Tag Archives: progress in ecology

The Problem of Time in Ecology

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There is a problem in doing ecological studies that is too little discussed – what is the time frame of a good study? The normal response would be that the time frame varies with each study so that no guidelines can be provided. There is increasing recognition that more long-term studies are needed in ecology (e.g. Hughes et al. 2017) but the guidelines remain unclear.

The first issue is usually to specify a time frame, e.g. 5 years, 10 years. But this puts the cart before the horse, as the first step ought to be to define the hypothesis being investigated. In practice hypotheses in many ecological papers are poorly presented and there should not be one hypothesis but a series of alternative hypotheses. Given that, the question of time can be given with more insight. How many replicated time periods do you need to measure the ecological variables in the study? If your time scale unit is one year, 2 or 3 years is not enough to come to any except very tentative conclusions. We have instantly fallen into a central dilemma of ecology – studies are typically planned and financed on a 3–5-year time scale, the scale of university degrees.

Now we come up against the fact of climate change and the dilemma of trying to understand a changing system when almost all field work assumes an unchanging environment. Taken to some extreme we might argue that what happens in this decade tells us little about what will happen in the next decade. The way around this problem is to design experiments to test the variables that are changing ahead of time, e.g., what a 5⁰C temperature increase will do to the survival of your corals. To follow this approach, which is the classic experimental approach of science, we must assume we know the major variables affecting our population or community changes. At present we do not know the answer to this question, and we rely on correlations of a few variables as predictors of how large a change to expect.

There is no way out of this empirical box, which defines clearly how physics and chemistry differ from ecology and medicine. There are already many large-scale illustrations of this problem. Forest companies cut down old-growth timber on the assumption that they can get the forest back by replanting seedlings in the harvested area. But what species of tree seedlings should we replant if we are concerned that reforestation often operates on a 100–500-year time scale? And in most cases, there is no consideration of the total disruption of the ecosystem, and we ignore all the non-harvestable biodiversity. Much research is now available on reforestation and the ecological problems it produces. Hole-nesting birds can be threatened if old trees with holes are removed for forestry or agricultural clearing (Saunders et al. 2023). Replanting trees after fire in British Columbia did not increase carbon storage over 55 years of recovery when compared with unplanted sites (Clason et al. 2022). Consequently, in some forest ecosystems tree planting may not be useful if carbon storage is the desired goal.

At the least we should have more long-term monitoring of the survival of replanted forest tree seedlings so that the economics of planting could be evaluated. Short-term Australian studies in replanted agricultural fields showed over 4 years differences in survival of different plant species (Jellinek et al. 2020). For an on-the-ground point of view story about tree planting in British Columbia see:
https://thetyee.ca/Opinion/2023/11/02/Dont-Thank-Me-Being-Tree-Planter/. But we need longer-term studies on control and replanted sites to be more certain of effective restoration management. Gibson et al. (2022) highlighted the fact that citizen science over a 20-year study could make a major contribution to measuring the effectiveness of replanting. Money is always in short supply in field ecology and citizen science is one way of achieving goals without too much cost. 

Forest restoration is only one example of applied ecology in which long-term studies are too infrequent. The scale of restoration of temperate and boreal ecosystems is around 100 years, and this points to one of the main failures of long-term studies, that they are difficult to carry on after the retirement of the principal investigators who designed the studies.

The Park Grass Experiment begun in 1856 on 2.8 ha of grassland in England is the oldest ecological experiment in existence (Silvertown et al. 2006). As such it is worth a careful evaluation for the questions it asked and did not ask, for the scale of the experiment, and for the experimental design. It raises the question of generality for all long-term studies and cautions us about the utility and viability of many of the large-scale, long-term studies now in progress or planned for the future.

The message of this discussion is that we should plan for long-term studies for most of our critical ecological problems with clear hypotheses of how to conserve biodiversity and manage our agricultural landscapes and forests. We should move away from 2–3-year thesis projects on isolated issues and concentrate on team efforts that address critical long-term issues with specific hypotheses. Which says in a nutshell that we must develop a vision that goes beyond our past practices in scatter-shot, short-term ecology and at the same time avoid poorly designed long-term studies of the future.

Clason, A.J., Farnell, I. & Lilles, E.B. (2022) Carbon 5–60 Years After Fire: Planting Trees Does Not Compensate for Losses in Dead Wood Stores. Frontiers in Forests and Global Change, 5, 868024. doi: 10.3389/ffgc.2022.868024.

Gibson, M., Maron, M., Taws, N., Simmonds, J.S. & Walsh, J.C. (2022) Use of citizen science datasets to test effects of grazing exclusion and replanting on Australian woodland birds. Restoration Ecology, 30, e13610. doi: 10.1111/rec.13610.

Hughes, B.B.,et al. (2017) Long-term studies contribute disproportionately to ecology and policy. BioScience, 67, 271-281. doi.: 10.1093/biosci/biw185.

Jellinek, S., Harrison, P.A., Tuck, J. & Te, T. (2020) Replanting agricultural landscapes: how well do plants survive after habitat restoration? Restoration Ecology, 28, 1454-1463. doi: 10.1111/rec.13242.

Saunders, D.A., Dawson, R. & Mawson, P.R. (2023) Artificial nesting hollows for the conservation of Carnaby’s cockatoo Calyptorhynchus latirostris: definitely not a case of erect and forget. Pacific Conservation Biology, 29, 119-129. doi: 10.1071/PC21061.

Silvertown, J., Silvertown, J., Poulton, P. & Biss, P.M. (2006) The Park Grass Experiment 1856–2006: its contribution to ecology. Journal of Ecology, 94, 801-814. doi: 10.1111/j.1365-2745.2006.01145.x.

The Ecological Outlook

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There is an extensive literature on ecological traps going back two decades (e.g. Schlaepfer et al. 2002, Kristan 2003, Battin 2004) discussing the consequences of particular species selecting a habitat for breeding that is now unsuitable. A good example is discussed in Lamb et al. (2017) for grizzly bears in southeastern British Columbia in areas of high human contact. The ecological trap hypothesis has for the most part been discussed in relation to species threatened by human developments with some examples of whole ecosystems and human disturbances (e.g. Lindenmayer and Taylor 2020). The concept of an ecological trap can be applied to the Whole Earth Ecosystem, as has been detailed in the IPCC 2022 reports and it is this global ecological trap that I wish to discuss.

