Category Archives: Conservation Biology

Big Science – Poor Data?

The big global problems of our time are climate change, human population growth, and migration. From these emerge all the others that worry us from inequality leading to poverty, regional wars, emerging diseases, and biodiversity loss. As ecologists we typically worry about climate change and biodiversity loss. We can do little directly about climate change except to change our life style and replace our do-nothing-politicians. We can have some effect on biodiversity conservation, a subject of later discussions. But the elephant in the room is always climate change, and Bill McKibben (2019) has presented us with a synopsis of a positive evaluation from the viewpoint of fossil fuels and is currently bringing out a book on these issues (McKibben 2019a).

There is much discussion of these articles in reviews such as Diamond (2019) and in the social media. The negative concerns for the future have in recent years been getting more press than the positive possibilities and these negative views may cause the public to give up and say all efforts are hopeless. But these three references from McKibben and Diamond push the possibility of a positive outcome, premised partly on the growing concern of humans to the effects of climate change and the emerging technologies in energy capture that do not depend on oil and gas. I do not wish to question these statements but rather to raise the question of where ecological scientists should fit into this picture.

If natural capital is in decline and some to many species are at risk of extinction, what should be the reaction of a young ecologist just starting their ecological career? I can see two extreme responses to the current situation. One I will call the Carry-on-Regardless approach, and the other the Mad Panic approach. The Carry-on Regardless approach believes that we as one or a few scientists have little ability to change the global paradigm of environmental destruction. Certainly, we will use our own efforts to educate and give good environmental example to all we encounter. But as a scientist the most important achievement one can make is to do good ecological science, to understand in some small way how populations and communities of organisms interact and sustain themselves at the present time. In this way we can hopefully solve some immediate practical problems but more importantly collect some critical data for the next generations of ecologists to use in understanding the changes that will go on during the next several centuries. In a simple manner, future ecologists will be able to say, ‘so this is how system X was working in 2020”. We have no way to know now how much our hard-earned knowledge will be useful to our great-grandchildren, but we press on in the hope that it will be of some help in understanding the trace of the human footprint down the ages.

The Mad Panic approach at the other extreme argues that you should stop all the research that you are doing and become an advocate to try to convince the world to change course and prevent disaster. There is no time to do research, we ought to be out there shouting from the rooftops. If you wish to work at the research end of this school of thought you should perhaps be looking for an ecological disaster (e.g. plastics in the ocean) that you can investigate to beat politicians over the head about how we must change now to prevent further disaster. There is certainly a need for this sort of action.

The problem is how to advise ecologists starting their careers. There is no simple answer, and some are better at the first approach and others at the second. The key point is that we need both, and my concern (being a Carry-on phenotype) is that we need to have clear and precise data of how the planet is changing as a prerequisite for the second approach. We do not have this now except for a few species in a few locations. We have very little long-term data on biodiversity, and we only kid ourselves if we decide that a 3-year study can be classified as a long-term study, or that a list of species in a given area tells us something about ecosystem function. Consider how long it has taken to show clear trends in climate data, or in a more news-worthy area how little economic understanding has emerged from all the detailed minute-by-minute data on the stock market over the last 70 years.

So, we end up with big questions and poor data, and somehow hope that we can model the future changes in the world’s ecosystems to give the public guidance. To achieve this goal, we need more Carry-on Regardless ecologists doing good work and fewer, less strident Mad Panic environmentalists. Environmental warning bells are certainly going off, and we should listen to them and try to gather the data necessary to understand what is happening and how good management might counter negative environmental trends. It is good to be optimistic, but we must couple our optimism with strong ecological studies to understand how communities and ecosystems function. And we are a long way from having enough of these basic studies to be confident of future projections to guide the next generations.

Diamond, Jared. 2019. Striking a balance between fear and hope on climate change. New York Times, 15 April 2019.

McKibben, Bill. 2019. A Future Without Fossil Fuels? New York Review of Books, April 4, 2019, pp.

McKibben, Bill. 2019a. Falter: Has the Hunan Game Begun to Play Itself Out? Henry Holt and Company, 291 pp. ISBN:13: 9781250178268

Is Conservation Ecology Destroying Ecology?

Ecology became a serious science some 100 years ago when the problems that it sought to understand were clear and simple: the reasons for the distribution and abundance of organisms on Earth. It subdivided fairly early into three parts, population, community, and ecosystem ecology. It was widely understood that to understand population ecology you needed to know a great deal about physiology and behaviour in relation to the environment, and to understand community ecology you had to know a great deal about population dynamics. Ecosystem ecology then moved into community ecology plus all the physical and chemical interactions with the whole environment. But the sciences are not static, and ecology in the past 60 years has come to include nearly everything from chemistry and geography to meteorological sciences, so if you tell someone you are an ‘ecologist’ now, they have only a vague idea of what you do.

