Category Archives: Charley Krebs’ blogs

Blaming Climate Change for Ecological Changes

The buzz word for all ecological applications for funding and for many submitted papers is climate change. Since the rate of climate change is not something ecologists can control, there are only two reasons to cite climate change as a reason to fund current ecological research. First, since change is continuous in communities and ecosystems, it would be desirable to determine how many of the observed changes might be caused by climate change. Second, it might be desirable to measure the rate of change in ecosystems, correlate these changes to some climate variable, and then use these data as a political and social tool to stimulate politicians to do something about greenhouse gas emissions. The second approach is that taken by climatologists who blame hurricanes and tornadoes on global warming. There is no experimental way to trace any particular hurricane to particular amounts of global warming, so it is easy for critics to say these are just examples of weather variation of which we have measured much over the last 150 years and paleo-ecologists have traced over tens of thousands of years using proxies from tree rings and sediment cores. If we are to use the statistical approach we need a large enough sample to argue that extreme events are becoming more frequent, and that might take 50 years by which time the argument would be made too late to request proper action.

The second approach to prediction in ecology is fraught with problems, as outlined in Berteaux et al. (2006) and Dietze (2017). The first approach has many statistical problems as well in selecting a biologically coherent model that can be tested by in a standard scientific manner. Since there are a very large number of climate variables, the possibility of spurious correlations is excessive, and the only way to avoid these kinds of results is to be predictive and to have a biological causal chain that is testable. Myers (1998) reviewed all the fishery data for predictive models of juvenile recruitment that used environmental variables as predictors and data was subsequently collected and tested with the published model. The vast majority of these aquatic models failed when retested but a few were very successful. The general problem is that model failures or successes might not be published so even this approach can be biased if only a literature survey is undertaken. The take home message from Myers (1998) was that almost none of the recruitment-environment correlations were being used in actual fishery management.

How much would this conclusion about the failure of environmental models in fishery management apply to other areas in ecology? Mouquet et al. (2014) pointed out that predictions could be classified as ‘explanatory’ or ‘anticipatory’ and that “While explanatory predictions are necessarily testable, anticipatory predictions need not be…….In summary, anticipatory predictions differ from explanatory predictions in that they do not aim at testing models and theory. They rely on the assumption that underlying hypotheses are valid while explanatory predictions are based on hypotheses to be tested. Anticipatory predictions are also not necessarily supposed to be true.” (page 1296). If we accept these distinctions, we have (I think) a major problem in that many of the predictive models put forward in the ecological literature are anticipatory, so they would be of little use to a natural resource manager who requires an explanatory model.

If we ignore this problem with anticipatory predictions, we can concentrate on explanatory predictions that are useful to managers. One major set of explanatory predictions in ecology are those associated with range changes in relation to climate change. Cahill et al. (2014) examined the conventional hypothesis that warm-edge range limits are set by biotic interactions rather than abiotic interactions. Contrary to expectations, they found in 125 studies that abiotic factors were more frequently supported as setting warm-edge range limits. Clearly a major paradigm about warm-edge range limits is of limited utility.

Explanatory predictions are not always explicit. Mauck et al. (2018) for example developed a climate model to predict reproductive success in Leach’s storm petrel on an island off New Brunswick in eastern Canada. From 56 years of hatching success they concluded that annual global mean temperature during the spring breeding season was the single most important predictor of breeding success. They considered only a few measures of temperature as predictor variables and found that a quadratic form of annual global mean temperature was the best variable to describe the changes in breeding success. The paper speculates about how global or regional mean temperature could possibly be an ecological predictor of breeding success, and no mechanisms are specified. The actual data on breeding success are not provided in the paper, even as a temporal plot. Since global temperatures were rising steadily from 1955 to 2010, any temporal trend in any population parameter that is rising would correlate with temperature records. The critical quadratic relationship in their analysis suggests that a tipping point was reached in 1988 when hatching success began to decline. Whether or not this is a biologically correct explanatory model can be determined by additional data gathered in future years. But it would be more useful to find out what the exact ecological mechanisms are.

If the ecological world is going to hell in a handbasket, and temperatures however measured are going up, we can certainly construct a plethora of models to describe the collapse of many species and the rise of others. But this is hardly progress and would appear to be anticipatory predictions of little use to advancing ecological science, as Guthery et al. (2005) pointed out long ago. Someone ought to review and evaluate the utility of AIC methods as they are currently being used in ecological and conservation science for predictions.

Berteaux, D., Humphries, M.M., Krebs, C.J., Lima, M., McAdam, A.G., Pettorelli, N., Reale, D., Saitoh, T., Tkadlec, E., Weladji, R.B., and Stenseth, N.C. (2006). Constraints to projecting the effects of climate change on mammals. Climate Research 32, 151-158. doi: 10.3354/cr032151.

Cahill, A.E., Aiello-Lammens, M.E., Fisher-Reid, M.C., Hua, X., and Karanewsky, C.J. (2014). Causes of warm-edge range limits: systematic review, proximate factors and implications for climate change. Journal of Biogeography 41, 429-442. doi: 10.1111/jbi.12231.

