Category Archives: Ecological Bandwagons

On Defining a Statistical Population

The more I do “field ecology” the more I wonder about our standard statistical advice to young ecologists to “random sample your statistical population”. Go to the literature and look for papers on “random environmental fluctuations”, or “non-random processes”, or “random mating” and you will be overwhelmed with references and biology’s preoccupation with randomness. Perhaps we should start with the opposite paradigm, that nothing in the biological world is random in space or time, and then the corollary that if your data show a random pattern or random mating or whatever random, it means you have not done enough research and your inferences are weak.

Since virtually all modern statistical inference rests on a foundation of random sampling, every statistician will be outraged by any concerns that random sampling is possible only in situations that are scientifically uninteresting. It is nearly impossible to find an ecological paper about anything in the real world that even mentions what their statistical “population” is, what they are trying to draw inferences about. And there is a very good reason for this – it is quite impossible to define any statistical population except for those of trivial interest. Suppose we wish to measure the heights of the male 12-year-olds that go to school in Minneapolis in 2017. You can certainly do this, and select a random sample, as all statisticians would recommend. And if you continued to do this for 50 years, you would have a lot of data but no understanding of any growth changes in 12-year-old male humans because the children of 2067 in Minneapolis would be different in many ways from those of today. And so, it is like the daily report of the stock market, lots of numbers with no understanding of processes.

Despite all these ‘philosophical’ issues, ecologists carry on and try to get around this by sampling a small area that is considered homogeneous (to the human eye at least) and then arm waving that their conclusions will apply across the world for similar small areas of some ill-defined habitat (Krebs 2010). Climate change may of course disrupt our conclusions, but perhaps this is all we can do.

Alternatively, we can retreat to the minimalist position and argue that we are drawing no general conclusions but only describing the state of this small piece of real estate in 2017. But alas this is not what science is supposed to be about. We are supposed to reach general conclusions and even general laws with some predictive power. Should biologists just give up pretending they are scientists? That would not be good for our image, but on the other hand to say that the laws of ecology have changed because the climate is changing is not comforting to our political masters. Imagine the outcry if the laws of physics changed over time, so that for example in 25years it might be that CO2 is not a greenhouse gas. Impossible.

These considerations should make ecologists and other biologists very humble, but in fact this cannot be because the media would not approve and money for research would never flow into biology. Humility is a lost virtue in many western cultures, and particularly in ecology we leap from bandwagon to bandwagon to avoid the judgement that our research is limited in application to undefined statistical populations.

One solution to the dilemma of the impossibility of random sampling is just to ignore this requirement, and this approach seems to be the most common solution implicit in ecology papers. Rabe et al. (2002) surveyed the methods used by management agencies to survey population of large mammals and found that even when it was possible to use randomized counts on survey areas, most states used non-random sampling which leads to possible bias in estimates even in aerial surveys. They pointed out that ground surveys of big game were even more likely to provide data based on non-random sampling simply because most of the survey area is very difficult to access on foot. The general problem is that inference is limited in all these wildlife surveys and we do not know the ‘population’ to which the numbers derived are applicable.

In an interesting paper that could apply directly to ecology papers, Williamson (2003) analyzed research papers in a nursing journal to ask if random sampling was utilized in contrast to convenience sampling. He found that only 32% of the 89 studies he reviewed used random sampling. I suspect that this kind of result would apply to much of medical research now, and it might be useful to repeat his kind of analysis with a current ecology journal. He did not consider the even more difficult issue of exactly what statistical population is specified in particular medical studies.

I would recommend that you should put a red flag up when you read “random” in an ecology paper and try to determine how exactly the term is used. But carry on with your research because:

Errors using inadequate data are much less than those using no data at all.

Charles Babbage (1792–1871

Krebs CJ (2010). Case studies and ecological understanding. Chapter 13 in: Billick I, Price MV, eds. The Ecology of Place: Contributions of Place-Based Research to Ecological Understanding. University of Chicago Press, Chicago, pp. 283-302. ISBN: 9780226050430

Rabe, M. J., Rosenstock, S. S. & deVos, J. C. (2002) Review of big-game survey methods used by wildlife agencies of the western United States. Wildlife Society Bulletin, 30, 46-52.