The key question for ecologists concerned about global biodiversity is how much biodiversity will be left after the next century of human disturbances. The ecological outlook is grim as you can hear every day on the media. The global community of ecologists can ameliorate biodiversity loss but cannot stop it except on a very local scale. The ecological problem operates on a century time scale, just the same as the political and social change required to escape the global ecological trap. E.O. Wilson (2016) wrote passionately about our need to set aside half of the Earth for biodiversity. Alas, this was not to be. Dinerstein et al. (2019) reduced the target to 30% in the “30 by 30” initiative, subsequently endorsed by 100 countries by 2022. Although a noble political target, there is no scientific evidence that 30 by 30 will protect the world’s biodiversity. Saunders et al. (2023) determined that for North America only a small percentage of refugia (5– 14% in Mexico, 4–10% in Canada, and 2–6% in the USA) are currently protected under four possible warming scenarios ranging from +1.5⁰C to +4⁰C. And beyond +2⁰C refugia will be valuable only if they are at high latitudes and high elevations.

The problem as many people have stated is that we are marching into an ecological trap of the greatest dimension. A combination of global climate change and continually increasing human populations and impacts are the main driving factors, neither of which are under the control of the ecological community. What ecologists and conservationists can do is work on the social-political front to protect more areas and keep analysing the dynamics of declining species in local areas. We confront major political and social obstacles in conservation ecology, but we can increase our efforts to describe how organisms interact in natural ecosystems and how we can reduce undesirable declines in populations. All this requires much more monitoring of how ecosystems are changing on a local level and depends on how successful we can be as scientists to diagnose and solve the ecological components of ecosystem collapse.

As with all serious problems we advance by looking clearly into what we can do in the future based on what we have learned in the past. And we must recognize that these problems are multi-generational and will not be solved in any one person’s lifetime. So, as we continue to march into the ultimate ecological trap, we must rally to recognize the trap and use strong policies to reverse its adverse effects on biodiversity and ultimately to humans themselves. None of us can opt out of this challenge.

There is much need in this dilemma for good science, for good ecology, and for good education on what will reverse the continuing degradation of our planet Earth. Every bit counts. Every Greta Thunberg counts.

Battin, J. (2004) When good animals love bad habitats: ecological traps and the conservation of animal populations. Conservation Biology, 18, 1482-1491.

Dinerstein, E., Vynne, C., Sala, E., et al. (2019) A Global Deal For Nature: Guiding principles, milestones, and targets. Science Advances, 5, eaaw2869.doi: 10.1126/sciadv.aaw2869..

IPCC, 2022b. In: Skea, J., Shukla, P.R., et al. (Eds.), Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of theIntergovernmental Panel on Climate Change. Cambridge University Press. doi: www.ipcc.ch/report/ar6/wg3/.

Kristan III, W.B. (2003) The role of habitat selection behavior in population dynamics: source–sink systems and ecological traps. Oikos, 103, 457-468.

Lamb, C.T., Mowat, G., McLellan, B.N., Nielsen, S.E. & Boutin, S. (2017) Forbidden fruit: human settlement and abundant fruit create an ecological trap for an apex omnivore. Journal of Animal Ecology, 86, 55-65. doi. 10.1111/1365-2656.12589.

Lindenmayer, D.B. and Taylor, C. (2020) New spatial analyses of Australian wildfires highlight the need for new fire, resource, and conservation policies. Proceedings of the National Academy of Sciences 117, 12481-124485. doi. 10.1073/pnas.2002269117.

Saunders, S.P., Grand, J., Bateman, B.L., Meek, M., Wilsey, C.B., Forstenhaeusler, N., Graham, E., Warren, R. & Price, J. (2023) Integrating climate-change refugia into 30 by 30 conservation planning in North America. Frontiers in Ecology and the Environment, 21, 77-84. doi. 10.1002/fee.2592.

Schlaepfer, M.A., Runge, M.C. & Sherman, P.W. (2002) Ecological and evolutionary traps. Trends in Ecology & Evolution, 17, 474-480.

Wilson, E.O. (2016) Half-Earth: Our Planet’s Fight for Life. Liveright, New York. ISBN: 978-1-63149-252-5.

Back to Nature vs. Nurture

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The ancient argument of ‘nature’ versus ‘nurture’ continues to arise in biology. The question has arisen very forcefully in a new book by James Tabery (Tabery 2023). The broad question he examines in this book is the conflict between ‘nature’ and ‘nurture’ in western medicine. In a broad sense ‘nature’ is discussed as the modern push in medicine to find the genetic basis of some of the common human degenerative diseases – Parkinson’s, dementia, asthma, diabetes, cancer, hypertension – to mention only a few medical problems of our day. The ‘nature’ approach to medicine in this book is represented by molecular genetics and the Human Genome Project. The ‘nurture’ approach to treating these medical conditions is via studying health outcomes in people subject to environmental contamination, atmospheric pollution, water quality, chemicals in food preparations, asbestos in buildings, and other environmental issues including how children are raised and educated. The competition over these two approaches was won very early by the Human Genome Project, and many of the resources for medicine over the last 30 years were put into molecular biology which made spectacular progress in diving into the genome of affected people and then making great promises of personalized medicine. The environmental approach to these medical conditions received much less money and was not viewed as sufficiently scientific. The irony of all this in retrospect is that the ‘nature’ or DNA school had no hypotheses about the problems being investigated but relied on the assumption that if we got enough molecular genetic data on thousands of people that something would jump out at us, and we would locate for example the gene(s) causing Parkinson’s, and then we could alter these genes with gene therapy or specific pharmaceuticals. By contrast the ‘nurture’ school had many specific hypotheses to test about air pollution and children’s health, about lead in municipal water supply and brain damage, and a host of very specific insights about how some of these health problems could be alleviated by legislation and changes in diet for example.

So, the question then becomes where are we today? The answer Tabery (2023) gives is that the ‘nature’ or molecular genetic “personalized medicine” approach has largely failed in achieving its goals despite the large amount of money invested because there is no single or small set of genes that cause specific diseases, but many genes that have complex interactions. In contrast, the ‘nurture’ school has made progress in identifying conditions that help decrease the occurrence of some of our common diseases, realizing that the problems are often difficult because they require changes in human behaviour like stopping smoking or improving diets.