Dietze, M.C. (2017). Prediction in ecology: a first-principles framework. Ecological Applications 27, 2048-2060. doi: 10.1002/eap.1589.

Guthery, F.S., Brennan, L.A., Peterson, M.J., and Lusk, J.J. (2005). Information theory in wildlife science: Critique and viewpoint. Journal of Wildlife Management 69, 457-465. doi: 10.1890/04-0645.

Mauck, R.A., Dearborn, D.C., and Huntington, C.E. (2018). Annual global mean temperature explains reproductive success in a marine vertebrate from 1955 to 2010. Global Change Biology 24, 1599-1613. doi: 10.1111/gcb.13982.

Mouquet, N., Lagadeuc, Y., Devictor, V., Doyen, L., and Duputie, A. (2015). Predictive ecology in a changing world. Journal of Applied Ecology 52, 1293-1310. doi: 10.1111/1365-2664.12482.

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

 

Ecology as a Contingent Science

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

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

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

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

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

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

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

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

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

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

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

Martin, J-L., Maris, V., and Simberloff, D.S. (2016). The need to respect nature and its limits challenges society and conservation science. Proceedings of the National Academy of Sciences 113, 6105-6112. doi: 10.1073/pnas.1525003113.

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

On A Global Agenda for Ecology

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 Culling Overabundant Wildlife

Ecologists have written much about the culling of wildlife from an ecological and conservation perspective (Caughley 1981, Jewell et al. 1981, Bradford and Hobbs 2008, Hampton and Forsyth 2016). The recommendations for culling as a method for reducing overabundant wildlife populations are typically scientifically well established and sensitive to animal welfare. The populations chosen for culling are classified as ‘overabundant’. But overabundant is a human-defined concept, and thus requires some form of social license to agree about what species, in which conditions, should be classified as ‘overabundant’. The problem of overabundance usually arises when humans make changes that permit a species to become so numerous locally that it is having an adverse effect on its food supply, its competitors, or the integrity of the ecosystem it occupies. Once overabundance is recognized, the management issue is to determine which methods should be used to reduce abundance to a suitable level. Culling is only one option for removing wildlife, and animals may be captured and moved elsewhere if that is possible or sterilized to prevent reproduction and further increase (Liu et al. 2012, Massei and Cowan 2014).

All these policy issues are subject to open public debate and these debates are often heated because of different belief systems. Animal rights advocates may push the assumption that we humans have no rights to kill any wildlife at all. News media often concentrate on the most stringent views on controlling populations that are overabundant, and public discussion becomes impossible. Two aspects need to be noted that are often lost in any discussion. First is the cost of alternatives in dollars and cents. As an example, most ecologists would agree that wild horses are overabundant on open range in western United States (Davies et al. 2014, Rutberg et al. 2017) but the question is what to do about this. Costs to reduce horse populations by capturing horses and penning them and feeding them are astronomical (the current situation in western USA, estimated at $25,000 per animal) but this method of control could be done if society wishes to spend money to achieve this goal. Culling would be much cheaper, but the killing of large animals is anathema to many people who speak loudly to politicians. Fertility control methods are improving with time and may be more acceptable socially, but costs are high and results in population reduction can be slow in coming (Hobbs and Hinds 2018). Models are essential to sort out many of these issues, whether it be the projected costs of various options (including doing nothing), the expected population trajectory, or the consequences for other species in the ecosystem.

The bottom line is that if overabundant wildlife populations are not reduced by some means, the result must be death by starvation or disease coupled with extensive damage to other species in these ecosystems. This type of “Plan B” is the second aspect not often considered in discussions of policies on overabundant species. In the present political scene in North America opposition to culling overabundant wildlife is strong, coherent discussion is rarely possible, and Plan B problems are rarely heard. Most overabundant wildlife result from human actions in changing the vegetation, introducing new species, and reducing and fragmenting wildlife habitats. Wishing the problems will go away without doing anything is not a feasible course of action.

These kinds of problems in wildlife management are soluble in an objective manner with careful planning of research and management actions (Hone et al. 2017). Ecologists have a moral duty to present all scientific sides of the management of overabundant species, and to bring evidence into the resulting social and political discussions of management issues. It is not an easy job.

Bradford, J.B., and N.T. Hobbs. 2008. Regulating overabundant ungulate populations: An example for elk in Rocky Mountain National Park, Colorado. Journal of Environmental Management 86:520-528. doi: 10.1016/j.jenvman.2006.12.005

Caughley, G. 1981. Overpopulation. Pages 7-19 in P.A. Jewell S. Holt, and D. Hart, editors. Problems in Management of Locally Abundant Wild Mammals. Academic Press, New York. ISBN: 978-0-12-385280-9

Davies, K. W., Collins, G. & Boyd, C. S. (2014) Effects of feral free-roaming horses on semi-arid rangeland ecosystems: an example from the sagebrush steppe. Ecosphere, 5, 127. doi: 10.1890/ES14-00171.1

Hampton, J. O., and D. M. Forsyth. 2016. An assessment of animal welfare for the culling of peri-urban kangaroos. Wildlife Research 43:261-266. doi: 10.1071/WR16023

Hobbs, R.J. and Hinds, L.A. (2018). Could current fertility control methods be effective for landscape-scale management of populations of wild horses (Equus caballus) in Australia? Wildlife Research 45, 195-207. doi: 10.1071/WR17136.