Williamson, G. R. (2003) Misrepresenting random sampling? A systematic review of research papers in the Journal of Advanced Nursing. Journal of Advanced Nursing, 44, 278-288. doi: 10.1046/j.1365-2648.2003.02803.x

 

On Biodiversity and Ecosystem Function

I begin with a quote from Seddon et al. (2016):

By 2012, the consensus view based on 20 years of research was that (i) experimental reduction in species richness, at any trophic level, negatively impacts both the magnitude and stability of ecosystem functioning [12,52], and (ii) the impact of biodiversity loss on ecosystem functioning is comparable in magnitude to other major drivers of global change [13,54].”

The references are to Cardinale et al. (2012), Naeem et al. (2012), Hooper et al. (2012), and Tilman et al. (2012).

The basic conclusion of the literature cited here is that with very extensive biodiversity loss, ecosystem function such as primary productivity will be reduced. I first of all wonder which set of ecologists would doubt this. Secondly, I would like to see these papers analysed for problems of data analysis and interpretation. A good project for a graduate class in experimental design and analysis. Many of the studies I suspect are so artificial in design as to be useless for telling us what will really happen as natural biodiversity is lost. At best perhaps we can view them as political ecology to try to convince politicians and the public to do something about the true drivers of the mess, climate change and overpopulation.

Too many of the graphs I see in published papers on biodiversity and ecosystem function look like this (from Maestre et al. (2012): data from 224 global dryland plots)

There is a trend in these data but zero predictability. And even if you feel that showing trends are good enough in ecology, the trend is very weak.

Many of these analyses utilize meta-analysis. I am a critic of the philosophy of meta-analysis and not alone in wondering how useful many of these are in guiding ecological research (Vetter et al. 2013, Koricheva, and Gurevitch 2014). Perhaps the strongest division in deciding the utility of these meta-analyses is whether one is interested in general trends across ecosystems or predictability which depends largely on understanding the mechanisms behind particular trends.

Another interesting aspect of many of these analyses lies in the preoccupation with stability as a critical ecosystem function maintained by species richness. In contrast to this belief, Jacquet et al. (2016) have argued that in empirical food webs there is no simple relationship between species richness and stability, contrary to conventional theory.

Finally, another quotation from Naeem et al. (2012) which raises a critical issue on which ecologists need to focus more:

“In much of experimental ecological research, nature is seen as the complex, species-rich reference against which treatment effects are measured. In contrast, biodiversity and ecosystem functioning experiments often simply compare replicate ecosystems that differ in biodiversity, without any replicate serving as a reference to nature. Consequently, it has often been difficult to evaluate the external validity of biodiversity and ecosystem functioning research, or how its findings map onto the “real” worlds of conservation and decision making. Put another way, what light can be shed on the stewardship of nature by microbial microcosms that have no analogs in nature, or by experimental grassland studies in which some plots have, by design, no grass species? “ (page 1403)

And for those of you who are animal ecologists, the vast bulk of these studies were done on plants with none of the vertebrate browsers and grazers present. Perhaps some problems here.

Whatever one’s view of these research paradigms, no questions will be answered if we lose too much biodiversity.

Cardinale, B.J., Duffy, J.E., Gonzalez, A., Hooper, D.U., Perrings, C., Venail, P., Narwani, A., Mace, G.M., Tilman, D., Wardle, D.A., Kinzig, A.P., Daily, G.C., Loreau, M., Grace, J.B., Larigauderie, A., Srivastava, D.S. & Naeem, S. (2012) Biodiversity loss and its impact on humanity. Nature, 486, 59-67. doi: 10.1038/nature11148

Hooper, D.U., Adair, E.C., Cardinale, B.J., Byrnes, J.E.K., Hungate, B.A., Matulich, K.L., Gonzalez, A., Duffy, J.E., Gamfeldt, L. & O/’Connor, M.I. (2012) A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature, 486, 105-108. doi: 10.1038/nature11118

Jacquet, C., Moritz, C., Morissette, L., Legagneux, P., Massol, F., Archambault, P. & Gravel, D. (2016) No complexity–stability relationship in empirical ecosystems. Nature Communications, 7, 12573. doi: 10.1038/ncomms12573

Koricheva, J. & Gurevitch, J. (2014) Uses and misuses of meta-analysis in plant ecology. Journal of Ecology, 102, 828-844. doi: 10.1111/1365-2745.12224

Maestre, F.T. et al. (2012) Plant species richness and ecosystem multifunctionality in global drylands. Science, 335, 214-218. doi: 10.1126/science.1215442

Naeem, S., Duffy, J.E. & Zavaleta, E. (2012) The functions of biological diversity in an Age of Extinction. Science, 336, 1401.