All this discussion would possibly produce the simple conclusion that both “nature” and “nurture” are both involved in these complex human conditions. So, what could this medical discussion tell us about the condition of modern ecological science? I think two things perhaps. First, it is a general error to use science without hypotheses. Yet this is too often what ecologists do – gather a large amount of data that can be measured without too much prolonged effort and then try to make sense of it by applying hypotheses after the fact. And second, technology in ecology can be a benefit or a curse. Take, for example, the advances in vertebrate ecology that have come from the ability to describe the movements of individual animals in space. To have a map of hundreds of locations of an individual animal provides good natural history but does not address any specific hypothesis. Contrast this approach with that of Studd et al. (2021) and Shiratsuru et al. (2023) who use movement data to test important questions about kill rates of predators on different species of prey.

Many large-scale ecological approaches suffer from the same problem as the ‘nature’ paradigm – use ‘big science’ to measure many variables and then try to answer some important question for example about how climate change is affecting communities of plants and animals. Nagy et al. (2021) and Li et al. (2022) provide excellent examples of this approach. Schimel and Keller (2015) discuss what is needed to bring hypothesis testing to ‘big science’. Lindenmayer et al. (2018) discuss how conventional, question-driven long-term monitoring and hypothesis testing need to be combined with ‘big science’ to better ecological understanding. Pau et al. (2022) give a warning of how ‘big science’ data from airborne imaging can fail to agree with ground-based field studies in one core NEON grassland site in central USA.

The conclusion to date is that there is little integration in ecology of the equivalent of “nature” and “nurture” in medicine if in ecology we match ‘big science’ with ‘nature’ and field studies on the ground with ‘nurture’. Without that integration we risk in future another negative review in ecology like that provided now by Tabery (2023) for medical approaches to human diseases.

Lindenmayer, D.B., Likens, G.E. & Franklin, J.F. (2018) Earth Observation Networks (EONs): Finding the Right Balance. Trends in Ecology & Evolution, 33, 1-3.doi: 10.1016/j.tree.2017.10.008.

Li, D., et al. (2022) Standardized NEON organismal data for biodiversity research. Ecosphere, 13, e4141.doi:10.1002/ecs2.4141.

Nagy, R.C., et al. (2021) Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community. Ecosphere, 12, e03833.doi: 10.1002/ecs2.3833.

Pau, S., et al. (2022) Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland. Ecology, 103, e03590.doi: 10.1002/ecy.3590.

Schimel, D. & Keller, M. (2015) Big questions, big science: Meeting the challenges of global ecology. Oecologia, 177, 925-934.doi: 10.1007/s00442-015-3236-3.

Shiratsuru, S., Studd, E.K., Majchrzak, Y.N., Peers, M.J.L., Menzies, A.K., Derbyshire, R., Jung, T.S., Krebs, C.J., Murray, D.L., Boonstra, R. & Boutin, S. (2023) When death comes: Prey activity is not always predictive of diel mortality patterns and the risk of predation. Proceedings of the Royal Society B, 290, 20230661.doi.

Studd, E.K., Derbyshire, R.E., Menzies, A.K., Simms, J.F., Humphries, M.M., Murray, D.L. & Boutin, S. (2021) The Purr-fect Catch: Using accelerometers and audio recorders to document kill rates and hunting behaviour of a small prey specialist. Methods in Ecology and Evolution, 12, 1277-1287.doi. 10.1111/2041-210X.13605

Tabery, J. (2023) Tyranny of the Gene: Personalized Medicine and the Threat to Public Health. Knopf Doubleday Publishing Group, New York. 336 pp. ISBN: 9780525658207.

The Time Frame of Ecological Science

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Ecological research differs from many branches of science in having a more convoluted time frame. Most of the sciences proceed along paths that are more often than not linear – results A → results B → results C and so on. Of course, these are never straight linear sequences and were described eloquently by Platt (1964) as strong inference:

“Strong inference consists of applying the following steps to every problem in science, formally and explicitly and regularly: 1) Devising alternative hypotheses; 2) Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly as possible, exclude one or more of the hypotheses; 3) Carrying out the experiment so as to get a clean result; “Recycling the procedure, making sequential hypotheses to refine the possibilities that remain; and so on. It is like climbing a tree.” (page 347 in Platt).

If there is one paper that I would recommend all ecologists read it is this paper which is old but really is timeless and critical in our scientific research. It should be a required discussion topic for every graduate student in ecology.

Some ecological science progresses as Platt (1964) suggests and makes good progress, but much of ecology is lost in a failure to specify alternative hypotheses, in changing questions, in abandoning topics because they are too difficult, and in a shortage of time. It is the time component of ecological research that I wish to discuss in this blog.

The idea of long-term studies has always been present in ecology but was perhaps brought to our focus by the compilation by Gene Likens in 1989 in a book of 14 chapters that are as vital now as they were 34 years ago. Many discussions of long-term studies are now available to examine this issue. Buma et al. (2019) for example discuss plant primary succession at Glacier Bay, Alaska which has 100 years of data, and which illustrates in a very slow ecosystem a test of conventional rules of community development. Cusser et al. (2021) follow this by asking a critical question of how long field experiments need to be. They restrict long-term to be > 10 years of study and used data from the USA LTER sites. This question depends very much on the community or ecosystem of study. Studies in areas with a stable climate produced results more quickly than those in highly seasonal environments, and plant studies needed to be longer term than animal studies to reach stable conclusions. Ten years may not be enough.

Reinke et al. (2019) reviewed 3 long term field studies and suggest that long-term studies can be useful to allow us to predict how ecosystems will change with time. All these studies lead to three unanswered questions that are critical for progress in ecology. The first question is how we decide as a community exactly which ecological system we should be studying long-term. No one knows how to answer this question, and a useful graduate seminar could debate the utility of what are now considered model long-term studies, such as the three highlighted in Reinke et al. (2019) or the Park Grass Experiment (Addy et al. 2022). At the moment these decisions are opportunistic, and we should debate how best to proceed. Clearly, we cannot do everything for every population and community of interest, so how do we choose? We need model systems that can be applied to a wide variety of environments across the globe and that ask questions of global significance. Many groups of ecologists are trying to do this, but a host of decisions about who to fund and support in what institution are vital to avoid long-term studies driven more by convenience than by ecological importance.

