. The complications of the real world can be ignored in the search for simplicity, but one has to do this with the realization that predictions that flow from faulty generalizations can harm our science. We ecologists have very much research yet to do to establish secure generalizations that lead to reliable predictions.

Prins, H.H.T. & Gordon, I.J. (2014) Invasion Biology and Ecological Theory: Insights from a Continent in Transformation. Cambridge University Press, Cambridge. 540 pp. ISBN 9781107035812.

In Praise of Long Term Studies

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I have been fortunate this week to have had a tour of the Konza Prairie Long Term Ecological Research (LTER) site in central Kansas. Kansas State University has run this LTER site for about the last 30 years with support from the National Science Foundation (NSF) of the USA. Whoever set up this program in NSF so many years ago deserves the praise of all ecologists for their foresight, and the staff of KSU who have managed the Konza site should be given our highest congratulations for their research plan and their hard work.

The tall grass prairie used to occupy much of the central part of the temperate zone of North America from Canada to Texas. There is almost none of it left, in Kansas about 1% of the original area with the rest given over to agriculture and grazing. The practical person sees this as progress through the lens of dollar bills, the ecologist sees it as a biodiversity catastrophe. The big questions for the tall-grass prairie are clear and apply to many ecosystems: What keeps this community going? Is it fire or grazing or both in some combination? If fire is too frequent, what are the consequences for the plant community of tall-grass prairie, not to mention the aquatic community of fishes in the streams and rivers? How can shrub and tree encroachment be prevented? All of these questions are under investigation, and the answers are clear in general but uncertain in many details about effects on particular species of birds or forbs.

It strikes me that ecology very much needs more LTER programs. To my knowledge Canada and Australia have nothing like this LTER program that NSF funds. We need to ask why this is, and whether this money could be used much better for other kinds of ecological research. To my mind ecology is unique among the hard sciences in requiring long term studies, and this is because the ecological world is not an equilibrial system in the way we thought 50 years ago. Environments change, species geographical ranges change, climate varies, and all of this on top of the major human impacts on the Earth. So we need to ask questions like why is the tall grass prairie so susceptible to shrub and tree encroachment now when it apparently was not this way 200 years ago? Or why are polar bears now threatened in Hudson’s Bay when they thrived there for the last 1000 or more years? The simple answer is that the ecosystem has changed, but the ecologist wants to know how and why, so that we have some idea if these changes can be managed.

By contrast with ecological systems, physics and chemistry deal with equilibrial systems. So nobody now would investigate whether the laws of gravitation have changed in the last 30 years, and you would be laughed out of the room by physical scientists for even asking such a question and trying to get a research grant to answer this question. Continuous system change is what makes ecology among the most difficult of the hard sciences. Understanding the ecosystem dynamics of the tall-grass prairie might have been simpler 200 years ago, but is now complicated by landscape alteration by agriculture, nitrogen deposition from air pollution, the introduction of weeds from overseas, and the loss of large herbivores like bison.

Long-term studies always lead us back to the question of when we can quit such studies. There are two aspects of this issue. One is scientific, and that question is relatively easy to answer – stop when you find there are no important questions left to pursue. But this means we must have some mental image of what ‘important’ questions are (itself another issue needing continuous discussion). Scientists typically answer this question with their intuition, but not everyone’s intuition is identical. The other aspect leads us into the monitoring question – should we monitor ecosystems? The irony of this question is that we monitor the weather, and we do so because we do not know the future. So the same justification can be made for ecosystem monitoring which should be as much a part of our science as weather monitoring, human health monitoring, or stock market monitoring are to our daily lives. The next level of discussion, once we agree that monitoring is necessary, is how much money should go into ecological monitoring? The current answer in general seems to be only a little, so we stumble on with too few LTER sites and inadequate knowledge of where we are headed, like cars driving at night with weak headlights. We should do better.