Seddon, N., Mace, G.M., Naeem, S., Tobias, J.A., Pigot, A.L., Cavanagh, R., Mouillot, D., Vause, J. & Walpole, M. (2016) Biodiversity in the Anthropocene: prospects and policy. Proceedings of the Royal Society B: Biological Sciences, 283, 20162094. doi: 10.1098/rspb.2016.2094

Tilman, D., Reich, P.B. & Isbell, F. (2012) Biodiversity impacts ecosystem productivity as much as resources, disturbance, or herbivory. Proceedings of the National Academy of Sciences 109, 10394-10397. doi: 10.1073/pnas.1208240109

Vetter, D., Rücker, G. & Storch, I. (2013) Meta-analysis: A need for well-defined usage in ecology and conservation biology. Ecosphere, 4, art74. doi: 10.1890/ES13-00062.1

Technology Can Lead Us Astray

Our iPhones teach us very subtly to have great faith in technology. This leads the public at large to think that technology will solve large issues like greenhouse gases and climate change. But for scientists we should remember that technology must be looked at very carefully when it tells us we have a shortcut to ecological measurement and understanding. For the past 35 years satellite data has been available to calculate an index of greening for vegetation from large landscapes. The available index is called NDVI, normalized difference vegetation index, and is calculated as a ratio of near infrared light to red light reflected from the vegetation being surveyed. I am suspicious that NDVI measurements tell ecologists anything that is useful for the understanding of vegetation dynamics and ecosystem stability. Probably this is because I am focused on local scale events and landscapes of hundreds of km2 and in particular what is happening in the forest understory. The key to one’s evaluation of these satellite technologies most certainly lies in the questions under investigation.

A whole array of different satellites have been used to measure NDVI and since the more recent satellites have different precision and slightly different physical characteristics, there is some problem of comparing results from different satellites in different years if one wishes to study long-term trends (Guay et al. 2014). It is assumed that NDVI measurements can be translated into aboveground net primary production and can be used to start to answer ecological questions about seasonal and annual changes in primary production and to address general issues about the impact of rising CO2 levels on ecosystems.

All inferences about changes in primary production on a broad scale hinge on the reliability of NDVI as an accurate measure of net primary production. Much has been written about the use of NDVI measures and the need for ground truthing. Community ecologists may be concerned about specific components of the vegetation rather than an overall green index, and the question arises whether NDVI measures in a forest community are able to capture changes in both the trees and the understory, or for that matter in the ground vegetation. For overall carbon capture estimates, a greenness index may be accurate enough, but if one wishes to determine whether deciduous trees are replacing evergreen trees, NDVI may not be very useful.

How can we best validate satellite based estimates of primary productivity? To do this on a landscape scale we need to have large areas with ground truthing. Field crops are one potential source of such data. Kang et al. (2016) used crops to quantify the relationship between remotely sensed leaf-area index and other satellite measures such as NDVI. The relationships are clear in a broad sense but highly variable in particular, so that the ability to predict crop yields from satellite data at local levels is subject to considerable error. Johnson (2016, Fig. 6, p. 75) found the same problem with crops such as barley and cotton (see sample data set below). So there is good news and bad news from these kinds of analyses. The good news is that we can have extensive global coverage of trends in vegetation parameters and crop production, but the bad news is that at the local level this information may not be helpful for studies that require high precision for example in local values of net primary production. Simply to assume that satellite measures are accurate measures of ecological variables like net aboveground primary production is too optimistic at present, and work continues on possible improvements.

Many of the critical questions about community changes associated with climate change cannot in my opinion be answered by remote sensing unless there is a much higher correlation of ground-based research that is concurrent with satellite imagery. We must look critically at the available data. Blanco et al. (2016) for example compared NDVI estimates from MODIS satellite data with primary production monitored on the ground in harvested plots in western Argentina. The regression between NDVI and estimated primary production had R2 values of 0.35 for the overall annual values and 0.54 for the data restricted to the peak of annual growth. Whether this is a satisfactory statistical association is up to plant ecologists to decide. I think it is not, and the substitution of p values for the utility of such relationships is poor ecology. Many more of these kind of studies need to be carried out.

The advent of using drones for very detailed spectral data on local study areas will open new opportunities to derive estimates of primary production. For the present I think we should be aware that NDVI and its associated measures of ‘greenness’ from satellites may not be a very reliable measure for local or landscape values of net primary production. Perhaps it is time to move back to the field and away from the computer to find out what is happening to global plant growth.