The latest invader into the ecology sphere has been conservation biology so that in the last 20 years it has become a dominant driver of ecological concerns. This has brought ecology into the forefront of publicity and the resulting political areas of controversy, not necessarily bad but with some scientific consequences. ‘Bandwagons’ are for the most part good in science because it attracts good students and professors and brings public support on side. Bandwagons are detrimental when they draw too much of the available scientific funding away from critical basic research and champion scientific fads.

The question I wish to raise is whether conservation ecology has become the latest fad in the broad science of ecology and whether this has derailed important background research. Conservation science begins with the broad and desirable goal of preserving all life on Earth and thus thwarting extinctions. This is an impossible goal and the question then becomes how can we trim it down to an achievable scientific aim? We could argue that the most important goal is to describe all the species on Earth, so that we would then know what “money” we have in the “bank”. But if we look at the insects alone, we see that this is not an achievable goal in the short term. And the key to many of these issues is what we mean by “the short term”. If we are talking10 years, we may have very specific goals, if 100 years we may redesign the goal posts, and if 1000 years again our views might change.

This is a key point. As humans we design our goals in the time frames of months and a few years, not in general in geological time. Because of climate change we are now being forced to view many things in a shorter and shorter time frame. If you live in Miami, you should do something about sea level rise now. If you grow wheat in Australia, you should worry about decreasing annual rainfall. But science in general does not have a time frame. Technology does, and we need a new phone every year, but the understanding of cancer or the ecology of tropical rain forests does not have a deadline.

But conservation biology has a ticking clock called extinction. Now we can compound our concerns about climate change and conservation to capture more of the funding for biological research in order to prevent extinctions of rare and endangered species. 

Ecological science over the past 40 years has been progressing slowly through population ecology into community and ecosystem ecology while learning that the details of populations are critical to the understanding of community function and learning how communities operate is necessary for understanding ecosystem change. None of this has been linear progress but rather a halting progression with many deviations and false leads. In order to push this agenda forward more funding has clearly been needed because teams of researchers are needed to understand a community and even more people to study an ecosystem. At the same time the value of long-term studies has become evident and equipment has become more expensive.

We have now moved into the Anthropocene in which in my opinion the focus has shifted completely from trying to answer the primary problems of ecological science to the conservation of organisms. In practice this has too often resulted in research that could only be called poor population ecology. Poor in the sense of the need for immediate short-term answers for declining species populations with no proper understanding of the underlying problem. We are faced with calls for funding that are ‘crying wolf’ with inadequate data but heartfelt opinions. Recovery plans for single species or closely related groups focus on a set of unstudied opinions that may well be correct, but to test these ideas in a reliable scientific manner would take years. Triage on a large scale is practiced without discussing the issue, and money is thrown at problems based on the publicity generated. Populations of threatened species continue to decline in what can only be described as failed management. Blame is spread in all directions to developers or farmers or foresters or chemical companies. I do not think these are the signs of a good science which above all ought to work from the strength of evidence and prepare recovery plans based on empirical science.

Part of the problem I think lies in the modern need to ‘do something’, ‘do anything’ to show that you care about a particular problem. ‘We have now no time for slow-moving conventional science, we need immediate results now’. Fortunately, many ecologists are critical of these undesirable trends in our science and carry on (e.g. Amos et al. 2013). You will not likely read tweets about these people or read about them in your daily newspapers. Evidence-based science is rarely quick, and complaints like those that I give here are not new (Sutherland et al. 2004, Likens 2010, Nichols 2012).  

Amos, J.N., Balasubramaniam, S., Grootendorst, L. et al. (2013). Little evidence that condition, stress indicators, sex ratio, or homozygosity are related to landscape or habitat attributes in declining woodland birds. Journal of Avian Biology 44, 45-54. doi: 10.1111/j.1600-048X.2012.05746.x

Likens, G.E. (2010). The role of science in decision making: does evidence-based science drive environmental policy? Frontiers in Ecology and the Environment 8, e1-e9. doi: 10.1890/090132

Nichols, J.D. (2012). Evidence, models, conservation programs and limits to management. Animal Conservation 15, 331-333. doi: 10.1111/j.1469-1795.2012.00574.x

Sutherland, W.J., Pullin, A.S., Dolman, P.M., Knight, T.M. (2004). The need for evidence-based conservation. Trends in Ecology and Evolution 19, 305-308. doi: 10.1016/j.tree.2004.03.018

