Category Archives: General Ecology

On Indices of Population Abundance

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A discussion with Murray Efford last week stimulated me to raise again this issue of using indices to measure population changes. One could argue that this issue has already been fully aired by Anderson (2003) and Engemann (2003) and I discussed it briefly in a blog about 2 years ago. The general agreement appears to be that mark-recapture estimation of population size is highly desirable if the capture procedure is clearly understood in relation to the assumption of the model of estimation. McKelvey and Pearson (2001) made this point with some elegant simulations. The best procedure then, if one wishes to replace mark-recapture methods with some index of abundance (track counts, songs, fecal pellets, etc.), is to calibrate the index with absolute abundance information of some type and show that the index and absolute abundance are very highly correlated. This calibration is difficult because there are few natural populations on which we know absolute abundance with high accuracy. We are left hanging with no clear path forward, particularly for monitoring programs that have little time or money to do extensive counting of any one species.

McKelvey and Pearson (2001) laid out a good guide for the use of indices in small mammal trapping, and showed that for many sampling programs the use of the number of unique individuals caught in a sampling session was a good index of population abundance, even though it is negatively biased. The key variable in all these discussions of mark-recapture models is the probability of capture of an individual animal living on the trapping area per session. Many years ago Leslie et al. (1953) considered this issue and the practical result was the recommendation that all subsequent work with small rodents should aim for a maximum probability of capture of individuals. The simplest way to do this was with highly efficient traps and large numbers of traps (single catch traps) so that there was always an excess of traps available for the population being censused. Krebs and Boonstra (1984) presented an analysis of trappability for several Microtus populations in which these recommendations were typically followed (Longworth traps in excess), and they found that the average per session detection probability ranged from about 0.6 to 0.9 for the four Microtus species studied. In all these studies live traps were present year round in the field, locked open when not in use, so the traps became part of the local environment for the voles. Clean live traps were much less likely to catch Microtus townsendii than dirty traps soiled with urine and feces (Boonstra and Krebs 1976). It is clear that minor behavioural quirks of the species under study may have significant effects on the capture data obtained. Individual heterogeneity in the probability of capture is a major problem in all mark-recapture work. But in the end natural history is as important as statistics.

There are at least two take home messages that can come from all these considerations. First, there are many statistical decisions that have to be made before population size can be estimated from mark-recapture data or any kind of quadrat based data. Second, there is also much biological information that must be well known before starting out with some kind of sampling design. Detectability may vary greatly with observers, with types of traps used, and observer skills so that again the devil is in the details. A third take home message given to me by someone who must remain nameless is that mark-recapture is hopeless as an ecological method because even after much work, the elusive population size that one wishes to know is lost in a pile of assumptions. But we cannot accept such a negative view without trying very hard to overcome the problems of sampling and estimation.

One way out of the box we find ourselves in (if we want to estimate population size) is to use an index of abundance and recognize its limitations. We cannot use quantitative population modelling on indices but we may find that indices are the best we can do for now. In particular, monitoring with little money must rely on indices of many populations of both plants and animals. Some data are better than no data for the management of populations and communities.

For the present time spatially explicit capture-recapture (SECR) methods of population estimation have provided a most useful approach to estimating density (Efford et al. 2009, 2013) and much future work will be needed to tell us how useful this relatively new approach is for accurately estimating population density (Broekhuis and Gopalaswamy 2016).

And a final reminder that even if you study community or ecosystem ecology, you must rely on getting measures of abundance for many quantitative models of system performance. So methods that provide accuracy for population sizes are just as essential for the vast array of ecological studies.

Anderson, D.R. 2003. Index values rarely constitute reliable information. Wildlife Society Bulletin 31(1): 288-291.

Boonstra, R. and Krebs, C.J. 1976. The effect of odour on trap response in Microtus townsendii. Journal of Zoology (London) 180(4): 467-476. Doi: 10.1111/j.1469-7998.1976.tb04692.x.

Broekhuis, F. and Gopalaswamy, A.M. 2016. Counting cats: Spatially explicit population estimates of cheetah (Acinonyx jubatus) using unstructured sampling data. PLoS ONE 11(5): e0153875. Doi: 10.1371/journal.pone.0153875.