Blanco, L.J., Paruelo, J.M., Oesterheld, M., and Biurrun, F.N. 2016. Spatial and temporal patterns of herbaceous primary production in semi-arid shrublands: a remote sensing approach. Journal of Vegetation Science 27(4): 716-727. doi: 10.1111/jvs.12398.

Guay, K.C., Beck, P.S.A., Berner, L.T., Goetz, S.J., Baccini, A., and Buermann, W. 2014. Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment. Global Change Biology 20(10): 3147-3158. doi: 10.1111/gcb.12647.

Johnson, D.M. 2016. A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. International Journal of Applied Earth Observation and Geoinformation 52(1): 65-81. doi: 10.1016/j.jag.2016.05.010.

Kang, Y., Ozdogan, M., Zipper, S.C., Roman, M.O., and Walker, J. 2016. How universal Is the relationship between remotely sensed vegetation Indices and crop leaf area Index? A global assessment. Remote Sensing 2016 8(7): 597 (591-529). doi: 10.3390/rs8070597.

Cotton yield vs NDVI Index

Biodiversity Conundrums

Conservation ecologists face a conundrum, as many have pointed out before. As scientists we do not make policy. Most conservation problems are essentially a moral issue of dealing with conflicts in goals and allowable actions. Both the United States and Canada have endangered species legislation in which action plans are written for species of concern. In the USA species of concern are allotted some funding and more legal protection than in Canada, where much good material is written but funding for action or research is typically absent. What is interesting from an ecological perspective is the list of species that are designated as endangered or threatened. Most of them can be described colloquially as the “charismatic megafauna”, species that are either large or beautiful or both. There are exceptions of course for some amphibians and rare plants, but by and large the list of species of concern is a completely non-random collection of organisms that people see in their environment. Birds and butterflies and large mammals are at the head of the list.

All of this is fine and useful because it is largely political ecology, but it raises the question of what will happen should these rescue plans for threatened or endangered species fail. This question lands ecologists in a rather murky area of ecosystem function, which leads to the key question: how is ecosystem function affected by the loss of species X? The answer to this question depends very much on how you define ecosystem function. If species X is a plant and the ecosystem function measured is the uptake of CO2 by the plant community, the answer could be a loss of function, no change, or indeed an increase in CO2 uptake if species X for example is replaced by a weed that is more productive that species X. The answer to this simple question is thus very complicated and requires much research. For a hypothetical example, plant X may be replaced by a weed that fixes more CO2, and thus ecosystem function is improved as measured by carbon uptake from the atmosphere. But the weed may deplete soil nitrogen which could adversely affect other plants and soil quality. Again more data are needed to decide this. If the effect size is small, much research could provide an ambiguous answer to the original question, since all measurement involves errors.

So now we are in a box, a biodiversity conundrum. The simplest escape is to say that all species loss is undesirable in any ecosystem, a pontification that is more political than scientific. And, for a contrary view, if the species lost is a disease organism, or an insect that spreads human diseases, we will not mourn its passing. In practice we seem to agree with the public that the species under concern are not all of equal value for conservation. The most serious outcome of this consideration is that where the money goes for conservation is highly idiosyncratic. There are two major calls for funding that perhaps should not be questioned: first, for land (and water) acquisition and protection, and second, for providing compensation for the people whose livelihoods are affected by protected areas with jobs and skills that improve their lives. The remaining funds need to be used for scientific research that will further the cause of conservation in the broad sense. The most useful principle at this stage is that all research has a clear objective and a clear list of what outcomes can be used to judge its success. For conservation outcomes this judgement should be clear cut. Currently they are not.

When Caughley (1994) described the declining population paradigm and the small population paradigm he clearly felt that the small population paradigm, while theoretically interesting, had little to contribute to most of the real world problems of biodiversity conservation. He could not have imagined at the time how genetics would develop into a powerful set of methods of analysis of genomes. But with a few exceptions the small population paradigm and all the elegant genetic work that has sprung from it has delivered a mountain of descriptive information with only a molehill of useful management options for real world problems. Many will disagree with my conclusion, and it is clear that conservation genetics is a major growth industry. That is all well and good but my question remains as to its influence on the solution of current conservation problems (Caro 2008; Hutchings 2015; Mattsson et al. 2008). Conservation genetic papers predicting extinctions in 100 years or more based on low levels of genetic variation are not scientifically testable and rely on a law of conservation genetics that is riddled with exceptions (Nathan et al. 2015; Robinson et al. 2016). Do we need more untestable hypotheses in conservation biology?

Caro, T. 2008. Decline of large mammals in the Katavi-Rukwa ecosystem of western Tanzania. African Zoology 43(1): 99-116. doi:10.3377/1562-7020(2008)43[99:dolmit]2.0.co;2.