How to do Conservation Planning

The biota of the Earth is in trouble because of human activities, and the question conservation people ask is what should we do about it? Conservation planning has become the key way to proceed, but its implementation has unleased an unholy row of the best way to proceed. In a sense conservation planning is like city planning in having to make decisions about what to do where. There are two broad approaches to conservation: focus on species-at-risk and their needs and plan accordingly to protect them. Alternatively focus on ecosystems and protect them without all the detailed knowledge that is required for protecting species-at-risk. This is the first source of conflict because the public at large falls in love with species, so polar bears and blue whales and tigers are an easy sell to obtain funding from private and government sources. This is at present the dominant force in conservation, and if there is enough information available the conservation of polar bears and tigers will act as “umbrella species” to protect many other species at possible risk. So single species conservation can perhaps impinge on ecosystem conservation if we designate national parks or protected areas for the charismatic species that we all admire.

But there are many other species out there that conservation biologists are concerned about, collectively labeled biodiversity. Perhaps we should conserve biodiversity instead of focusing on individual species. But right away we run into two problems. In any ecosystem many of the species present are undescribed so we cannot put a Latin name on them, or if they are described we know almost nothing about their function in the ecosystem. So, we have a key question: do we need to understand ecosystem dynamics before we can prioritize biodiversity conservation? Most ecologists believe that many of the species in any ecosystem could disappear with little effect on ecosystem functioning. The arguments are largely about which species can we dispense with, and true conservation advocates say that all must be saved.

The problem is that conservation planning is a resource allocation problem – where do we put our money (Gerber et al. 2018)? Ideas about how best to establish rules for setting aside critical areas for conservation go back to Pressey and Nicholls (1989) and in subsequent years the conservation planning literature has exploded (e.g. Margules and Pressey 2000). Techniques for conservation planning were first designed to maximize the number of species retained in the reserve, and this was the first of many problems. You had to have ‘empty’ land to set aside in the proposed reserve and you had to know the species that the reserve would protect. This was perhaps more possible in Australia or Canada but difficult to implement in the USA and Europe where much land was in private hands.

Conservation planning involves a set of difficult hurdles. If we concentrate on single species, we may well find that protected areas are in the wrong place (Mason et al. 2018). If we concentrate on ecosystems, we must decide on which ecosystems containing which species, and make this decision when the ecosystems are poorly documented and changing with climate change. All this planning must take place in the public arena where people and their elected politicians are trying to decide whether it is more important to protect large charismatic species like caribou or zebras rather than a few small butterflies. In general, all these decisions are made in a near-absence of ecological concerns about predator-prey interactions, competition, movements, or disease threats, the factors individual ecologists spend their lives studying. Much discussion focuses on critical habitat for a favoured species, and, given that we can define critical habitat for our favoured species, how much is needed and how much will it cost (Gerber et al. 2018). By 2015, critical habitat had been legally identified for only 45% of listed species in the United States, 13% in Canada and less than 1% in Australia (Martin et al. 2017, Bird and Hodges 2017). Once we have identified ‘critical habitats’ for a threatened species, we need to be sure it is not an ecological trap (Battin 2004, Camaclang et al. 2014, Lamb et al. 2017), or that the data we have is not related to the data we need for conservation planning (Dallas and Hastings 2018).

Given all these problems, many efforts are underway to plan conservation areas, particularly in the marine realm (Edgar et al. 2014, Mason et al. 2018). What is necessary now is follow up by careful monitoring the population and ecosystem changes in areas that are set aside for conservation. Without monitoring we will lack an early-warning system to pick up mistakes and try to correct them (Lindenmayer et al. 2018).  

Álvarez-Romero JG, Mills M, Adams VM, Gurney GG, Pressey RL (2018). Research advances and gaps in marine planning: towards a global database in systematic conservation planning. Biological Conservation 227, 369-382. Doi: 10.1016/j.biocon.2018.06.027

Battin J (2004). When good animals love bad habitats: ecological traps and the conservation of animal populations. Conservation Biology 18, 1482-1491. Doi: 10.1111/geb.12820

Bird SC, Hodges KE (2017). Critical habitat designation for Canadian listed species: Slow, biased, and incomplete. Environmental Science & Policy 71, 1-8. Doi: 10.1016/j.envsci.2017.01.007

Camaclang AE, Maron M, Martin TG, Possingham HP (2015). Current practices in the identification of critical habitat for threatened species. Conservation Biology 29, 482-492. Doi: 10.1111/cobi.12428

Dallas, T.A. & Hastings, A. (2018) Habitat suitability estimated by niche models is largely unrelated to species abundance. Global Ecology and Biogeography, 27, 1448-1456. Doi: 10.1111/geb.12820

