The fork in the road: science versus denialism and conspiracy theories

The world is awash in information. Never before have people had as much access to humanity’s collective knowledge as we do today. You want to know when the Normans conquered England? How many people use Weibo? Or what Machu Picchu would have looked like in its glory days? Simply pull out your phone and ask Siri. 

 

This cornucopia of knowledge should mean that people are in the position to make the best decisions possible. From the insurance plans that best fit their needs to voting for candidates or political parties that support policies that return optimal outcomes for individuals and society as a whole. Beyond individuals, this wealth of information should mean that evidence-based policy would be easy to pursue and outcomes for nations continually improving.

 

However, this is clearly not the case. The availability of knowledge doesn’t mean that evidence, fact and truth are utilized. Preconceived belief and ideology are important filters through which evidence is evaluated. Yet, what is really disheartening about the use of knowledge and evidence is how others (individuals and organizations) with political and economic agendas filter and manipulate what is channelled to various audiences.

 

While we might naively refer to the modern era as one based on information and the democratization of evidence and knowledge, the reality is that we live in the era of disinformation. Disinformation is the active and knowing creation and spread of false information, like politicians saying a fair election was stolen. Misinformation is the cancerous offspring of disinformation, where this false information is shared by those unaware of its nefarious origins. Disinformation and misinformation have the power to derail robust democracies and motivate atrocities.

 

The study of the origins and valuation of knowledge is a complex, convoluted and challenging area to say the least. But it is not esoteric nor just academic. Knowledge and understanding are the cornerstone of societal well-being, technological development and ultimately underpin democracy. Public policy driven by misinformation and dismissal of basic facts is simply ill-equipped to deal with many of the problems we face. This is easily showcased by the dismal, and frankly embarrassing, chaotic COVID-19 response in the United States -a clear failure for proponents of evidence-based policy.

 

Knowledge and belief arise through a number of different endeavours that span social influences, logic and reasoning, and, importantly, the empirical claims of science. Science is the process by which we assess testable claims about the world. Scientists use accumulated knowledge and evidence to formulate questions or predictions and then ultimately assess these against experiments and observation. We commonly ascribe science to the scientific method, but what scientists actually do and how they go about developing explanations and testing them is actually quite a bit more complicated. Philosophers of science, from Popper to Kuhn to Lakatos and on to Lauden have argued about what demarcates science from other knowledge-gaining exercises and these debates have, in some ways, been mired by the reliance on a scientific method that may or may not exist (see Lee McIntyre’s The Scientific Attitude for a wonderful overview).

 

The best way to think about science is to use McIntyre’s lead, where science is both a process and worldview. It is a process because it has rules in place to guide how we assess claims about the world. Perhaps more importantly, as a worldview that scientists subscribe to, we are willing to test our explanations against fair and unbiased evidence and are willing to alter our belief in light of countervailing evidence. Explanation and belief are constantly assessed and refined, or in some cases completely dropped, because we allow the real world to correct us. I’ve certainly gone through this process and have changed my thinking about the theories that I work on. More than once.

 

 

As the figure above indicates, there are multiple avenues to gain knowledge and empirical science is one of them. I take a broad view of science, so that it would include a lot of what is done in social sciences. Economics, for example, can certainly answer the question, based on more than 80 years of empirical evidence from neoliberal policies, of whether tax cuts or infrastructure investment result in greater economic growth (it’s the latter).

 

Science is one route to knowledge, insight and introspection about ourselves and our place in the universe. However, on matters of the observable world, it is the most important. Science starts with testable questions which necessitate the collection and assessment of evidence (‘facts’), but something can go wrong here. People who don’t follow the rules of science (like objectivity, honesty and transparency) and have a pre-ordained conclusion can simply use only evidence that confirms their belief (confirmation bias) while downplaying damning evidence that shoots their theory full of holes (refutation bias). Once we hit this fork, we go down the path to denialism, pseudoscience and conspiracy theory.

 

We throw around these last three terms a lot when talking about anti-science and anti-fact movements like QAnon, anti-vaccine movements and flat-Earth proponents, but they are not actually synonyms. Though these three terms are clearly interrelated, and many irrational movements invoke all three.

 

Denialism refers to the refusal to believe empirical evidence that casts doubt on one’s belief or ideology. No amount of negative evidence can change the mind of an adherent. Positive evidence is given extremely high weight, often without critically examining the origins of evidence.  But evidence is often not an important ingredient, it is just convenient when it reinforces belief. 