Caughley, G. 1994. Directions in conservation biology. Journal of Animal Ecology 63: 215-244. doi: 10.2307/5542

Hutchings, J.A. 2015. Thresholds for impaired species recovery. Proceedings of the Royal Society. B, Biological sciences 282(1809): 20150654. doi:10.1098/rspb.2015.0654.

Mattsson, B.J., Mordecai, R.S., Conroy, M.J., Peterson, J.T., Cooper, R.J., and Christensen, H. 2008. Evaluating the small population paradigm for rare large-bodied woodpeckers, with Implications for the Ivory-billed Woodpecker. Avian Conservation and Ecology 3(2): 5. http://www.ace-eco.org/vol3/iss2/art5/

Nathan, H.W., Clout, M.N., MacKay, J.W.B., Murphy, E.C., and Russell, J.C. 2015. Experimental island invasion of house mice. Population Ecology 57(2): 363-371. doi:10.1007/s10144-015-0477-2.

Robinson, J.A., Ortega-Del Vecchyo, D., Fan, Z., Kim, B.Y., and vonHoldt, B.M. 2016. Genomic flatlining in the endangered Island Fox. Current Biology 26(9): 1183-1189. doi:10.1016/j.cub.2016.02.062.

Climate Change and Ecological Science

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

On Tipping Points and Regime Shifts in Ecosystems

A new important paper raises red flags about our preoccupation with tipping points, alternative stable states and regime shifts (I’ll call them collectively sharp transitions) in ecosystems (Capon et al. 2015). I do not usually call attention to papers but this paper and a previous review (Mac Nally et al. 2014) seem to me to be critical for how we think about ecosystem changes in both aquatic and terrestrial ecosystems.

Consider an oversimplified example of how a sharp transition might work. Suppose we dumped fertilizer into a temperate clear-water lake. The clear water soon turns into pea soup with a new batch of algal species, a clear shift in the ecosystem, and this change is not good for many of the invertebrates or fish that were living there. Now suppose we stop dumping fertilizer into the lake. In time, and this could be a few years, the lake can either go back to its original state of clear water or it could remain as a pea soup lake for a very long time even though the pressure of added fertilizer was stopped. This second outcome would be a sharp transition, “you cannot go back from here” and the question for ecologists is how often does this happen? Clearly the answer is of great interest to natural resource managers and restoration ecologists.

The history of this idea for me was from the 1970s at UBC when Buzz Holling and Carl Walters were modelling the spruce budworm outbreak problem in eastern Canadian coniferous forests. They produced a model with a manifold surface that tipped the budworm from a regime of high abundance to one of low abundance (Holling 1973). We were all suitably amazed and began to wonder if this kind of thinking might be helpful in understanding snowshoe hare population cycles and lemming cycles. The evidence was very thin for the spruce budworm, but the model was fascinating. Then by the 1980s the bandwagon started to roll, and alternative stable states and regime change seemed to be everywhere. Many ideas about ecosystem change got entangled with sharp transition, and the following two reviews help to unravel them.

Of the 135 papers reviewed by Capon et al. (2015) very few showed good evidence of alternative stable states in freshwater ecosystems. They highlighted the use and potential misuse of ecological theory in trying to predict future ecosystem trajectories by managers, and emphasized the need of a detailed analysis of the mechanisms causing ecosystem change. In a similar paper for estuaries and near inshore marine ecosystems, Mac Nally et al. (2014) showed that of 376 papers that suggested sharp transitions, only 8 seemed to have sufficient data to satisfy the criteria needed to conclude that a transition had occurred and was linkable to an identifiable pressure. Most of the changes described in these studies are examples of gradual ecosystem changes rather than a dramatic shift; indeed, the timescale against which changes are assessed is critical. As always the devil is in the details.

All of this is to recognize that strong ecosystem changes do occur in response to human actions but they are not often sharp transitions that are closely linked to human actions, as far as we can tell now. And the general message is clearly to increase rigor in our ecological publications, and to carry out the long-term studies that provide a background of natural variation in ecosystems so that we have a ruler to measure human induced changes. Reviews such as these two papers go a long way to helping ecologists lift our game.