Hone, J., Drake, V.A. & Krebs, C.J. (2017) The effort–outcomes relationship in applied ecology: Evaluation and implications BioScience, 67, 845-852. doi: 10.1093/biosci/bix091

Jewell, P. A., Holt, S. & Hart, D. (1982) Problems in Management of Locally Abundant Wild Mammals. Academic Press, New York. 360 pp. ISBN: 978-0-12-385280-9

Liu, M., Qu, J., Yang, M., Wang, Z., Wang, Y., Zhang, Y. & Zhang, Z. (2012) Effects of quinestrol and levonorgestrel on populations of plateau pikas, Ochotona curzoniae, in the Qinghai-Tibetan Plateau. Pest Management Science, 68, 592-601. doi: 10.1002/ps.2302

Massei, G. & Cowan, D. (2014) Fertility control to mitigate human–wildlife conflicts: a review. Wildlife Research, 41, 1-21. doi: 10.1071/WR13141

Rutberg, A., Grams, K., Turner, J.W. & Hopkins, H. (2017) Contraceptive efficacy of priming and boosting doses of controlled-release PZP in wild horses. Wildlife Research, 44, 174-181. doi: 10.1071/WR16123

On Questionable Research Practices

Ecologists and evolutionary biologists are tarred and feathered along with many scientists who are guilty of questionable research practices. So says this article in “The Conservation” on the web:
https://theconversation.com/our-survey-found-questionable-research-practices-by-ecologists-and-biologists-heres-what-that-means-94421?utm_source=twitter&utm_medium=twitterbutton

Read this article if you have time but here is the essence of what they state:

“Cherry picking or hiding results, excluding data to meet statistical thresholds and presenting unexpected findings as though they were predicted all along – these are just some of the “questionable research practices” implicated in the replication crisis psychology and medicine have faced over the last half a decade or so.

“We recently surveyed more than 800 ecologists and evolutionary biologists and found high rates of many of these practices. We believe this to be first documentation of these behaviours in these fields of science.

“Our pre-print results have certain shock value, and their release attracted a lot of attention on social media.

  • 64% of surveyed researchers reported they had at least once failed to report results because they were not statistically significant (cherry picking)
  • 42% had collected more data after inspecting whether results were statistically significant (a form of “p hacking”)
  • 51% reported an unexpected finding as though it had been hypothesised from the start (known as “HARKing”, or Hypothesising After Results are Known).”

It is worth looking at these claims a bit more analytically. First, the fact that more than 800 ecologists and evolutionary biologists were surveyed tells you nothing about the precision of these results unless you can be convinced this is a random sample. Most surveys are non-random and yet are reported as though they are a random, reliable sample.

Failing to report results is common in science for a variety of reasons that have nothing to do with questionable research practices. Many graduate theses contain results that are never published. Does this mean their data are being hidden? Many results are not reported because they did not find an expected result. This sounds awful until you realize that journals often turn down papers because they are not exciting enough, even though the results are completely reliable. Other results are not reported because the investigator realized once the study is complete that it was not carried on long enough, and the money has run out to do more research. One would have to have considerable detail about each study to know whether or not these 64% of researchers were “cherry picking”.

Alas the next problem is more serious. The 42% who are accused of “p-hacking” were possibly just using sequential sampling or using a pilot study to get the statistical parameters to conduct a power analysis. Any study which uses replication in time, a highly desirable attribute of an ecological study, would be vilified by this rule. This complaint echos the statistical advice not to use p-values at all (Ioannidis 2005, Bruns and Ioannidis 2016) and refers back to complaints about inappropriate uses of statistical inference (Armhein et al. 2017, Forstmeier et al. 2017). The appropriate solution to this problem is to have a defined experimental design with specified hypotheses and predictions rather than an open ended observational study.

The third problem about unexpected findings hits at an important aspect of science, the uncovering of interesting and important new results. It is an important point and was warned about long ago by Medewar (1963) and emphasized recently by Forstmeier et al. (2017). The general solution should be that novel results in science must be considered tentative until they can be replicated, so that science becomes a self-correcting process. But the temptation to emphasize a new result is hard to restrain in the era of difficult job searches and media attention to novelty. Perhaps the message is that you should read any “unexpected findings” in Science and Nature with a degree of skepticism.