 

Pseudoscience uses the language of science and even purports to uses empirical evidence and experimentation. However, the preferred explanation is assumed to be true, and all that is required is the evidence support it. Opposing explanations are assumed to be wrong, regardless of empirical support. A classic example was the shift from young-Earth creationism (which usually fell firmly in the Denialism camp) to intelligent design (ID). ID attempted to avoid the language of creationism and instead used technical-sounding concepts like ‘irreducible complexity’ to conclude that a creator was a necessary ingredient to explain life. Unfortunately, for ID, proponents’ claims have not been able to withstand rigorous testing, but proponents will still cling to fragile evidence to support their beliefs. 

 

Finally, conspiracy theory has much in common with denialism, and it can be argued that you need to be a denialist in order to truly be a conspiracy theorist. However, in order to support their claims, they go a step further and see a vast collusion of nefarious actors whose primary agenda is to undermine the ‘truth’. Take for example the recent claims of election fraud in the USA. Adherents to this conspiracy theory are willing to believe that dead dictators, Democrat leaders and a vast network of thousands of election volunteers are all part of an organized attempt to change the outcome of an election. Without e-mails. Or social media posts. Or any other evidence. Compare this to the fact that average people could easily figure out the identities of members of the mob that stormed Congress because of extensive social media threads and verbal communication with friends and neighbours. This strange juxtaposition can only lead us to one of two conclusions. Either there was no election rigging conspiracy, or those who stormed the Capital are idiots and the thousands of election stealers are just so much smarter.

 

In all three of these cases, some form of authority or ideology is given more weight than reality. I have a couple of hypotheses why this happens, and especially in the USA, where the nationalistic hubris creates a large gap between the belief about how great one is compared to their reality, and so instead of accepting reality, feelings and scapegoats trump fact.

 

The dismissal of evidence has become commonplace in political life. No one said it better than Newt Gringerich. He basically says that conservative voters believe America is more dangerous today than in the past, and when the newsperson confronts him with the fact that crime has been on a downward trajectory for a long time and that we are statistically safer today than a couple of decades ago, he responds that ‘Liberals’ might have facts that are theoretically true, but his facts are true too. Remember Sean Spicer’s ‘alternative facts’, and this thinking has been around for a while. Have conclusion, need fact.

 

Christmas day 2020, Wisconsin pharmacist Steven Brandenburg purposely destroyed hundreds of dosesof the Moderna COVID19 vaccine. Turns out that Mr. Brandenburg believes that the world is flat and that the Moderna vaccine was designed to harm people and also includes microchips for tracking. While we might chuckle at the absurdity of these believes, there is a deeper, more troubling issue at play. Mr. Brandenburg is a pharmacist. Meaning that he not only has scientific training, but also needs to make evidence-based decisions to help patients. As a supposedly scientifically literate person, he could have easily devised ways of testing his claims. For example, take a plane to Asia, then another to Europe, and then back to the USA. There, the world is not flat. As for microchips in vaccines, a simple compound microscope ought to be enough to observe these.

 

So, if a pharmacist is not willing to put the effort into testing easily refutable claims, why would we expect our bank teller, auto mechanic or Ted Cruz? This goes to the core of the problem. Given the politics of Truth and fact, science and scientists no longer have any authority for many people. In fact, just being a scientist might be enough to get you dismissed as an agenda peddler or a member of some number of absurd conspiracy theories.

 

There is no doubt that vaccines have saved more lives than almost any other medical technology. Yet no other medical treatment or intervention has elicited more skepticism and outright rage than vaccines. And yet there is no rational reason for this, the evidence is very clear. But, there is a denialist and alarmist reason that plays on parents’ anxiety about the health of their children and mistrust of science. 

 

In 1998, Andrew Wakefield published a paper in the prestigious journal, Lancet, in which he reported a link between MMR vaccines and autism. This paper should have never been published. It was based on a sample size of 12 children, and from which there was evidence that Wakefield altered data and records. This paper was retracted by the journal, which is pretty much the worst public humiliation a scientist can experience. It is a recognition that you broke the sacred rules of science and it is a shame you wear for the rest of your career. Despite this public shaming, non-scientific audiences gravitated to his messaging in books and paid lectures.

 

Today, many thousands of people believe that vaccines are bad for children and might cause autism. Of course, these same people would probably have no problem taking antibiotics for an infection, receiving chemotherapy for cancer or eating a hotdog when hungry, despite the fact they probably can’t tell you what exactly is in these. Why vaccines? That is an interesting question. Maybe it’s just serendipity that this was the fraud target of Wakefield, or maybe it’s because of the violation of having a needle pierce your skin, or maybe it is because of the undeniable success of vaccines.