A second question involves the implied disagreement whether many important questions in ecology today could be answered by short-term studies, so we reach a position where there is competition between short- and long-term funding. These decisions about where to do what for how long are largely uncontrolled. One would prefer to see an articulated set of hypotheses and predictions to proceed with decision making, whether for short-term studies suitable for graduate students or particularly for long-term studies that exceed the life of individual researchers.

A third question is the most difficult one of the objectives of long-term research. Given climate change as it is moving today, the hope that long-term studies will give us reliable predictions of changes in communities and ecosystems is at risk, the same problem of extrapolating a regression line beyond the range of the data. Depending on the answer to this climate dilemma, we could drop back to the suggestion that because we have only a poor ability to predict ecological change, we should concentrate more on widespread monitoring programs and less on highly localized studies of a few sites that are of unknown generality. Testing models with long-term data is enriching the ecological literature (e.g. Addy et al 2022). But the challenge is whether our current understanding is sufficient to make predictions for future populations or communities. Should ecology adopt the paradigm of global weather stations?

Addy, J.W.G., Ellis, R.H., MacLaren, C., Macdonald, A.J., Semenov, M.A. & Mead, A. (2022) A heteroskedastic model of Park Grass spring hay yields in response to weather suggests continuing yield decline with climate change in future decades. Journal of the Royal Society Interface, 19, 20220361. doi: 10.1098/rsif.2022.0361.

Buma, B., Bisbing, S.M., Wiles, G. & Bidlack, A.L. (2019) 100 yr of primary succession highlights stochasticity and competition driving community establishment and stability. Ecology, 100, e02885. doi: 10.1002/ecy.2885.

Cusser, S., Helms IV, J., Bahlai, C.A. & Haddad, N.M. (2021) How long do population level field experiments need to be? Utilising data from the 40-year-old LTER network. Ecology Letters, 24, 1103-1111. doi: 10.1111/ele.13710.

Hughes, B.B., Beas-Luna, R., Barner, A., et al. (2017) Long-term studies contribute disproportionately to ecology and policy. BioScience, 67, 271-281. doi: 10.1093/biosci/biw185.

Likens, G.E. (Editor, 1989) Long-term Studies in Ecology: Approaches and Alternatives. Springer Verlag, New York. 214 pp. ISBN: 0387967435.

Platt, J.R. (1964) Strong inference. Science, 146, 347-353. doi: 10.1126/science.146.3642.347.

Reinke, B.A., Miller, D.A.W. & Janzen, F.J. (2019) What have long-term field studies taught as about population dynamics? Annual Review of Ecology, Evolution, and Systematics, 50, 261-278. doi: 10.1146/annurev-ecolsys-110218-024717.

The Two Questions: So what? What next?

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Assuming that these two questions are not copyright, I wanted to explore them as a convenient part of writing a scientific or popular paper in ecology, conservation, and wildlife and fisheries management. To protect the innocent, I will not identify which of many ecological colleagues has stimulated this blog.

The first question should be addressed in every scientific paper but clearly is not if you read a random sample of the articles in many ecological journals. So what? is the critical question of exactly what current problem this paper or book will contribute to. It is the microscopic and macroscopic focus of why we do science, and it does not matter at all if it addresses a minor problem or a major catastrophe like species loss in conservation. In writing one should assume that time is the critical limiting factor in our lives, and while it is fine to be entertained by watching a movie, scientists do not read scientific papers to be entertained. Some journals demand that the abstract of every paper ends with a statement of the importance of the research findings, captured by So what? Too often these statements are weak and editors as well as granting agencies should demand more incisive statements. Asking yourself So what? can be a useful guide as you progress in your research and evaluate others.

While most scientists should agree on the findings presented in a paper or lecture, not all of them will agree about the importance of the answer to So what? What is a major and important scientific finding for some may be of minor significance to others, but the key is to remember here that science is a broad church that should be progressing on a broad front, so that differences of opinion are to be expected, and we rely on evidence to evaluate these differences of opinion. Tests of ideas that turn out to be incorrect or only partly correct must not be considered as failures. If you doubt that, interview any senior scientist in your area and ask about progress and regress during their scientific career. If you find a scientist who insists that they were correct in all their ideas, you should probably request them to go into politics to improve decision making in the real world.

The second question is probably the most critical for all scientific research. Once research is completed, there are two paths. If the original question or problem is solved or answered, the question becomes what does this work suggest needs to be done to advance the general area of research. Most typically however a research project will end up with more questions than it solves. The growing end of science is the critical one, and by asking What next? we delve deeper into the area of research to fill in details that were not evident when it was started. Read Sutherland et al. (2013, 2022) for an excellent example of this approach in conservation science. A simple example of this approach comes from many conservation problems. A particular species of bird may be thought to be declining in numbers, so the first issue is whether this is correct, and so an investigation into the changes in abundance of the species becomes the first step. This could lead to an analysis of the demography of the species population, birth, death and movement rates could be determined to isolate more precisely why abundance is changing. Given these data, the next step might be (for example) why the death rate is increasing if indeed this is the case. The next step is what management methods can be applied to reduce the death rate, and does this situation apply to other closely related species. It is important that asking What next? does not imply a linear sequence in time, and a study could be designed to address more than one question at the same time. We finish the What next? approach with a web of information and conclusions that address a broader question than the original simple question. And What next? should not be answered with a broad set of statements like “climate change is the cause” but by suggestions of very specific experiments and studies to carry investigations forward.

The result in ecology is an increasing precision of thought into ecological interactions and the processes that link species, communities, and ecosystems to very large questions such as the environmental response to climate change. Not all questions need to be large-scale because there are important local questions about the adequacy of designated parks and protected areas to protect species, communities, and ecosystems. The key message is that ecological understanding is not static but grows incrementally by well-designed research programs that by themselves seem to address only small-scale issues.

Seemingly failed research programs are not to be scorned but rather to indicate what avenues of research have not led to good insights. In a sense ecological science is like an evolutionary tree in which some branches fade away with time and others blossom into a variety of forms that surprise us all. So, my advice is to carry on asking these two simple questions in science to help sharpen your research program.

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.doi: 10.1111/1365-2745.12025.

Sutherland, W.J. & Jake M. Robinson, D.C.A., Tim Alamenciak, Matthew Armes, Nina Baranduin, Andrew J. Bladon, Martin F. Breed, Nicki Dyas, Chris S. Elphick, Richard A. Griffiths, Jonny Hughes, Beccy Middleton, Nick A. Littlewood, Roger Mitchell, William H. Morgan, Roy Mosley, Silviu O. Petrovan, Kit Prendergast, Euan G. Ritchie,Hugh Raven, Rebecca K. Smith, Sarah H. Watts, Ann Thornton (2022) Creating testable questions in practical conservation: a process and 100 questions. Conservation Evidence Journal, 19, 1-7.doi: 10.52201/CEJ19XIFF2753.