Efford, M.G. and Fewster, R.M. 2013. Estimating population size by spatially explicit capture–recapture. Oikos 122(6): 918-928. Doi: 10.1111/j.1600-0706.2012.20440.x.

Efford, M.G., Dawson, D.K., and Borchers, D.L. 2009. Population density estimated from locations of individuals on a passive detector array. Ecology 90(10): 2676-2682. Doi: 10.1890/08-1735.1

Engeman, R.M. 2003. More on the need to get the basics right: population indices. Wildlife Society Bulletin 31(1): 286-287.

Krebs, C.J. and Boonstra, R. 1984. Trappability estimates for mark-recapture data. Canadian Journal of Zoology 62 (12): 2440-2444. Doi: 10.1139/z84-360

Leslie, P.H., Chitty, D., and Chitty, H. 1953. The estimation of population parameters from data obtained by means of the capture-recapture method. III. An example of the practical applications of the method. Biometrika 40 (1-2): 137-169. Doi:10.1093/biomet/40.1-2.137

McKelvey, K.S. & Pearson, D.E. (2001) Population estimation with sparse data: the role of estimators versus indices revisited. Canadian Journal of Zoology, 79(10): 1754-1765. Doi: 10.1139/cjz-79-10-1754

Does Forestry Make Money – Part 2

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About 2 years ago I wrote a blog asking the simple question of whether the forest industry in British Columbia makes money or whether it is operational only because of subsidies and the failure to recognize that biodiversity and ecosystem services could be valuable. A recent report from the research group in the Fenner School of the Australian National University has put the spotlight on the mountain ash forests of the Central Highlands of Victoria to answer this question for one region of southern Australia. I summarize their findings from their report (Keith et al. 2016) that you can access from the web address given below.

The ANU research group chose the Central Highlands study area because it included areas with controversial land use activities. The study area of 7370 sq km contains a range of landscapes including human settlements, agricultural land, forests, and waterways, and is used for a variety of activities including timber production, agriculture, water supply and recreation. It is also home to a range of species, including the endemic and critically endangered Leadbeater’s Possum. These activities and their use of ecosystems can be either complementary or conflicting. Managing the various activities within the region is therefore complex and requires evaluation of the trade-offs between different land uses and users, an issue common to forestry areas around the world.

The accounting structure (System of Environmental-Economic Accounting) which is used by the United Nations is described in more detail in the report. Both economic and ecological data are needed to produce ecosystem accounts and these sources of data must be integrated to gain an overall picture of the system. This integration of ecosystem services with traditional cash crops is the key to evaluating an area for all of its values to humans. In this particular area the provisioning of water to cities is a key economic benefit provided by this particular area. The following table from their report puts all these accounts together for the Central Highlands of Victoria:

Table 5. Economic information for industries within the study region in 2013-14
Agriculture Native Forestry Water supply Tourism
Area of land used (ha) 96,041a 324,380b 115,149c 737,072d
Sale of products ($m) 474 49 911 485
Industry valued added ($m) 257 9 233 260
Ecosystem services ($m) 121 15 101 42
Sale of products ($ ha-1) 4918 151 7911 659
Industry value added ($ ha-1) 2667 29 2023 353
Ecosystem services ($ ha-1) 1255 46 877 57

a area of agricultural land use
b area of native forest timber production
c area of water catchments
d total area of study region

The key point in this table is that the value-added per ha of forestry is $29 per ha per year. The equivalent value for water is $2033 per ha per year – or 70 times more, and the value added for agriculture is about 90 time more than that of forestry. The value-added value for tourism is $350 per ha per year, about 12 times more than that of forestry. None of this takes into account any potential government subsidies to these industries, and none involves directly the endangered species in the landscape. Three main points emerge from this analysis:

  1. In 2013-14, the most valuable industries in the region were tourism ($260 million), agriculture ($257 million), water supply ($233 million) and forestry ($9 million). This is as measured by the estimated industry value added (the contribution to GDP).
  2. In 2013-14, the most valuable ecosystem services in the region were food provisioning ($121 million), water provisioning ($101 million), cultural and recreation services ($42 million).
  3. At a carbon price of $12.25 per ton (the average price paid by the Commonwealth in 2015), the potential ecosystem service of carbon sequestration ($20 million) was more valuable than the service of timber provisioning ($15 million).