A few of the 186 papers listed in the Web of Science since 2010 that include reference to Konza Prairie data:

Raynor, E.J., Joern, A. & Briggs, J.M. (2014) Bison foraging responds to fire frequency in nutritionally heterogeneous grassland. Ecology, 96, 1586-1597. doi: 10.1890/14-2027.1

Sandercock, B.K., Alfaro-Barrios, M., Casey, A.E., Johnson, T.N. & Mong, T.W. (2015) Effects of grazing and prescribed fire on resource selection and nest survival of upland sandpipers in an experimental landscape. Landscape Ecology, 30, 325-337. doi: 10.1007/s10980-014-0133-9

Ungerer, M.C., Weitekamp, C.A., Joern, A., Towne, G. & Briggs, J.M. (2013) Genetic variation and mating success in managed American plains bison. Journal of Heredity, 104, 182-191. doi: 10.1093/jhered/ess095

Veach, A.M., Dodds, W.K. & Skibbe, A. (2014) Fire and grazing influences on rates of riparian woody plant expansion along grassland streams. PLoS ONE, 9, e106922. doi: 10.1371/journal.pone.0106922

Is Conservation Ecology a Science?

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Now this is certainly a silly question. To be sure conservation ecologists collect much data, use rigorous statistical models, and do their best to achieve the general goal of protecting the Earth’s biodiversity, so clearly what they do must be the foundations of a science. But a look through some of the recent literature could give you second thoughts.

Consider for example – what are the hallmarks of science? Collecting data is one hallmark of science but is clearly not a distinguishing feature. Collecting data on the prices of breakfast cereals in several supermarkets may be useful for some purposes but it would not be confused with science. The newspapers are full of economic statistics about this and that and again no one would confuse that with science. We commonly remark that ‘this is a good scientific way to go about doing things” without thinking too much about what this means.

Back to basics. Science is a way of knowing, of accumulating knowledge to answer questions or problems in an independently verifiable way. Science deals with questions or problems that require some explanation, and the explanation is a hypothesis that needs to be tested. If the test is retrospective, the explanation may be useful for understanding the past. But science at its best is predictive about what will happen in the future, given a set of assumptions. And science always has alternative explanations or hypotheses in case the first one fails. So much everyone knows.

Conservation ecology is akin to history in having a great deal of information about the past but wishing to use that information to inform the future. In a certain sense it has a lot of the problems of history. History, according to many historians (Spinney 2012) is “just one damn thing after another”, so that there can be no science of history. But Turchin disagrees (2003, 2012) and claims that general laws can be recognized in history and general mathematical models developed. He predicts from these historical models that unrest will break out in the USA around 2020 as cycles of violence have broken out in the past every 30-50 years in this country (Spinney 2012). This is a testable prediction in a reasonable time frame.

If we look at the literature of conservation ecology and conservation genetics, we can find many observations of species declines, of geographical range shifts, and many predictions of general deterioration in the Earth’s biota. Virtually all of these predictions are not testable in any realistic time frame. We can extrapolate linear trends in population size to zero but there are so many assumptions that have to be incorporated to make these predictions, few would put money on them. For the most part the concern is rather to do something now to prevent these losses and that is very useful research. But since the major drivers of potential extinctions are habitat loss and climate change, two forces that conservation biologists have no direct control over, it is not at all clear how optimistic or pessimistic we should be when we see negative trends. Are we becoming biological historians?

There are unfortunately too few general ‘laws’ in conservation ecology to make specific predictions about the protection of biodiversity. Every one of the “ecological theory predicts…” statements I have seen in conservation papers refer to theory with so many exceptions that it ought not to be called theory at all. There are some certain predictions – if we eliminate all the habitat a species occupies, it will certainly go extinct. But exactly how much can we get rid of is an open question that there are no general rules about. “Protect genetic diversity” is another general rule of conservation biology, but the consequences of the loss of genetic diversity cannot be estimated except for controlled laboratory populations that bear little relationship to the real world.

The problems of conservation genetics are even more severe. I am amazed that conservation geneticists think they can decide what species are most ‘important’ for future evolution so that we should protect certain clades (Vane-Wright et al. 1991, Redding et al. 2014 and much additional literature). Again this is largely a guess based on so many assumptions that who knows what we would have chosen if we were in the time of the dinosaurs. The overarching problem of conservation biology is the temptation to play God. We should do this, we should do that. Who will be around to pick up the pieces when the assumptions are all wrong? Who should play God?

Redding, D.W., Mazel, F. & Mooers, A.Ø. (2014) Measuring evolutionary isolation for conservation. PLoS ONE, 9, e113490.

Spinney, L. (2012) History as science. Nature, 488, 24-26.

Turchin, P. (2003) Historical dynamics : why states rise and fall. Princeton University Press, Princeton, New Jersey.