The Two Ecologies

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Trying to keep up with the ecological literature is a daunting task, and my aging efforts shout to me that there are now two ecologies that it might be worth partially separating. First, many published “ecological” papers are natural history. This is certainly an important component of the environmental literature but for the most part good observations alone are not science in the formal sense of science addressing problems and trying to solve them with the experimental approach. The information provided in the natural history literature regarding both plants and animals include their identification, where they live, what nutrients or food resources they utilize and in some cases information on their conservation status. A good foundation of natural history is needed to do good ecological research to be sure so my statements must not be misinterpreted to suggest that I do not appreciate natural history. Good natural history leads into the two parts of ecology that I would like to discuss. I call these social ecology and scientific ecology.

Social ecology flows most easily out of natural history and deals with the interaction between humans and the biota. Thus, for example, many people love birds which are ever present in both cities and countryside, are often highly colourful and vocal in our environment. Similarly, many tourists from North America visit Australia, Africa and Central America to see birds that are unique to those regions. Similar adventures are available to see elephants, bison, bears, and whales in their natural habitats. Social ecology flows into conservation biology in cases where preferred species are threatened by human changes to the landscape. The key here is that there is a mix in social ecology between human entertainment and a concern for species losses that are driven by human actions. Social ecology is mostly about people and their views of what parts of the environment are important to them. People love elephants but are little concerned about earthworms unless they bother them.

Scientific ecology should operate with a broader perspective of testing hypotheses to understand how populations and communities of animals and plants interact to produce the world as we see it. It asks about how species interactions change over time and whether they lead to environmental stability or instability. Scientific ecology has a time dimension that is much longer than that of social ecology. The focus of scientific ecology is hypothesis testing to answer problems or questions about how the biological world works. This perspective interacts strongly with climate change and human disturbances as well as natural disturbances like flooding or forest fires. While social ecology asks what is happening, scientific ecology asks why this is happening in our ecosystems. Scientific ecology allows us to determine the causal factors behind problems of change and the management approaches that might be required. While social ecology observes that migratory birds appear to be declining in abundance, scientific ecology asks exactly which bird species are at risk and what factors like food supplies, predation, or disease are the cause of the decline. And most importantly can humans change the environment to prevent species losses?

Conservation ecology has become the link between social and scientific ecology and shares elements of both approaches. Too much of social conservation biology consists of moaning and groaning about changes with little data and unverifiable speculations. As such it provides little help to solve conservation problems. When there is clear public support for issues like old growth logging, politicians often do not act ethically to follow public support because of economics or inertia. Scientific ecology has been strongly influenced by Karl Popper’s (1963) book, with much discussion today among philosophers about Popper’s approach to hypotheses within the context of our social values and objectives (Dias 2019). Lundblad and Conway (2021) provide a classic example of hypothesis testing for clutch size in birds which illustrates well the path of scientific ecology over many years from initial conjectures to more refined understanding of the original scientific question.

In a sense this ecological dichotomy is found in many of the sciences. Medicine is a good example. We can observe and describe symptoms of people dying of lung cancer, but medical scientists really wish to know what environmental causes like air pollution or cigarette smoking are producing this mortality, and whether genetic backgrounds are involved. Science is far from perfect and there are many false leads in proposals of drugs in medicine that turn out to be counterproductive to solving a particular problem. Kim and Kendeou (2021) discuss the critical question of knowledge transfer as science progresses in our society today through knowledge transfer from generation to generation.

My concern is that social ecology is replacing scientific ecology in the ecological literature so that as we are so enamoured with the beauty of nature, we forget the need to find out quantitatively what is happening and how it might be mitigated. As with medicine, talking about problems does not solve them without serious empirical scientific study.

Dias, E.A. (2019) Science as a game in Popper. Griot : Revista de Filosofia,, 19, 327-337.doi: 10.31977/grirfi.v19i3.1239. (in Portuguese; use Google Translate)

Kim, J. & Kendeou, P. (2021) Knowledge transfer in the context of refutation texts. Contemporary Educational Psychology, 67, 102002.doi: 10.1016/j.cedpsych.2021.102002.

Lundblad, C.G. & Conway, C.J. (2021) Ashmole’s hypothesis and the latitudinal gradient in clutch size. Biological Reviews, 96, 1349-1366.doi: 10.1111/brv.12705.

Popper, K.R. (1963) Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge and Kegan Paul, London. 412 pp.

On Ecological Imperialism

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It is well known among ecologists that there are more species of almost everything in the tropical regions, and it is also well known that there is rather much more research in the ecosystems of the temperate zone. A recent note in Science 379 (6632) – 8 Feb. 2023 highlights the problems faced by ornithologists in Latin America and the Caribbean trying to carry out research on their local birds. The details are in two papers now published (Soares et al. 2023, Ruelas Inzunza et al. 2023). Both of these papers are a response to a review paper published in 2020 (Lees et al. 2020) which discussed how much was not known about birds in Latin America, but which ignored most of the contributions of Latin American scientists. The red flag arose in part because all the authors of the 2020 paper were based at universities either in the United States or in the United Kingdom. The central criticisms were that the 2020 paper perpetuated an elitist, exclusionary, “northern” approach that has overlooked the knowledge produced by Latin American experts and Indigenous people, partly because these papers were not in English.

    Their case is certainly important and should be a call-to-arms but it should be read with a few minor qualifications. It is certainly not valid to ignore local knowledge both of scientists and indigenous peoples. But this has been going on now for more than 200 years in all areas of biological science, not that history justifies these barriers. Alas Charles Darwin would fall under the knife of this criticism. The funding for ecological research is higher in most European countries as well as North America compared with tropical countries. So we are dealing with economic issues that underlie the scientific funding that is less in Latin America in addition to the global problem that too many governments prefer guns to butter. We recognize these problems, but we can do nothing immediately about them.