The cited article published in “The Conversation” goes on to discuss some possible interpretations of what these survey results mean. And the authors lean over backwards to indicate that these survey results do not mean that we should not trust the conclusions of science, which unfortunately is exactly what some aspects of the public media have emphasized. Distrust of science can be a justification for rejecting climate change data and rejecting the value of immunizations against diseases. In an era of declining trust in science, these kinds of trivial surveys have shock value but are of little use to scientists trying to sort out the details about how ecological and evolutionary systems operate.

A significant source of these concerns flows from the literature that focuses on medical fads and ‘breakthroughs’ that are announced every day by the media searching for ‘news’ (e.g. “eat butter”, “do not eat butter”). The result is almost a comical model of how good scientists really operate. An essential assumption of science is that scientific results are not written in stone but are always subject to additional testing and modification or rejection. But one result is that we get a parody of science that says “you can’t trust anything you read” (e.g. Ashcroft 2017). Perhaps we just need to repeat to ourselves to be critical, that good science is evidence-based, and then remember George Bernard Shaw’s comment:

Success does not consist in never making mistakes but in never making the same one a second time.

Amrhein, V., Korner-Nievergelt, F., and Roth, T. 2017. The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research. PeerJ  5: e3544. doi: 10.7717/peerj.3544.

Ashcroft, A. 2017. The politics of research-Or why you can’t trust anything you read, including this article! Psychotherapy and Politics International 15(3): e1425. doi: 10.1002/ppi.1425.

Bruns, S.B., and Ioannidis, J.P.A. 2016. p-Curve and p-Hacking in observational research. PLoS ONE 11(2): e0149144. doi: 10.1371/journal.pone.0149144.

Forstmeier, W., Wagenmakers, E.-J., and Parker, T.H. 2017. Detecting and avoiding likely false-positive findings – a practical guide. Biological Reviews 92(4): 1941-1968. doi: 10.1111/brv.12315.

Ioannidis, J.P.A. 2005. Why most published research findings are false. PLOS Medicine 2(8): e124. doi: 10.1371/journal.pmed.0020124.

Medawar, P.B. 1963. Is the scientific paper a fraud? Pp. 228-233 in The Threat and the Glory. Edited by P.B. Medawar. Harper Collins, New York. pp. 228-233. ISBN 978-0-06-039112-6

On the Loss of Large Mammals

The loss of large mammals and birds in the Pleistocene was highlighted many years ago (Martin and Wright 1967, Grayson 1977, Guthrie 1984 and many other papers). Hypotheses about why these extinctions occurred were flying left and right for many years with no clear consensus (e.g. Choquenot and Bowman 1998). The museums of the world are filled with mastodons, moas, sabre-tooth tigers and many other skeletons of large mammals and birds long extinct. The topic has come up again in a discussion of these extinctions and a prognosis of future losses (Smith et al. 2018). I do not want to question the analysis in Smith et al. (2018) but I want to concentrate on this one quotation that has captured the essence of this paper in the media:

“Because megafauna have a disproportionate influence on ecosystem structure and function, past and present body size downgrading is reshaping Earth’s biosphere.”
(pg. 310).

What is the evidence for this very strong statement? The first thought that comes to mind is from my botanical colleagues who keep reminding me that plants make of 99% of the biomass of the Earth’s ecosystems. So, if this statement is correct, it must mean that large mammals have a very strong effect on plant ecosystem structure and function. And it must also imply that large mammals are virtually immune to predators, so no trophic cascade can occur to prevent plant overgrazing.

I appreciate that it is very difficult to test such a statement since evolution has been going on for a long time before humans arrived, and so there must have been a lot of other factors causing ecosystem changes in those early years. Humans have a disproportionate love for biodiversity that is larger than us. So, we revel in elephants, tigers, bears, and whales, while at the same time we pay little attention to the insects, small mammals, most fish, and plankton. Because of this size bias, we are greatly concerned with the conservation of large animals, as we should be, but much less concerned about what is happening to the small chaps.

What is the evidence that large mammals and birds have a disproportionate influence on ecosystem structure and function? In my experience, I would say there is very little evidence for strong ecosystem effects from the collapse of the megafauna. DeMaster et al. (2006) evaluated a proposed explanation for ecosystem collapse caused by whaling in the North Pacific Ocean and concluded that the evidence was weak for a sequential megafauna collapse caused by commercial whaling. Trites et al. (2007) and Wade et al. (2007) supported this conclusion. Citing paleo-ecological data for Australia, Johnson (2010) and Rule et al. (2012) argued in another evaluation of ecosystem changes that the human-driven extinction of the megafauna in Australia resulted in large changes in plant communities, potentially confounded by climate change and increases in fire frequency about 40K years ago. If we accept these controversies, we are left with trying to decide if the current losses of large mammals are of similar strength to those assigned to the Pleistocene megafauna, as suggested by Smith et al. (2018).