Edgar GJ, et al. (2014). Global conservation outcomes depend on marine protected areas with five key features. Nature 506, 216-220. Doi: 10.1038/nature13022

Gerber LR, et al. (2018). Endangered species recovery: A resource allocation problem. Science 362, 284-286. Doi: 10.1126/science.aat8434

Lamb CT, Mowat G, McLellan BN, Nielsen SE & 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 DB, Likens GE, Franklin JF (2018). Earth Observation Networks (EONs): Finding the Right Balance. Trends in Ecology & Evolution 33, 1-3. Doi: 10.1016/j.tree.2017.10.008

Margules CR, Pressey RL (2000). Systematic conservation planning. Nature 405, 243-253. Doi: 10.1038/35012251

Pressey RL, Nicholls AO (1989). Application of a numerical algorithm to the selection of reserves in semi-arid New South Wales. Biological Conservation 50, 263-278. Doi: 10.1016/0006-3207(89)90013-X

Martin TG, Camaclang AE, Possingham HP, Maguire LA, Chades I (2017). Timing of protection of critical habitat matters. Conservation Letters 10, 308-316. Doi: 10.1111/conl.12266

Mason C, et al. (2018). Telemetry reveals existing marine protected areas are worse than random for protecting the foraging habitat of threatened shy albatross (Thalassarche cauta). Diversity & Distributions 24, 1744-1755. Doi: 10.1111/ddi.12830

Why do Scientists Reinvent Wheels?

We may reinvent wheels by repeating research that has already been completed and published elsewhere. In one sense there is no great harm in this, and statisticians would call it replication of the first study, and the more replication the more we are convinced that the results of the study are robust. There is a problem when the repeated study reaches different results from the first study. If this occurs, there is a need to do another study to determine if there is a general pattern in the results, or if there are different habitats with different answers to the question being investigated. But after a series of studies is done, it is time to do something else since the original question has been answered and replicated. Such repeated studies are often the subject of M.Sc. or Ph.D. theses which have a limited 1-3-year time window to reach completion. The only general warning for these kinds of replicated studies is to read all the old literature on the subject. There is a failure too often on this and reviewers often notice missing references for a repeated study. Science is an ongoing process but that does not mean that all the important work has been carried out in the last 5 years.

There is a valid time and place to repeat a study when the habitat for example has been greatly fragmented or altered by human land use or when climate change has made a strong impact on the ecosystem under study. The problem in this case is to have an adequate background of data that allows you to interpret your current data. If there is a fundamental problem with ecological studies to date it is that we have an inadequate baseline for comparison for many ecosystems. We can conclude that a particular ecosystem is losing species (due to land use change or climate) only if we know what species comprised this ecosystem in past years and how much the species composition fluctuated over time. The time frame desirable for background data may be only 5 years for some species or communities but for many communities it may be 20-40 years or more. We are too often buried in the assumption that communities and ecosystems have been in equilibrium in the past so that any fluctuations now seen are unnatural. This time frame problem bedevils calls for conservation action when data are deficient.

The Living Planet Report of 2018 has been widely quoted as stating that global wildlife populations have decreased 60% in the last 4 decades. They base their analysis on the changes in 4000 vertebrate species. There are about 70,000 vertebrate species on Earth, so this statement is based on about 6% of the vertebrates. The purpose of the Living Planet Report is to educate us about conservation issues and encourage political action. No ecologist in his or her right mind would question this 60% quotation lest they be cast out of the profession, but it is a challenge to the graduate students of today to analyze this statistic to determine how reliable it is. We all ‘know’ that elephants and rhinos are declining but they are hardly a random sample. The problem in a nutshell is that we have reliable long-term data on perhaps 0.01% or less of all vertebrate species. By long term I suggest we set a minimal limit of 10 generations. As another sobering test of these kinds of statements I suggest picking your favorite animal and reading all you can on how to census the species and then locate how many studies of this species meet the criteria of a good census. The African elephant could be a good place to start, since everyone is convinced that it has declined drastically. The information in the Technical Supplement is a good starting point for a discussion about data accuracy in a conservation class.

My advice is that ecologists should not without careful thought repeat studies that have already been carried out many times on common species . Look for gaps in the current wisdom. Many of our species of concern are indeed declining and need action but we need knowledge of what kinds of management actions are helpful and possible. Many of our species have not been studied long enough to know if they are under threat or not. It is not helpful to ‘cry wolf’ if indeed there is no wolf there. We need precision and accuracy now more than ever.

World Wildlife Fund. 2018. Living Planet Report – 2018: Aiming Higher. Grooten, M. and Almond, R.E.A.(Eds). WWF, Gland, Switzerland. ISBN: 978-2-940529-90-2.