    The language issue is much more difficult because it is so clear. There is a long history of this conflict in scientific papers as well as in literature in general. French scientists years ago refused to publish in English, that has changed. Chinese scientists were all educated in Russian but when the tide turned they learned English and started to write scientific papers in English. The problem revolves back to the education system of North American schools that seem to operate on the assumption that to learn a foreign language is very close to being a traitor. Alas students hardly learn to speak and write English but that is another social issue. I think many northern scientists have helped Latin America scientists to assist them in English usage, so it is to me quite obscene to think that someone has a business charging people $600 for a translation. So much of the complaint in the predominance of English scientific papers arises from social issues that are difficult to overcome.

    In the end I am very sympathetic with the inequities raised in these papers and the desire to move forward on all these issues. Ironically the skeleton of the Lees et al. (2020) paper is an excellent roadmap for the analysis of any taxonomic group anywhere is the world, and these papers should be a reminder that similar reviews should be more inclusive of all published literature. Remember always that European or American knowledge is not the only or the best knowledge.

Lees, A.C., Rosenberg, K.V., Ruiz-Gutierrez, V., Marsden, S., Schulenberg, T.S. & Rodewald, A.D. (2020) A roadmap to identifying and filling shortfalls in Neotropical ornithology. Auk, 137, 1-17. doi: 10.1093/auk/ukaa048.

Ruelas Inzunza, E., Cockle, K.L., Núñez Montellano, M.G., Fontana, C.S., Cuatianquiz Lima, C., Echeverry-Galvis, M.A., Fernández-Gómez, R.A., Montaño-Centellas, F.A., Bonaccorso, E., Lambertucci, S.A., Cornelius, C., Bosque, C., Bugoni, L., Salinas-Melgoza, A., Renton, K., Freile, J.F., Angulo, F., Mugica Valdés, L., Velarde, E., Cuadros, S. & Miño, C.I. (2023) How to include and recognize the work of ornithologists based in the Neotropics: Fourteen actions for Ornithological Applications, Ornithology, and other global-scope journals. Ornithological Applications, 125, duac047. doi: 10.1093/ornithapp/duac047.

Soares, L., Cockle, K.L., Ruelas Inzunza, E., Ibarra, J.T., Miño, C.I., Zuluaga, S., Bonaccorso, E., Ríos-Orjuela, J.C., Montaño-Centellas, F.A., Freile, J.F., Echeverry-Galvis, M.A., Bonaparte, E.B., Diele-Viegas, L.M., Speziale, K., Cabrera-Cruz, S.A., Acevedo-Charry, O., Velarde, E., Cuatianquiz Lima, C., Ojeda, V.S., Fontana, C.S., Echeverri, A., Lambertucci, S.A., Macedo, R.H., Esquivel, A., Latta, S.C., Ruvalcaba-Ortega, I., Alves, M.A.S., Santiago-Alarcon, D., Bodrati, A., González-García, F., Fariña, N., Martínez-Gómez, J.E., Ortega-Álvarez, R., Núñez Montellano, M.G., Ribas, C.C., Bosque, C., Di Giacomo, A.S., Areta, J.I., Emer, C., Mugica Valdés, L., González, C., Rebollo, M.E., Mangini, G., Lara, C., Pizarro, J.C., Cueto, V.R., Bolaños-Sittler, P.R., Ornelas, J.F., Acosta, M., Cenizo, M., Marini, M.Â., Vázquez-Reyes, L.D., González-Oreja, J.A., Bugoni, L., Quiroga, M., Ferretti, V., Manica, L.T., Grande, J.M., Rodríguez-Gómez, F., Diaz, S., Büttner, N., Mentesana, L., Campos-Cerqueira, M., López, F.G., Guaraldo, A.C., MacGregor-Fors, I., Aguiar-Silva, F.H., Miyaki, C.Y., Ippi, S., Mérida, E., Kopuchian, C., Cornelius, C., Enríquez, P.L., Ocampo-Peñuela, N., Renton, K., Salazar, J.C., Sandoval, L., Correa Sandoval, J., Astudillo, P.X., Davis, A.O., Cantero, N., Ocampo, D., Marin Gomez, O.H., Borges, S.H., Cordoba-Cordoba, S., Pietrek, A.G., de Araújo, C.B., Fernández, G., de la Cueva, H., Guimarães Capurucho, J.M., Gutiérrez-Ramos, N.A., Ferreira, A., Costa, L.M., Soldatini, C., Madden, H.M., Santillán, M.A., Jiménez-Uzcátegui, G., Jordan, E.A., Freitas, G.H.S., Pulgarin-R, P.C., Almazán-Núñez, R.C., Altamirano, T., Gomez, M.R., Velazquez, M.C., Irala, R., Gandoy, F.A., Trigueros, A.C., Ferreyra, C.A., Albores-Barajas, Y.V., Tellkamp, M., Oliveira, C.D., Weiler, A., Arizmendi, M.d.C., Tossas, A.G., Zarza, R., Serra, G., Villegas-Patraca, R., Di Sallo, F.G., Valentim, C., Noriega, J.I., Alayon García, G., de la Peña, M.R., Fraga, R.M. & Martins, P.V.R. (2023) Neotropical ornithology: Reckoning with historical assumptions, removing systemic barriers, and reimagining the future. Ornithological Applications, 125, duac046. doi: 10.1093/ornithapp/duac046.

Belief vs. Evidence

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There is an interesting game you could enter into if you classified the statements you hear or read in the media or in ecological papers. The initial dichotomy is whether or not a statement is a BELIEF or EVIDENCE BASED. There is a continuum between these polar opposites so there can easily be disagreements based on a person’s background. If I say “I believe that the earth is round” you will recognize that this is not a simple belief but a physical fact that is evidence-based. Consequently we use the word ‘belief’ in many different ways. If I say that “Aliens from outer space are firing ray guns to cause flooding in California and Australia”, it is unlikely that you will be convinced because there is no evidence of how this process could work.

If we listen to the media or read the news, you will hear many statements that I or we ‘believe’ that speed limits on streets should be reduced, or that certain types of firearms should be prohibited. The natural response of a scientist to such statements is to ask for what evidence is available that such actions will solve problems, and if there is no evidence, we deal only with opinions or beliefs. If  you lived several hundred years ago, you would be told that “malaria” was a disease caused by “bad air” coming from swamps and rivers, since there was no evidence at the time about microorganisms causing disease. So in a broad sense historical progress was made by people looking for ‘evidence’ to temper and test ‘beliefs’.