Perhaps it is best to end with part of the abstract in Capon et al. (2015):

“We found limited understanding of the subtleties of the relevant theoretical concepts and encountered few mechanistic studies that investigated or identified cause-and-effect relationships between ecological responses and nominal pressures. Our results mirror those of reviews for estuarine, nearshore and marine aquatic ecosystems, demonstrating that although the concepts of regime shifts and alternative stable states have become prominent in the scientific and management literature, their empirical underpinning is weak outside of a specific environmental setting. The application of these concepts in future research and management applications should include evidence on the mechanistic links between pressures and consequent ecological change. Explicit consideration should also be given to whether observed temporal dynamics represent variation along a continuum rather than categorically different states.”

 

Capon, S.J., Lynch, A.J.J., Bond, N., Chessman, B.C., Davis, J., Davidson, N., Finlayson, M., Gell, P.A., Hohnberg, D., Humphrey, C., Kingsford, R.T., Nielsen, D., Thomson, J.R., Ward, K., and Mac Nally, R. 2015. Regime shifts, thresholds and multiple stable states in freshwater ecosystems; a critical appraisal of the evidence. Science of The Total Environment 517(0): in press. doi:10.1016/j.scitotenv.2015.02.045.

Holling, C.S. 1973. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4: 1-23. doi:10.1146/annurev.es.04.110173.000245.

Mac Nally, R., Albano, C., and Fleishman, E. 2014. A scrutiny of the evidence for pressure-induced state shifts in estuarine and nearshore ecosystems. Austral Ecology 39: 898-906. doi:10.1111/aec.12162.

Some Reflections on Evo-Eco

Some ecologists study evolutionary processes and we call them evolutionary ecologists. They have their own journals and are a thriving field of science. Other ecologists study populations, communities, and ecosystems in ecological time and do not in general concern themselves with evolutionary changes.The question is should they? Evo-Eco is a search for evolutionary changes that have a decisive impact on observable ecological changes like that of a collapsing bird population.

There are two schools of thought. The first is that evo-eco is very important and the changes that ecologists are trying to understand are partly caused by ecological mechanisms like predation and competition but are also associated with genetic changes that affect survival and reproduction. Consequently an ecologist studying the declining bird population should study both genetics and ecology. The second school of thought is that evo-eco is rarely of any importance in causing ecological changes, so that we can more or less ignore genetics if we wish to understand why this bird population is disappearing.

A practical problem immediately rears its head. To be safe we should all follow evo-eco in case genetics is involved in dynamics. But given the number of problems that ecologists face, the number of scientists available to analyse them, and the research dollars available it is rare to have the time, energy or money to take the comprehensive route. Conservation ecologists are perhaps the most tightly squeezed of all ecologists because they have no time to spare. Environmental managers request answers about what to do, and the immediate causes of conservation problems are (as everyone knows) habitat loss, introduced pests and diseases, and pollution.

The consequence of all this is that the two schools of thought drift apart. I cannot foresee any easy way to solve this issue. Progress in evolutionary ecology is often very slow and knowing the past rarely gives us much insight into predicting the human-affected future. Progress in conventional ecology is faster but our understanding is based on short-term studies of unknown generality for future events. Both schools of thought race along with mathematical models that may or may not tell us anything about the real world, but are conceptually elegant and in a pinch might be called progress if we had time to test them adequately.

The most useful evo-eco approach has been to look at human-caused selection via fishing for large sized fish or hunting for Dall sheep with the largest horns. The overuse of antibiotics for human sickness and as prophylactics for our farm animals is another classic case in which to understand the ecological dynamics we need to know the evolutionary changes that we humans have caused. These are clear cases in which genetic insights can teach us very much.

I end with a story from my past. In the 1950s, nearly 70 years ago now, Dennis Chitty working at Oxford on population fluctuations in small grassland rodents considered that he could reject most of the conventional explanations for animal population changes, and he suggested that individuals might change in quality with population density. This change he thought might involve genetic selection for traits that were favourable only in high density populations that reappeared every 3-4 years. So in some strange sense he was one of the earliest evo-eco ecologists. The result was that he was nearly laughed out of Oxford by the geneticists in control. The great evolutionary geneticist E.B. Ford told Chitty he was completely mad to think that short term selection was possible on a scale to impact population dynamics. Genetic changes took dozens to hundreds of years at the best of time. There were of course in the 1950s only the most primitive of genetic methods available for mammals that all look the same in their coat colour, and the idea that changes in animal behaviour involving territoriality might cause genetic shifts on a short-term period gradually lost favour. Few now think that Chitty was right in being evo-eco, but in some sense he was ahead of his time in thinking that natural selection might operate quickly in field populations. Given the many physiological and behavioural changes that can occur phenotypically in mammals, most subsequent work on grassland rodents has become buried in mechanisms that do not change because of genetic selection.