If we define ecosystem function as primary productivity and ecosystem structure as species diversity, I cannot think of a single case in recent studies where this idea has been clearly tested and supported. Perhaps this simply reflects my biased career working in arctic and subarctic ecosystems in which the vast majority of the energy flow in the system rotates through the smaller species rather than the larger ones. Take the Great Plains of North America with and without the bison herds. What aspect of ecosystem function has changed because of their loss? It is impossible to say because of human intervention in the fire cycle and agricultural pre-emption of much of the landscape. It is certainly correct that overgrazing impacts can be severe in human-managed landscapes with overstocking of cattle and sheep, and that is a tragedy brought on by economics, predator elimination programs, and human land use decisions. All the changes we can describe with paleo-ecological methods have potential explanations that are highly confounded.

I think the challenge is this: to demonstrate that the loss of large mammals at the present time creates a large change in ecosystem structure and function with data on energy flow and species diversity. The only place I can see it possible to do this experimentally today would be in arctic Canada where, at least in some areas, caribou come and go in large numbers and with relatively little human impact. I doubt that you could detect any large effect in this hypothetical experiment. It is the little chaps that matter to ecosystem function, not the big chaps that we all love so much. And I would worry if you could do this experiment, the argument would be that it is a special case of extreme environments not relevant to Africa or Australia.

No one should want the large mammals and birds to disappear, but the question of how this might play out in the coming 200 years in relation to ecosystem function requires more analysis. And unlike the current political inactivity over the looming crisis in climate change, we conservation biologists should certainly try to prevent the loss of megafauna.

Choquenot, D., and Bowman, D.M.J.S. 1998. Marsupial megafauna, Aborigines and the overkill hypothesis: application of predator-prey models to the question of Pleistocene extinction in Australia. Global Ecology and Biogeography Letters 7: 167-180.

DeMaster, D.P., Trites, A.W., Clapham, P., Mizroch, S., Wade, P., Small, R.J., and Hoef, J.V. 2006. The sequential megafaunal collapse hypothesis: testing with existing data. Progress in Oceanography 68(2-4): 329-342. doi:10.1016/j.pocean.2006.02.007

Grayson, D.K. 1977. Pleistocene avifaunas and the Overkill Hypothesis. Science 195: 691-693.

Guthrie, R.D. 1984. Mosaics, allelochemics and nutrients: An ecological theory of late Pleistocene megafaunal extinctions. In: Quaternary Extinctions: A Prehistoric Revolution ed by P.S. Martin and R.G. Klein. University of Arizona Press Tucson.

Johnson, C.N. 2010. Ecological consequences of Late Quaternary extinctions of megafauna. Proceeding of the Royal Society of London, Series B 276(1667): 2509-2519. doi: 10.1098/rspb.2008.1921.

Martin, P.S., and Wright, H.E. (eds). 1967. Pleistocene Extinctions; The Search for a Cause. Yale University Press, New Haven, Connecticut. 453 pp.

Rule, S., Brook, B.W., Haberle, S.G., Turney, C.S.M., Kershaw, A.P., and Johnson, C.N. 2012. The aftermath of megafaunal extinction: ecosystem transformation in Pleistocene Australia. Science 335(6075): 1483-1486. doi: 10.1126/science.1214261.

Smith, F.A., Elliott Smith, R.E., Lyons, S.K., and Payne, J.L. 2018. Body size downgrading of mammals over the late Quaternary. Science 360(6386): 310-313. doi: 10.1126/science.aao5987.

Trites, A.W., Deecke, V.B., Gregr, E.J., Ford, J.K.B., and Olesiuk, P.F. 2007. Killer whales, whaling, and sequential megafaunal collapse in the North Pacific: a comparative analysis of the dynamics of marine mammals in Alaska and British Columbia following commercial whaling. Marine Mammal Science 23(4): 751-765. doi: 10.1111/j.1748-7692.2006.00076.x.

Wade, P.R., et al. 2007. Killer whales and marine mammal trends in the North Pacific – a re-examination of evidence for sequential megafaunal collapse and the prey-switching hypothesis. Marine Mammal Science 23(4): 766-802. doi: 10.1111/j.1748-7692.2006.00093.x.

On Detecting Rare Species with Camera Trapping

If you are a conservation biologist and you wish to save all or as many species as possible, your first problem is detectability. Does the species of concern live in this habitat? If it is present how many are there, and is their abundance changing from year to year? These are fundamental questions in conservation science and there is accordingly a very large literature on how to answer these simple questions for animals in different taxonomic groups. I want to deal briefly here with rare species in which the issue of detectability is most critical.

There is a large array of papers on detection methods in the conservation literature (e.g. Brodie et al. 2018; Crates et al. 2017; Steenweg et al. 2016; Clement et al. 2016, Trolliet et al. 2014). Detection methods vary from live trapping marked individuals, visual sighting of unmarked individuals, camera photos of marked or unmarked individuals, sign data such as tracks or scats in snow, mud or sand, DNA fingerprinting, and many clever natural-history- derived methods to measure detection. These methods are well developed for common animals (Williams et al. 2002).