How does all this relate to ecological science? I would add the requirement to papers that state some conclusions in ecology journals to also state the beliefs the paper rely on to reach its conclusions, in addition to stating clear hypotheses and alternative hypotheses. Consider the simple case of random sampling, a basic requirement in all statistical methods. But almost no paper states what statistical population is being sampled, and if it does often the study plots are not placed randomly. The standard excuse to this is that our results apply to a large biome, and it is not physically possible to sample randomly, or that we get the same results whether we sample randomly or not. Whatever the excuse, we need to recognize this as a belief or an assumption, a less damning scientific term. And if this assumption is not accepted it is possible to sample other areas or with other methods to test if the evidence validates the assumption. Evidence can always be improved with enough funding, and this replication is exactly what many scientists are doing daily.

Until recently most scientists believed that CO2 was good for plants, and so the more CO2 the better. But the evidence provided was based on simple theory and short term lab experiments. Reich et al. (2018) and Zhu et al. (2018) pointed out that this was not correct when long-term studies were done on C3 plants like rice. So this is a good illustration of the progress of science from belief to evidence. And over the past 50 years it has become very clear that increased CO2 increases atmospheric temperature with drastic climatic and biodiversity consequences (Ripple et al. 2021). The result of these scientific advances is that now there is an extensive amount of scientific research giving the empirical evidence of climate change and CO2 effects on plants and animals. Most people agree with these broad conclusions, but there are people in large corporations and governments around the world who deny these scientific conclusions because they believe that climate change is not happening and is of little consequence to biodiversity or to daily life.

It is quite possible to ignore all the scientific literature about the consequences of climate change, CO2 increase, and biodiversity loss but the end result of passing over these problems now will fall heavily onto your children and grandchildren. The biosphere is screaming the message that ignorance will not necessarily lead to bliss.

Reich, P.B., Hobbie, S.E., Lee, T.D. & Pastore, M.A. (2018) Unexpected reversal of C3 versus C4 grass response to elevated CO2 during a 20-year field experiment. Science, 360, 317-320.doi: 10.1126/science.aas9313.

Ripple, W.J., Wolf, C., Newsome, T.M., Gregg, J.W., Lenton, T.M., Palomo, I., Eikelboom, J.A.J., Law, B.E., Huq, S., Duffy, P.B. & Rockström, J. (2021) World Scientists’ Warning of a Climate Emergency 2021. BioScience, 71, 894-898.doi: 10.1093/biosci/biab079.

Shivanna, K.R. (2022) Climate change and its impact on biodiversity and human welfare. Proceedings of the Indian National Science Academy, 88, 160-171.doi: 10.1007/s43538-022-00073-6.

Watson, R., Kundzewicz, Z.W. & Borrell-Damián, L. (2022) Covid-19, and the climate change and biodiversity emergencies. Science of The Total Environment, 844, 157188.doi: 10.1016/j.scitotenv.2022.157188.

Williams, S.E., Williams, S.E. & de la Fuente, A. (2021) Long-term changes in populations of rainforest birds in the Australia Wet Tropics bioregion: A climate-driven biodiversity emergency. PLoS ONE, 16.doi: 10.1371/journal.pone.0254307.

Zhu, C., Kobayashi, K., Loladze, I., Zhu, J. & Jiang, Q. (2018) Carbon dioxide (CO2) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries. Science Advances, 4, eaaq1012 doi: 10.1126/sciadv.aaq1012.

Have we moved on from Hypotheses into the New Age of Ecology?

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For the last 60 years a group of Stone Age scientists like myself have preached to ecology students that one needs hypotheses to do proper science. Now it has always been clear that not all ecologists followed this precept, and a recent review hammers this point home (Betts et al. 2021). I have always asked my students to read the papers from the Stone Age about scientific progress – Popper (1959), Platt (1964), Peters (1991) and even back to the Pre-Stone Age, Chamberlin (1897). There has been much said about this issue, and the recent Betts et al. (2021) paper pulls much of it together by reviewing papers from 1991 to 2015. Their conclusion is dismal if you think ecological science should make progress in gathering evidence. No change from 1990 to 2015. Multiple alternative hypotheses = 6% of papers, Mechanistic hypotheses = 25% of papers, Descriptive hypotheses = 12%, No hypotheses = 75% of papers. Why should this be after years of recommending the gold standard of multiple alternative hypotheses? Can we call ecology a science with these kinds of scores? 

The simplest reason is that in the era of Big Data we do not need any hypotheses to understand populations, communities, and ecosystems. We have computers, that is enough. I think this is a rather silly view, but one would have to interview believers to find out what they view as progress from big data in the absence of hypotheses. The second excuse might be that we cannot be bothered with hypotheses until we have a complete description of life on earth, food webs, interaction webs, diets, competitors, etc. Once we achieve that we will be able to put together mechanistic hypotheses rapidly. An alternative statement of this view is that we need very much natural history to make any progress in ecology, and this is the era of descriptive natural history and that is why 75% of papers do not list the word hypothesis.

But this is all nonsense of course, and try this view on a medical scientist, a physicist, an aeronautical engineer, or a farmer. The fundamental principle of science is cause-and-effect or the simple view that we would like to see how things work and why often they do not work. Have your students read Romesburg (1981) for an easy introduction and then the much more analytical book by Pearl and Mackenzie (2018) to gain an understanding of the complexity of the simple view that there is a cause and it produces an effect. Hone et al. (2023) discuss these specific problems with respect to improving our approach to wildlife management

What can be done about the dismal situation described by Betts et al. (2021)? One useful recommendation for editors and reviewers would be to request for every submitted paper for a clear statement of the hypothesis they are testing, and hopefully for alternative hypotheses. There should be ecology journals specifically for natural history where the opposite gateway is set: no use of ‘hypothesis’ in this journal. This would not solve all the Betts et al. problems because some ecology papers are based on the experimental design of ‘do something’ and then later ‘try to invent some way to support a hypotheses’, after the fact science. One problem with this type of literature survey is, as Betts et al. recognized, is that papers could be testing hypotheses without using this exact word. So words like ‘proposition’, ‘thesis’, ‘conjectures’ could camouflage thinking about alternative explanations without the actual word ‘hypothesis’.