 

This vaccine denialism not only resulted in the re-emergence of nearly eradicated childhood diseases in places like Paris and Los Angeles, but it wasted money and time that could have been put to better use research new therapies. The response required ever increasing numbers of studies to show that there were no links between vaccines and autism. In one of the largest assessments, Anders Hviid and colleagues examined and analyzed the health records of more than 650,000 Danish children for more than 10 years and they simply didnot find any links between MMR vaccines and autism.

 

If you happen to be one that doubts the safety and efficacy of vaccines, ask yourself why, and where you are getting your information from. Then ask yourself if you were, unfortunately, diagnosed with cancer, would you trust your doctor’s request that you start radiation or chemotherapy? If so, despite not really understanding what constitutes ‘chemotherapy’, you’d trust your doctor's knowledge and expertise. Why would you dismiss this same doctor when it came to vaccine advice? You can’t have it both ways, that is irrational.

 

So, where does this leave us? In a quagmire for sure. But it also means that those of us who practice, use or teach empirical science have the knowledge and scientific understanding to engage in dialogues about important issues, whether that is about climate change or vaccines. It doesn’t mean we need to be political (but we should engage with political structures), and we don’t need to be dismissive. We can ask questions to understand peoples’ mistrust or where they are getting their information from. I find that the best way to engage is to be affirmational and dispassionate (which can be hard for me). I recently engaged in a conversation with someone who wasn’t going to get a COVID vaccine and asked a bunch of ‘why’ questions and then started my statements with phrases like “I can understand why you’d be unsure…” and I laid out the medical and public health facts about vaccines.

 

The only way to counter disinformation is with the light of evidence. Not everyone will abandon their conspiracy theories, but many have been fed misinformation, and scientific understanding and fact can really help people make better decisions for themselves.

The Gender-Biased Scientist: Women in Science

Guest post by Maika Seki, MEnvSci Candidate in the Professional Masters of Environmental Science program at the University of Toronto-Scarborough

In November of 2017, Nature Ecology & Evolution published “100 articles every ecologist should read” by Courchamp and Bradshaw, sparking a social media outrage. Rightfully so, because the list of first authors only included two women. There remains a pervasive perception that women lack the skills to practice science, and that there simply are not enough women in the field for them to have made a significant contribution, referring to the male-dominated history of the sciences. Many of us have come across studies highlighting gender bias in science education - which people have attempted to use to explain gender gaps in STEM fields. However in 2011, neuroscientist Melissa Hines found no significant difference between the mathematical, spatial, and verbal skills of boys and girls. But of course that finding did not receive much attention. In light of the emerging discourse of vital inclusivity in science, now is the time to confront our own social biases with the goal of achieving gender equity in the scientific community.

Instead of rehashing these outdated arguments, why don’t we talk about the barriers that women face in science? Why don’t we talk about the sexism in the publishing and peer-review process? In 2015, evolutionary geneticist Fiona Ingleby submitted a research paper to PLOS ONE, where the peer-reviewer suggested that she work with male biologists in order to strengthen the study, stating, “It would probably … be beneficial to find one or two male biologists to work with (or at least obtain internal peer review from, but better yet as active co-authors).” The under-recognition of women scientists has been so rampant in the fabric of science that it has been coined the Matilda effect; named after the first women scientist to comment on the phenomenon, Matilda Jocelyn Gage.
   
Why don’t we talk about the barriers women face in accessing employment in science, even while possessing the same qualifications as their male counterparts? At Yale University, a study was conducted wherein over 100 scientists assessed a resume for a job posting. The only difference between the resumes were the names; half of them were given recognizably male names, and the other half recognizably female names. The resumes submitted under the female names were deemed significantly less competent and employable, and were offered lower salaries. Clearly there is work to be done.

And then there was Tim Hunt, a Nobel laureate who made outright sexist comments at the World Conference of Science Journalists stating, “Let me tell you about my trouble with girls … three things happen when they are in the lab … You fall in love with them, they fall in love with you and when you criticize them, they cry.” Twitter responded with the hashtag #DistractinglySexy, where women scientists shared unglamorous photos of them doing their research work. Hunt subsequently resigned from his honorary post at the University College-London. We may think that this is an exceptional and isolated event, but studies show that we are not immune to these kinds of social forces of gender discrimination, even if we like to think so — especially as scientists. These seemingly minor micro-aggressions translate to devastating and tangible effects, such as the gender pay gap. 