Turchin, P. (2012) Dynamics of political instability in the United States, 1780–2010. Journal of Peace Research, 49, 577-591.

Vane-Wright, R.I., Humphries, C.J. & Williams, P.H. (1991) What to protect?—Systematics and the agony of choice. Biological Conservation, 55, 235-254.

Why Do Physical Scientists Run Off with the Budget Pie?

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Take any developed country on Earth and analyse their science budget. Break it down into the amounts governments devote to physical science, biological science, and social science to keep the categories simple. You will find that the physical sciences gather the largest fraction of the budget-for-science pie, the biological sciences much less, and the social sciences even less. We can take Canada as an example. From the data released by the research councils, it is difficult to construct an exact comparison but within the Natural Sciences and Engineering Research Council of Canada the average research grant in Chemistry and Physics is 70% larger than the average in Ecology and Evolution, and this does not include supplementary funding for various infrastructure. By contrast the Social Sciences and Humanities Research Council reports research grants that appear to be approximately one-half those of Ecology and Evolution, on average. It seems clear in science in developed countries that the rank order is physical sciences > biological sciences > social sciences.

We might take two messages from this analysis. If you listen to the news or read the newspapers you will note that most of the problems discussed are social problems. Then you might wonder why social science funding is so low on our funding agenda in science. You might also note that environmental problems are growing in importance and yet funding for environmental research is also at the low end of our spending priority.

The second message you may wish to ask is: why should this be? In particular, why do physical scientists run off with the funding pie while ecologists and environmental scientists scratch through the crumbs? I do not know the answer to this question. I do know that it has been this way for at least the last 50 years, so it is not a recent trend. I can suggest several partial answers to this question.

  1. Physical scientists produce along with engineers the materials for war in splendid guns and aircraft and submarines that our governments believe will keep us safe.
  2. Physical scientists produce economic growth by their research so clearly they should be more important.
  3. Physical sciences produce scientific progress on a time scale of months while ecologists and environmental scientists produce research progress on a time scale of years and decades.
  4. Physical scientists do the research that produce good things like iPhones and computers while ecologists and environmental scientists produce mostly bad news about the deterioration in the earth’s ecosystem services.
  5. Physical scientists and engineers run the government and all the major corporations so they propagate the present system.

Clearly there are specific issues that are lost in this general analysis. Medical science produces progress in diagnosis and treatment as a result of the research of biochemists, molecular biologists, and engineers. Pharmaceutical companies produce compounds to control diseases with the help of molecular biologists and physiologists. So research in these specific areas must be supported well because they affect humans directly. Medical sciences are the recipient of much private money in the quest to avoid illness.

Lost in this are a whole other set of lessons. Why were multi-billions of dollars devoted to the Large Hadron Collider Project which had no practical value at all and has only led to the need for a Very Large Hadron Collider in future to waste even more money? The answer seems to lie somewhere in the interface of three points of view – it may be needed for military purposes, it is a technological marvel, and it is part of physics which is the only science that is important. The same kind of thinking seems to apply to space research which is wildly successful burning up large amounts of money while generating more military competition via satellites and in addition providing good movie images for the taxpayers.

While many people now support efforts on the conservation of biodiversity and the need for action on climate change, the funding is not given to achieve these goals either from public or private sources. One explanation is that these are long-term problems and so are difficult to get excited about when the lifespan of the people in power will not extend long enough to face the consequences of current decision making. Finally, many people are convinced that technological fixes will solve all environmental problems so that the problems environmental scientists worry about are trivial (National Research Council 2015, 2015a). Physics will fix climate change by putting chemicals into the stratosphere, endangered species will be resurrected by DNA, and fossil fuels will never run out. And as a bonus Canada and Scandinavia will be warmer and what is wrong with that?

An important adjunct to this discussion is the question of why economics has risen to the top of the heap along with physical sciences. As such the close triumvirate of physical sciences-engineering-economics seems to run the world. We should keep trying to change that if we have concern for the generations that follow.

 

National Research Council. 2015. Climate Intervention: Carbon Dioxide Removal and Reliable Sequestration. The National Academies Press, Washington, DC. 140 pp. ISBN: 978-0-309-36818-6.

National Research Council. 2015a. Climate Intervention: Reflecting Sunlight to Cool Earth. The National Academies Press, Washington, DC. 234 pp. ISBN: 978-0-309-36821-6.