Blaming Climate Change for Ecological Changes

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

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

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

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

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

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

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

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

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

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

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

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

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


On Culling Overabundant Wildlife

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

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

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

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

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

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

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

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

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

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

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

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

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

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

On the Loss of Large Mammals

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

On Detecting Rare Species with Camera Trapping

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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


Seven Prescriptive Principles for Ecologists

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

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

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

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

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

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

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

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

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

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

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


Three Approaches to Ecology

I ask the question here why ecology is not appreciated as a science at a time when it is critical to the survival of the existing world. So the first question we need to answer is if this premise is correct. I offer only one example. A university zoology department has recently produced a discussion paper on its plans for faculty recruitment over the next 15 years. This document does not include the word “ecology” in any of its forward planning. Now it is probably not unusual for biology or zoology departments in major universities to downplay ecology when there is so much excitement in molecular biology, but it is an indicator that ecology is not a good place to put your money and reputation as you await a Nobel Prize. So if we can accept the initial premise that ecology is not appreciated, we might ask why this situation exists, a point raised long ago by O’Connor (2000). Here are a few thoughts on the matter.

There are three broad approaches to the science of ecology – theoretical ecology, empirical ecology, and applied ecology. These three areas of ecology rarely talk to each other, although one might hope that they could in future evolve into a seamless thread of science.

Theoretical ecology deals with the mathematical world that has too often only a tangential concern with ecological problems. It has its own journals and a whole set of elegant discussions that have few connections to the real world. It is most useful for exploring what might be if we make certain mathematical assumptions. It is without question the most prestigious part of the broad science of ecology, partly because it involves elegant mathematics and partly because it does not get involved in all the complexities of real-world ecological systems. It is the physics of ecology. As such it carries on in its own world and tends to be ignored by most of those working in the other two broad areas of ecology.

Empirical ecology has set itself the task of understanding how the natural world works at the level of individuals, populations, communities and ecosystems. In its pure form it does not care about solving practical ecological or environmental problems, but its practitioners assume probably correctly that the information they provide will in fact be useful now or in the future. It seeks generality but rarely finds it because all individuals and species differ in how they play the ecological game of survival. If it has a mantra, it is “the devil is in the details”. The problem is the details of empirical ecology are boring to politicians, business people, and to much of the television generation now operating with a 7 second or 140 character limit on concentration.

Applied ecology is where the action is now, and if you wish to be relevant and topical you should be an applied ecologist, whether a conservation biologist, a forester, or an agricultural scientist. The mantra of applied ecologists is to do no harm to the environment while solving real world problems. Applied ecologists are forced to put the human imprint into empirical ecology, so they are very much concerned with declining populations and extinctions of plants and animals. The main but not the sole impact of humans is on climate change, so much of applied ecology traces back to the impacts of climate change on ecosystems, all added to by the increasing human population with its rising expectations. But applied ecologists are always behind the environmental problems of the day because the issues multiply faster than possible solutions can be evaluated. This ought to make for high employment for applied ecologists but in fact the opposite seems to be happening because governments too often avoid long-term problems beyond their 4-year mandate. If you do not agree, think climate change.

So, the consequence is that we have three independent worlds out there. Applied ecologists are too busy to apply the successful paradigms of empirical ecology to their problems because they are under strict time limits by their line managers who need to suggest immediate action on problems. They must therefore fire off solutions like golf balls in all directions, hoping that some might actually help solve problems. Empirical ecologists may not be helpful for applied ecologists if they are overwhelmed by the details of their particular system of study and are constrained by the ‘publish or perish’ mentality of the granting agencies.

Finally, we lay on top all this a lack of funding in the environmental sciences for investigating and solving both immediate and long-term ecological problems. And I am back to my favourite quote in the ecological literature:

“Humans, including ecologists, have a peculiar fascination with attempting to correct one ecological mistake with another, rather than removing the source of the problem.” (Schindler 1997).

What can we do about this? Three things. Pressure our politicians to increase funding on long-term environmental problems. This will provide the person-power to find and test solutions to our known problems. Vote with your ballot and your feet to improve sustainability. And whether you are young or old strive to do no harm to the Earth. And if all this is too difficult, take some practical advice not to buy a house in Miami Beach, or any house near the beach. Do something for the environment every day.


O’Connor, R.J. (2000) Why ecology lags behind biology. The Scientist 14(20):35. (October 16, 2000).

Schindler, D.W. (1997) Liming to restore acidified lakes and streams: a typical approach to restoring damaged ecosystems? Restoration Ecology 5:1-6