Rare species are the first problem faced by all these detection methods. Rare species range from those virtually impossible to detect with current technology to those that turn up infrequently in the designated detection device. The conservation challenge of rare species is difficult if they are hard to detect and difficult to study so that we have few natural history parameters to guide conservation actions. For these we can only set aside what we think are suitable areas and conserve them.

The technology of monitoring rare species that can be detected at some reasonable level has greatly improved with the advent of passive-infrared-cameras that can be deployed 24-7 to capture images of whomever walks or swims by. But this technology raises a whole set of methodological issues that must be addressed. The first and most obvious one is the skill of the observer both in setting up the cameras and in looking at the photos to identify correctly the species present. The second and more difficult question is what to count as a detection or ‘hit’. If your question is simply ‘occupancy’ seeing one photograph in the time period of the study provides a + for occupancy. But many ecologists wish to connect the dots from occupancy scores to abundance so that some index of population numbers can arise from these camera data. To make this leap of faith relies heavily on the experimental design of the camera placements, the number of cameras, the make of the cameras (Meek et al. 2014), and the exact placement of cameras on trees or stakes to cover a specific area of habitat. Clearly if cameras are placed too close to one another, the photos from the different cameras are not independent, as most of the models of occupancy assume (Brodie et al. 2018). If bait is used with the cameras the situation becomes even more complex because some species may be attracted while others are repelled by the bait. In general camera detections or ‘hits’ for a particular species are a measure of activity rather than a direct measure of abundance, and so often the assumption is made that activity = abundance, which must be justified. In the extreme case in which a density estimate is needed from camera data, the problem of ‘edge effects’ of the sampled area must be considered just as it does with grid trapping (e.g Thornton and Pekins 2015). New approaches for estimating density from camera data appear almost daily and must be evaluated for accuracy (Nakashima et al. 2018).

We are now in the exponential phase of camera trapping with cameras put up in all sorts of spatial designs for different lengths of time with the hope that someone will have time to look at the photos and some clever statistician can factor out all the potential biases and non-independence of the resulting data. So in a nutshell my simple advice is to use cameras to gather wildlife information but think carefully about what exactly you wish to achieve: occupancy?, an index of abundance?, actual numerical abundance? population density? Or simply beautiful photos of interesting animals? And in the end you may be envious of plant ecologists whose plants do not walk away when you census them.

 

Brodie, J.F., et al. (2018). Models for assessing local-scale co-abundance of animal species while accounting for differential detectability and varied responses to the environment. Biotropica 50, 5-15. doi: 10.1111/btp.12500.

Clement, M. J., J. E. Hines, J. D. Nichols, K. L. Pardieck, and D. J. Ziolkowski. 2016. Estimating indices of range shifts in birds using dynamic models when detection is imperfect. Global Change Biology 22:3273-3285. doi: 10.1111/gcb.13283

Crates, R., L. Rayner, D. Stojanovic, M. Webb, and R. Heinsohn. 2017. Undetected Allee effects in Australia’s threatened birds: implications for conservation. Emu 117:207-221. doi: 10.1080/01584197.2017.1333392

Meek, P.D., et al. (2014). Camera traps can be heard and seen by animals. PLoS ONE 9, e110832. doi: 10.1371/journal.pone.0110832.

Nakashima, Y., Fukasawa, K., and Samejima, H. (2018). Estimating animal density without individual recognition using information derivable exclusively from camera traps. Journal of Applied Ecology 55, 735-744. doi: 10.1111/1365-2664.13059.

Smith, D.H.V. and Weston, K.A. (2017). Capturing the cryptic: a comparison of detection methods for stoats (Mustela erminea) in alpine habitats. Wildlife Research 44, 418-426. doi: 10.1071/WR16159.

Steenweg, R., et al. (2016). Camera-based occupancy monitoring at large scales: Power to detect trends in grizzly bears across the Canadian Rockies. Biological Conservation 201:192-200. doi: 10.1016/j.biocon.2016.06.020

Thornton, D.H. and Pekins, C.E. (2015). Spatially explicit capture-recapture analysis of bobcat (Lynx rufus) density: implications for mesocarnivore monitoring Wildlife Research 42, 394-404. doi: 10.1071/WR15092.

Trolliet, F., et al. (2014). Use of camera traps for wildlife studies. A review. Biotechnology, Agronomy, Society and Environment (BASE) 18, 446-454.

Williams, B.K., Nichols, J.D., and Conroy, M.J. (2002) ‘Analysis and Management of Animal Populations.’ (Academic Press: New York.). 817 pp.

 

Seven Prescriptive Principles for Ecologists

After three of us put together a paper to list the principles of applied ecology (Hone, Drake, and Krebs 2015), I thought that perhaps we might have an additional set of general behavioural principles for ecologists. We might think of using these seven principles as a broad template for the work we do in science.