The main implications from the report for this large geographical area are three:

  • The benefits from tourism, agriculture, and water supply are large, while those from forestry are comparatively small. There is a potential for income from carbon sequestration.
  • The activities of tourism, agricultural and water supply industries are complimentary and may be combined with biodiversity conservation and carbon sequestration.
  • Timber harvesting in native forests needs to better account for the occurrence of fires and can be incompatible with species requirements for conservation.

The recent global interest in both climate change and species conservation has pushed this type of analysis to uncover the complementary and conflicting activities of all major global industries. Replacing the conventional GDP of a country or a region with a measure that takes into account the changes in the natural capital including gains and losses is a necessary step for sustainability (Dasgupta 2015, Guerry et al. 2015). This report from Australia shows how this goal of replacing the current GDP calculation with a green GDP can be done in specific areas. Much of biodiversity conservation hinges on these developments.

Dasgupta, P. 2015. Disregarded capitals: what national accounting ignores. Accounting and Business Research 45(4): 447-464. doi: 10.1080/00014788.2015.1033851.

Guerry, A.D., et al. 2015. Natural capital and ecosystem services informing decisions: From promise to practice. Proceedings of the National Academy of Sciences 112(24): 7348-7355. doi: 10.1073/pnas.1503751112.

Keith, H., Vardon, M., Stein, J., Stein, J., and Lindenmayer, D. 2016. Exzperimental Ecosystem Accounts for the Central Highlands of Victoria. Australian National University, Fenner School of Environment and Society. 22 pp. Available from:
http://fennerschool-associated.anu.edu.au/documents/CLE/VCH_Accounts_Summary_FINAL_for_pdf_distribution.pdf

On Disease Ecology

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

On Statistical Progress in Ecology

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There is a general belief that science progresses over time and given that the number of scientists is increasing, this is a reasonable first approximation. The use of statistics in ecology has been one of ever increasing improvements of methods of analysis, accompanied by bandwagons. It is one of these bandwagons that I want to discuss here by raising the general question:

Has the introduction of new methods of analysis in biological statistics led to advances in ecological understanding?

This is a very general question and could be discussed at many levels, but I want to concentrate on the top levels of statistical inference by means of old-style frequentist statistics, Bayesian methods, and information theoretic methods. I am prompted to ask this question because of my reviewing of many papers submitted to ecological journals in which the data are so buried by the statistical analysis that the reader is left in a state of confusion whether or not any progress has been made. Being amazed by the methodology is not the same as being impressed by the advance in ecological understanding.

Old style frequentist statistics (read Sokal and Rohlf textbook) has been criticized for concentrating on null hypothesis testing when everyone knows the null hypothesis is not correct. This has led to refinements in methods of inference that rely on effect size and predictive power that is now the standard in new statistical texts. Information-theoretic methods came in to fill the gap by making the data primary (rather than the null hypothesis) and asking the question which of several hypotheses best fit the data (Anderson et al. 2000). The key here was to recognize that one should have prior expectations or several alternative hypotheses in any investigation, as recommended in 1897 by Chamberlin. Bayesian analysis furthered the discussion not only by having several alternative hypotheses but by the ability to use prior information in the analysis (McCarthy and Masters 2006). Implicit in both information theoretic and Bayesian analysis is the recognition that all of the alternative hypotheses might be incorrect, and that the hypothesis selected as ‘best’ might have very low predictive power.

Two problems have arisen as a result of this change of focus in model selection. The first is the problem of testability. There is an implicit disregard for the old idea that models or conclusions from an analysis should be tested with further data, preferably with data obtained independently from the original data used to find the ‘best’ model. The assumption might be made that if we get further data, we should add it to the prior data and update the model so that it somehow begins to approach the ‘perfect’ model. This was the original definition of passive adaptive management, which is now suggested to be a poor model for natural resource management. The second problem is that the model selected as ‘best’ may be of little use for natural resource management because it has little predictability. In management issues for conservation or exploitation of wildlife there may be many variables that affect population changes and it may not be possible to conduct active adaptive management for all of these variables.