When we try to sort out whether to be concerned about evo-eco, we must strike a compromise between what the exact question is that we are trying to investigate, and how we can best construct a decision tree that can operate in real time with results that are useful for the research question. Not every ecological problem can be solved by sequencing the study organism.

Chitty, D. 1960. Population processes in the vole and their relevance to general theory. Canadian Journal of Zoology 38:99-113.

Models need testable predictions to be useful

It has happened again.  I have just been to a seminar on genetic models – something about adaptation of species on the edges of their ranges.  Yes this is an interesting topic of relevance to interpreting species’ responses to changing environments.  It ended by the speaker saying something like, “It would be a lot of work to test this in the field”. How much more useful my hour would have been spent if the talk had ended with “Although it would be difficult to do, this model makes the following predictions that could be tested in the field,” or “The following results would reject the hypothesis upon which this model is based.”

Now it is likely that some found these theoretical machinations interesting and satisfying in some mathematical way, but I feel that it is irresponsible to not even consider how a model could be tested and the possibility (a likely possibility at that) that it doesn’t apply to nature and tells us nothing helpful about understanding what is going to happen to willow or birch shrubs at the edge of their ranges in the warming arctic (for example).

Recommendation – no paper on models should be published or talked about unless it makes specific, testable predictions of how the model can be tested.

On Important Questions in Ecology

There is a most interesting paper that you should read about the important questions in ecology:

Sutherland, W.J. et al. (2013) Identification of 100 fundamental ecological questions. Journal of Ecology, 101, 58-67.

This paper represents the views of 388 ecologists who culled through all of the 754 questions submitted and vetted in a two day workshop in London in April 2012. There are many thesis topics highlighted in this list and it gives a good overview of what many ecologists think is important. But there are some problems with this approach that you might wish to consider after you read this paper.

We can begin with a relatively trivial point. The title indicates that it will discuss ‘fundamental’ questions in ecology but the Summary changes this to ‘important’ questions. To be sure the authors recognize that what we now think is ‘important’ may be judged in the future to be less than important, so in a sense they recognize this problem. ‘Important’ is not an operational word in science, and consequently it is always a focus for endless argument. But let us not get involved with semantics and look at the actual 100 questions.

As I read the paper I was reminded of the discussion in Peters (1991, p. 13) who had the audacity to point out that academic ecologists thrived on unanswerable questions. In particular Peters (1991) focused on ‘why’ questions as being high on the list of unanswerable ones, and it is good to see that there are only 2 questions out of 100 that have a ‘why’ in them. Most of the questions posed are ‘how’ questions (about 65 instances) and ‘what’ questions (about 52 instances).

In framing questions in any science there is a fine line in the continuum of very broad questions that define an agenda and at the other extreme to very specific questions about one species or community. With very broad questions there will never be a clear point at which we can say that we have answered that question so we can move on. With very specific questions we can answer them experimentally and move on. So where do we cut the cake of questions? Most of these 100 questions are very broad and so they both illuminate and frustrate me because they cannot be answered without making them more specific.

Let me go over just one example. Question 11 What are the evolutionary and ecological mechanisms that govern species’ range margins? First, we might note that this question goes back at least 138 years to Alfred Wallace (1876, The Geographical Distribution of Animals), and has been repeated in many ecology textbooks ever since. There are few organisms for which it has been answered and very much speculation about it. At the moment the ecological mechanism in favour is ‘climate’. This is a question that can be answered ecologically only for particular species, and cannot be answered in real (human) time for the evolutionary mechanisms. Consequently it is an area rife for correlational ecology whose conclusions could possibly be tested in a hundred years if not longer. All of these problems should not stand in the way of doing studies on range margins, and there are many hundreds of papers that attest to this conclusion. My question is when will we know that we have answered this question, and my answer is never. We can in some cases use paleoecology to get at these issues, and then extrapolate that the future will be like the past, a most dubious assumption. My concern is that if we have not answered this question in 138 years, what is the hope that we will answer it now?

It is good to be optimistic about the future development of ecological science. Perhaps I have picked a poor example from the list of 100 questions, and my concern is that in this case at least this is not a question that I would suggest to a new PhD student. Still I am glad to have this list set out so clearly and perhaps the next step would be to write a synthesis paper on each of the 100 topics and discuss how much progress has been made on that particular issue, and what exactly we might do to answer the question more rapidly. How can we avoid in ecology what Cox (2007) called a “yawning abyss of vacuous generalities”?