  1. Do good science and avoid opinions that are not based on facts and reliable studies. Do not cite bad science even if it is published in Science.
  2. Appreciate and support your colleagues.
  3. Because you disagree with another scientist it is not acceptable to be rude, and it is preferable to decide what experiment can solve the disagreement.
  4. Adulterating your data to remove values that do not fit your hypothesis is not acceptable.
  5. Alternative facts have no place in science. A Professor should not profess nonsense. Nonsense should be the sole prerogative of politicians.
  6. Help your fellow scientists whenever possible, and do not envy those whose papers get published in Science or Nature. Your contribution to science cannot be measured by your h-index.
  7. We have only one Earth. We should give up dreaming about moving to Mars and take care of our home here.

Many of these principles can be grouped under the umbrella of ‘scientific integrity’, and there is an extensive discussion in the literature about integrity (Edwards and Roy 2017, Horbach and Halffman 2017). Edwards and Roy (2017, pg. 53) in a (dis-) service to aspiring young academics quote a method for increasing an individual’s h-index without committing outright fraud. Horbach and Halffman (2017) point out that scientists and policymakers adopt different approaches to research integrity. Scientists discuss ‘integrity’ with a positive view of ‘good scientific practice’ that has an ethical focus, while policy people discuss ‘integrity’ with a negative view of ‘misconduct’ that has a legal focus.

The immediate problem with scientific integrity in the USA involves the current President and his preoccupation with defining ‘alternative facts’ (Goldman et al. 2017). But the problem is also a more general one, as illustrated by the long discussion carried out by conservation biologists who asked whether or not a scientist can also be an advocate for a particular policy (Garrard et al. 2016, Carroll et al. 2017).

The bottom line for ecologists and environmental scientists is important, and a serious discussion of scientific integrity should be part of every graduate seminar class. Scientific journals should become more open to challenges to papers that use faulty data, and maintaining high standards must remain number one on the list for all of us.

Carroll, C., Hartl, B., Goldman, G.T., Rohlf, D.J., and Treves, A. 2017. Defending the scientific integrity of conservation-policy processes. Conservation Biology 31(5): 967-975. doi: 10.1111/cobi.12958.

Edwards, M.A., and Roy, S. 2017. Academic research in the 21st century: Maintaining scientific integrity in a climate of perverse incentives and hypercompetition. Environmental Engineering Science 34(1): 51-61. doi: 10.1089/ees.2016.0223.correx.

Garrard, G.E., Fidler, F., Wintle, B.C., Chee, Y.E., and Bekessy, S.A. 2016. Beyond advocacy: Making space for conservation scientists in public debate. Conservation Letters 9(3): 208-212. doi: 10.1111/conl.12193.

Goldman, G.T., Berman, E., Halpern, M., Johnson, C. & Kothari, Y. (2017) Ensuring scientific integrity in the Age of Trump. Science, 355, 696-698. doi: 10.1126/science.aam5733

Hone, J., A. Drake, and C. J. Krebs. 2015. Prescriptive and empirical principles of applied ecology. Environmental Reviews 23:170-176. doi: 10.1139/er-2014-0076

Horbach, S.P.J.M., and Halffman, W. 2017. Promoting virtue or punishing fraud: Mapping contrasts in the language of ‘scientific integrity’. Science and Engineering Ethics 23(6): 1461-1485. doi: 10.1007/s11948-016-9858-y.

 

A Need for Champions

The World has many champions for the Olympics, economists have champions for free trade, physicists have champions for the Hadron Collider, astronomists for space telescopes, but who are the champions for the environment?  We have many environmental scientists who try to focus the public’s attention on endangered species, the state of agriculture, pollution of air and water, and the sustainability of marine fisheries, but they are too much ignored. Why do we have this puzzle that the health of the world we all live in is too often ignored when governments release their budgets?

There are several answers to this simple question. First of all, the ‘jobs and growth’ paradigm rules, and exponential growth is the ordained natural order. The complaint we then get is that environmental scientists too often suggest that studies are needed, and the results of these studies produce recommendations that will impede jobs and growth. Environmental science not only does not produce more dollar bills but in fact diverts dollars from other more preferred activities that increase the GDP.

Another important reason is that environmental problems are slow-moving and long-term, and our human evolutionary history shows that we are poor at dealing with such problems. We can recognize and adapt quickly to short-term problems like floods, epidemics, and famines but we cannot see the inexorable rise in sea levels of 3 mm per year. We need therefore champions of the environment with the charisma to attract the world’s attention to slow-moving, long-term problems. We have some of these champions already – James Hansen, David Suzuki, Tim Flannery, Paul Ehrlich, Naomi Klein – and they are doing an excellent job of producing scientific discussions on our major environmental problems, information that is unfortunately still largely ignored on budget day. There is progress, but it is slow, and in particular young people are more aware of environmental issues than are those of the older generation.