The take home message is that we need in the conclusions of our papers to have a measure of progress in ecological insight whatever statistical methods we use. The significance of our research will not be measured by the number of p-values, AIC values, BIC values, or complicated tables. The key question must be: What new ecological insights have been achieved by these methods?

Anderson, D.R., Burnham, K.P., and Thompson, W.L. 2000. Null hypothesis testing: problems, prevalence, and an alternative. Journal of Wildlife Management 64(4): 912-923.

Chamberlin, T.C. 1897. The method of multiple working hypotheses. Journal of Geology 5: 837-848 (reprinted in Science 148: 754-759 in 1965). doi:10.1126/science.148.3671.754.

McCarthy, M.A., and Masters, P.I.P. 2005. Profiting from prior information in Bayesian analyses of ecological data. Journal of Applied Ecology 42(6): 1012-1019. doi:10.1111/j.1365-2664.2005.01101.x.

Walters, C. 1986. Adaptive Management of Renewable Resources. Macmillan, New York.

 

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.

What do the Data Points Mean?

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In Statistics 101 we were told that each data point in a scatter plot should have a precise meaning. Hopefully all ecologists agree with this, and if so I proceed to ask two questions about the ecology literature:

  1. What fraction of scatter plots in ecology papers define what the dots on the plot mean? Are they individual measurements, are they means of several measurements? Are they predictions from a mathematical model?
  2. Given that we know what the dots are, are we shown confidence limits for the points, or do we assume they are absolutely precise with no possible error?

With these two simple questions in mind I did a short, non-random search of recent ecology journals. Perhaps if a graduate ecology class is reading this blog, they could do a much wider search so that we might even be able to tell some of the editors of our journals how they score on Statistics 101 Quiz # 1. I went through 3 issues of Ecology (2015, issues 4, 5, and 6), 3 issues of the Journal of Animal Ecology (2015, issues 4 to 6), and 3 issues of Ecology Letters (2016, issues 1, 2, and 3). I scored each figure in each paper. The first question above is harder to score, so I divided the answer into three groups: clearly defined in figure legend, not defined in figure legend but clear in the paper itself, and not clearly defined anywhere. I kept the second question above on a simpler scale by asking if there were or were not confidence limits or S.E. on the dots in the scatter diagram. I considered histogram bars as ‘data points’ equivalent to scatter plots and scored these with these same 2 questions. I scored figures with multiple plots in the same figure as just one data source for my survey. I ignored maps, simulation data, and papers with only models. I got these results:

    Data points Confidence Limits or S.E.
Journal Number of papers Clearly defined in figure legend Yes No
Ecology 80 179
(95%)		 98
(50%)		 96
(50%)
Journal of Animal Ecology 84 195
(98%)		 119
(60%)		 81
(40%)
Ecology Letters 33 64
(94%)		 29
(43%)		 39
(57%)

The good news is that virtually all the data points in figures that contained empirical data were clearly defined, so the first question was not problematic. The potentially bad news is that around half of the data figures did not contain any measure of statistical precision for the data points.

There could be many reasons why confidence limits could not be applied to data points on graphs in papers. In some cases it would clutter the plot too much. In other cases the data points are completely accurate and have no error although this might be unusual in ecological data. Whatever the reason, some mention of the reason should be given in the text or the figure legend.

There were many limitations to this brief survey. It is clear that some subdisciplines of ecology adhere to Statistics 101 recommendations more carefully than others, but I did not tally these subdisciplines. One could make a thesis out of this sort of tally. Often I could not decipher if the data point was for an experimental unit or for a sampling unit but I have not analyzed for this error here.

So what do we conclude from this non-random survey? The take home message for authors is to make sure that the data points or histograms in their published figures are clearly defined in the figure legend and include if possible some measure of probable error. The message for reviewers and journal editors is to check that data points presented in submitted papers are properly identified and labeled with some measure of precision.

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

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

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

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

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

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

How helpful are mechanistic models – the gypsy moth story

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

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

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

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

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

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

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

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

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

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

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