Cox, D. R. (2007) Applied statistics: A review. Annals of Applied Statistics, 1, 1-16.

Peters, R. H. (1991) A Critique for Ecology, Cambridge University Press, Cambridge, England.

Sutherland, W. J., Freckleton, R. P., Godfray, H. C. J., Beissinger, S. R., Benton, T., Cameron, D. D., Carmel, Y., Coomes, D. A., Coulson, T., Emmerson, M. C., Hails, R. S., Hays, G. C., Hodgson, D. J., Hutchings, M. J., Johnson, D., Jones, J. P. G., Keeling, M. J., Kokko, H., Kunin, W. E. & Lambin, X. (2013) Identification of 100 fundamental ecological questions. Journal of Ecology, 101, 58-67.

Two Visions of Ecological Research

Let us assume for the moment that the goal of scientific ecology is to understand the reasons for changes in the distribution and abundance of animals, plants, and microbes. If you do not think this is our main agenda, perhaps you should not read further.

The conventional, old paradigm to achieve this goal is to obtain a good description of the natural history of the organisms of interest in a population or community, define the food web they operate within, and then determine by observations or manipulations the parameters that limit its distribution and abundance. This can be difficult to achieve in rich food webs with many species, and in systems in which the species are not yet taxonomically described, and particularly in microbe communities. Consequently a prerequisite of this paradigm is to have good taxonomy and to be able to recognize species X versus species Y. A whole variety of techniques can be used for this taxonomy, including morphology (the traditional approach) and genetics. Using this approach ecologists over the past 90 years have made much progress in deriving some tentative explanations for the changes that occur in populations and communities. If there has been a problem with this approach, it is largely because of disagreements about what data are sufficient to test hypothesis X, and whether the results of manipulation Y are convincing. A great deal of the accumulated data obtained with this approach has been useful to fisheries management, wildlife management, pest control, and agricultural production.

The new metagenomics paradigm, to use one label, suggests that this old approach is not getting us anywhere fast enough for microbial communities, and we need to forget most of this nonsense and get into sequencing, particularly for microbial communities. New improvements in the speed of doing this work makes it feasible. The question I wish to address here is not the validity or the great improvements in genetic analysis, but rather whether or not this approach can replace the conventional old paradigm. I appreciate that if we grab a sample of mud, water, or the bugs in an insect trap and grind it all up, and run it through these amazing sequencing machines, we get a very great amount of data. We then might try to associate some of these kinds of data with particular ‘species’ and this may well work in groups for which the morphological species are well described. But what do we do about the undescribed sequences? We know that microbial diversity is much higher than what we can currently culture in the laboratory. We can make rules about what to call unknown unit A, unknown unit B, and so on. That is fine, but now what? We are in some sense back where Linnaeus was in 1753 in giving names to plants.

Now comes the difficult bit. Do we just take the metagenomics approach and tack it on to the conventional approach, using unknown A, unknown B, etc. instead of Pseudomonas flavescens or Bacillus licheniformis? We cannot get very far this way because the first thing we need to decide is does unknown A a primary producer or unknown B a decomposer of complex organic molecules? So perhaps this leads us to invent a whole new taxonomy to replace the old one. But perhaps we will go another way to say we will answer questions with the new system like is this pond ecosystem changing in response to global warming or nutrient additions? We can describe many system shifts in DNA-terminology but will we have any knowledge of what they mean or how management might change these trends? We could work all this out in the long term I presume. So I guess my confusion is largely exactly which set of hypotheses are you going to test with the new metagenomics paradigm? I can see a great deal of alpha-descriptive information being captured but I am not sure where to go from there. My challenge to the developers of the new paradigm is to list a set of problems in the Earth’s ecosystems for which this new paradigm could provide better answers more quickly than the old approach.

Microbial ecology is certainly much more difficult to carry out than traditional ecology on macroscopic animals and plants. As such it should be able to use new technology that can improve understanding of the structure and function of microbe communities. All new advances in technology are helpful for solving some ecological problems and should be so used. The suggestion that the conventional approach is out of date should certainly be entertained but in the last 70 years the development of air photos, of radio telemetry, of satellite imagery, of electrophoresis, of simplified chemical analyses, of automated weather stations, and the new possibilities of genetic analysis have been most valuable to solving ecological questions for many of our larger species. But in every case, at every step we should be more careful to ask exactly what questions the new technology can answer. Piling up terabytes of data is not science and could in fact hinder science. We do not wish to validate the Rutherford prediction that our ecological science is “stamp collecting”.