What can we do to change the existing dominant paradigm into a sustainable ecological paradigm? Begon (2017) argues that ecology is both a science and a crisis discipline, and his concern is that at the present time ecological ideas about our current crises are not taken seriously by the general public and policy leaders. One way to change this, Begon argues, is to reduce our reliance on specific and often complicated evidence and convert to sound bites, slogans that capture the emotions of the public rather than their intellect. So, I suggest a challenge can be issued to ecology classes across the world to spend some time brainstorming on suitable slogans, short appealing phrases that encapsulate what ecologists understand about our current problems. Here are three suggestions: “We cannot eat coal and oil – support agriculture”, “Think long-term, become a mental eco-geologist”, and “The ocean is not a garbage can”. Such capsules are not for all occasions, and we must maintain our commitment to evidence-based-ecology of course (as Saul et al. 2017 noted). That this kind of communication to the general public is not simple is well illustrated in the paper by Casado-Aranda et al. (2017) who used an MRI to study brain waves in people exposed to ecological information. They found that people’s attitudes to ecological messages were much more positive when the information was conveyed in future-framed messages delivered by a person with a younger voice. So perhaps the bottom line is to stop older ecologists from talking so much, avoid talking about the past, and look in the future for slogans to encourage an ecological world view.

Begon, M. 2017. Winning public arguments as ecologists: Time for a New Doctrine? Trends in Ecology & Evolution 32:394-396. doi: 10.1016/j.tree.2017.03.009

Casado-Aranda, L.-A., M. Martínez-Fiestas, and J. Sánchez-Fernández. 2018. Neural effects of environmental advertising: An fMRI analysis of voice age and temporal framing. Journal of Environmental Management 206:664-675. doi: 10.1016/j.jenvman.2017.10.006

Saul, W.-C., R.T. Shackleton, and F.A. Yannelli. 2017. Ecologists winning arguments: Ends don’t justify the means. A response to Begon. Trends in Ecology & Evolution 32:722-723. doi: 10.1016/j.tree.2017.08.005

 

A Few Rules for Giving a Lecture

I’ve discussed some of the rules for graphics in publications and preparing posters before but I feel it’s time for a more general discussion of lecturing for scientists. All of us have suffered through at least one poor lecture at scientific meetings and some of us many more. If you are a scientist or educator and must give a short talk or a long lecture, you should not panic since there are just a few rules that can help in communication and reduce potential suffering for you and the audience.

First, let the audience know what precisely you will be discussing in your talk – what is the problem and what you are going to present about it. The opening 2 minutes of your talk is when you can lose two-thirds of your audience. If you are a politician, this may be what you wish to happen, but if you are a scientist do not go there. You do not need to begin by stating the obvious – we all know that the earth is round and biodiversity is under threat – but dive into the details of the particular problem you are going to resolve.

Second, if you are showing powerpoints, follow a few simple rules or again you will lose your audience. Do not put more than a few dot points on a slide, or more than 1 or at most 2 graphs or maps. You must not spend more than 1-2 minutes on each slide or those of us with a sleep deficit will have a power nap instead of listening. Use writing in large letters only so they can be read from the back of the room.

Thirdly, do not use acronyms anywhere. Most of us do not know that DAE means ‘demographic Allee effect’ or that RR means ‘log response ratio’ so if your slides contain GDD or DOC or HBL or ODE you may be losing your audience. In most cases it is possible to write out the meaning of these acronyms without crowding the slide.

Finally, sum up at the end of your talk what you have achieved and what more might be required to completely answer your opening question or problem. The audience will typically take home one or two points you have raised in your talk. Do not expect miracles.

There is an enormous literature on powerpoints and lecturing, much of it more relevant to medical education than to biology. I have put together 8 specific rules for powerpoints and I list these here:

  1. Never use a dark background for your slides. The reason is that in rooms that have too much light, the audience will be unable to read white printing on a dark background. It is best to use black printing on a white or pastel background.
  2. Use at least 28 point font on every slide. If you think this is too large a font, project your lecture and go back 10 meters in a not-too-dark room, and see if you can read what you have written.
  3. Never have more than one graph on a slide. It is impossible to digest 4 or 8 graphs on one slide, and the audience can never read the labels on the axes.
  4. If you use colour on your slide for different lines or points, make the colours strong and check that you can distinguish them from 10 meters.
  5. Never use a table on a slide with more than 4 columns and 4 rows. No one can read most tables used in most talks because the font size is typically too small.
  6. Allow at least one minute to talk about what is the message on each slide. If you are giving a 15-minute talk, you should have no more than 12 slides.
  7. Do not use a photo as a background for a slide. Use photos as photos to make a particular point, and text as text. Do not in general put several photos on one slide.
  8. Do not use animation in your powerpoints unless you have already gotten an Academy Award for your work. If you need to use a short video, imbed it properly and test that it really works and is clear.

I think these two papers make additional points that are useful in developing lectures. Good luck and an early thank you from your audiences.

Blome, C., H. Sondermann, and M. Augustin. 2017. Accepted standards on how to give a Medical Research Presentation: a systematic review of expert opinion papers. GMS Journal for Medical Education 34: doc11. doi: 10.3205/zma001088

Harolds, J. A. 2012. Tips for giving a memorable presentation, Part IV: Using and composing PowerPoint slides. Clinical Nuclear Medicine 37:977-980. doi: 10.1097/RLU.0b013e3182614219