One other suggestion to deal with this situation might be for journal editors to disallow all papers with hypotheses that are completely untestable. This type of rejection could be instructive to authors to assist rewriting your paper to be more specific about alternative hypotheses. If you can make a clear causal set of predictions that a particular species will go extinct in 100 years, this could be described as a ‘possible future scenario’ that could be guided by some mechanisms that are specified. Or if you have a hypothesis that ‘climate change will affect species geographical ranges, you are providing  a very vague inference that is difficult to test without being more specific about mechanisms, particularly if the species involved is rare.

There is a general problem with null hypotheses which state there is “no effect”. In some few cases these null hypotheses are useful but for the most part they are very weak and should indicate that you have not thought enough about alternative hypotheses.

So read Platt (1964) or at least the first page of it, the first chapter of Popper (1959), and Betts et al. (2021) paper and in your research try to avoid the dilemmas they discuss, and thus help to move our science forward lest it become a repository of ‘stamp collecting’.

Betts, M.G., Hadley, A.S., Frey, D.W., Frey, S.J.K., Gannon, D., Harris, S.H., et al. (2021) When are hypotheses useful in ecology and evolution? Ecology and Evolution, 11, 5762-5776. doi: 10.1002/ece3.7365.

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.

Hone, J., Drake, A. & Krebs, C.J. (2023) Evaluation options for wildlife management and strengthening of causal inference BioScience, 73, 48-58.doi: 10.1093/biosci/biac105.

Pearl, J., and Mackenzie, D. 2018. The Book of Why. The New Science of Cause and Effect. Penguin, London, U.K. 432 pp. ISBN: 978-1541698963.

Peters, R.H. (1991) A Critique for Ecology. Cambridge University Press, Cambridge, England. ISBN: 0521400171.

Platt, J.R. (1964) Strong inference. Science, 146, 347-353.doi: 10.1126/science.146.3642.347.

Popper, K.R. (1959) The Logic of Scientific Discovery. Hutchinson & Co., London. ISBN: 978-041-5278-447.

Romesburg, H.C. (1981) Wildlife science: gaining reliable knowledge. Journal of Wildlife Management, 45, 293-313. doi:10.2307/3807913.

Is Ecology Becoming a Correlation Science?

2 Replies

One of the first lessons in Logic 101 is classically called “Post hoc, ergo propter hoc” or in plain English, “After that, therefore because of that”. The simplest example of many you can see in the newspapers might be: “The ocean is warming up, salmon populations are going down, it must be another effect of climate change. There is a great deal of literature on the problems associated with these kinds of simple inferences, going back to classics like Romesburg (1981), Cox and Wermuth (2004), Sugihara et al. (2012), and Nichols et al. (2019). My purpose here is only to remind you to examine cause and effect when you make ecological conclusions.

My concern is partly related to news articles on ecological problems. A recent example is the collapse of the snow crab fishery in the Gulf of Alaska which in the last 5 years has gone from a very large and profitable fishery interacting with a very large crab population to, at present, a closed fishery with very few snow crabs. What has happened? Where did the snow crabs go? No one really knows but there are perhaps half a dozen ideas put forward to explain what has happened. Meanwhile the fishery and the local economy are in chaos. Without very many critical data on this oceanic ecosystem we can list several factors that might be involved – climate change warming of the Bering Sea, predators, overfishing, diseases, habitat disturbances because of bottom trawl fishing, natural cycles, and then recognizing that we have no simple way for deciding cause and effect and therefore making management choices.

The simplest solution is to say that many interacting factors are involved and many papers indicate the complexity of populations, communities and ecosystems (e,g, Lidicker 1991, Holmes 1995, Howarth et al. 2014). Everyone would agree with this general idea, “the world is complex”, but the arguments have always been “how do we proceed to investigate ecological processes and solve ecological problems given this complexity?” The search for generality has led mostly into replications in which ‘identical’ populations or communities behave very differently. How can we resolve this problem? A simple answer to all this is to go back to the correlation coefficient and avoid complexity.

Having some idea of what is driving changes in ecological systems is certainly better than having no idea, but it is a problem when only one explanation is pushed without a careful consideration of alternative possibilities. The media and particularly the social media are encumbered with oversimplified views of the causes of ecological problems which receive wide approbation with little detailed consideration of alternative views. Perhaps we will always be exposed to these oversimplified views of complex problems but as scientists we should not follow in these footsteps without hard data.

What kind of data do we need in science? We must embrace the rules of causal inference, and a good start might be the books of Popper (1963) and Pearl and Mackenzie (2018) and for ecologists in particular the review of the use of surrogate variables in ecology by Barton et al. (2015). Ecologists are not going to win public respect for their science until they can avoid weak inference, minimize hand waving, and follow the accepted rules of causal inference. We cannot build a science on the simple hypothesis that the world is complicated or by listing multiple possible causes for changes. Correlation coefficients can be a start to unravelling complexity but only a weak one. We need better methods for resolving complex issues in ecology.

Barton, P.S., Pierson, J.C., Westgate, M.J., Lane, P.W. & Lindenmayer, D.B. (2015) Learning from clinical medicine to improve the use of surrogates in ecology. Oikos, 124, 391-398.doi: 10.1111/oik.02007.

Cox, D.R. and Wermuth, N. (2004). Causality: a statistical view. International Statistical Reviews 72: 285-305.

Holmes, J.C. (1995) Population regulation: a dynamic complex of interactions. Wildlife Research, 22, 11-19.

Howarth, L.M., Roberts, C.M., Thurstan, R.H. & Stewart, B.D. (2014) The unintended consequences of simplifying the sea: making the case for complexity. Fish and Fisheries, 15, 690-711.doi: 10.1111/faf.12041

Lidicker, W.Z., Jr. (1991) In defense of a multifactor perspective in population ecology. Journal of Mammalogy, 72, 631-635.

Nichols, J.D., Kendall, W.L. & Boomer, G.S. (2019) Accumulating evidence in ecology: Once is not enough. Ecology and Evolution, 9, 13991-14004.doi: 10.1002/ece3.5836.

Pearl, J., and Mackenzie, D. 2018. The Book of Why. The New Science of Cause and Effect. Penguin, London, U.K. 432 pp. ISBN: 978-1541698963

Popper, K.R. 1963. Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge and Kegan Paul, London. 608 pp. ISBN: 978-1541698963

Romesburg, H.C. (1981) Wildlife science: gaining reliable knowledge. Journal of Wildlife Management, 45, 293-313.

Sugihara, G., et al. (2012) Detecting causality in complex ecosystems. Science, 338, 496-500.doi: 10.1126/science.1227079.