Photo by @STEPHEVZ43 on Twitter, as a response to Tim Hunt’s sexist comments.

-Source: http://www.bbc.com/news/blogs-trending-33099289

Within scientific fields, we like to pride ourselves in being as close to bias-free as possible with our empirical, quantitative, and reproducible data. But scientists are people, and as such, we must confront the cultural and social influences that may permeate our objectivity. As scientists, we do not like to admit to this. But if we are going to arrive as close to the truth as possible, we need to capitalize on the emerging discourse of gender issues in science.
    
As of 2015, Canadian women represented only 22% of the STEM workforce. Not only are women under-represented in the workforce despite 62% of undergraduate students being women, but they are under-compensated. According to Statistics Canada, the wage gap persists across all fields, with the women median income of a bachelor’s degree being $68,342, and $82,083 for men. This is not a “third world” problem. This is a global issue. It is indisputable that there are systemic barriers that women face when pursuing careers in science. So why can’t scientists consider the confounding social factors at play that create these patterns? In science when somebody denies a phenomenon after many analyses point to the same mechanism, we would likely consider that as being irrational. With this in mind, is the denial of gender bias in science not irrational? By acknowledging these biases and promoting change, we take aim at the lack of objectivity in the discipline of science. It should also be encouraged to confront the sexism, racism, and all other intersectionalities of power imbalance within the science community. Some may argue that there is no place for politics in science, but we must face the reality that the two can not be separated. Addressing the sexism would bring us better, more balanced science. 

Statistics Canada graph on the Canadian men and women in STEM fields.

-Source: http://www.macleans.ca/society/science/gender-inequality-in-the-sciences-its-still-very-present-in-canada/

How can we aspire towards a world of innovation and ground-breaking research when roughly half of the population is held back? And how can we address it? To start, we need to hold institutions more accountable. It is disheartening to know that had people not reacted to the all-male panels, it would not be seen as a problem. Furthermore, it is not enough to tweet about it. It’s a start, but not nearly enough — because how many of these types of stories repeat themselves in the media? We need it to be written in the mandates of institutions, and this is not enough. We need it to be enforced. We also need women to be more involved and hold power in these decision-making panels; it is not enough to throw in a token white woman and call it a day. It is not enough for women to be given a seat on the board as a corporate marketing tool under the guise of inclusivity. They must also be afforded the same power that men have. We need to hold each other more accountable. We need to confront our own prejudices, no matter how uncomfortable that may be. If not for women, then do it for practical and selfish reasons; do it because there are studies that show that women have to be more productive than men to be deemed equally scientifically competent (feeling the pressure to prove themselves). And do it because it is better for the economy, and because diversity in the workplace increases productivity

Graph by The Star on the income of full-time men and women in Canada, who have a bachelor’s degree.

-Source: https://www.thestar.com/news/canada/2017/11/29/gender-pay-gap-persists-census-data-shows.html

There is no good reason to continue to exclude women from the same influential roles that men have, and it is time that we each consider our own sexist views (whether sub-conscious or not). It is time to challenge the systemic biases in powerful institutions in order to let women claim their full potential as true peers to men; as colleagues, partners, scientists, and in all other walks of life. In order to increase scientific literacy, we can not afford to continue to exclude women from science, because science needs women. In the spirit of the United Nations’ International Day of Women and Girls in Science day, which passed on February 11^th, and International Women’s day today, let us commit to empowering women to reach political, social, and economic equality to men. And let us make changes in our own lives, begin conversations with those around us, and become more active in our communities to progress towards gender equity.

Pulling a fast one: getting unscientific nonsense into scientific journals. (or, how PLOS ONE f*#ked up)

The basis of all of science is that we can explain the natural world through observation and experiments. Unanswered questions and unsolved riddles are what drive scientists, and with every observation and hypothesis test, we are that much closer to understanding the universe. However, looking to supernatural causes for Earthly patterns is not science and has no place in scientific inquiry. If we relegate knowledge to divine intervention, then we fundamentally lose the ability to explain phenomena and provide solutions to real world problems.

Publishing in science is about leaping over numerous hurdles. You must satisfy the demands of reviewers and Editors, who usually require that methodologies and inferences satisfy strict and ever evolving criteria -science should be advancing. But sometimes people are able to 'game the system' and get junk science into scientific journals. Usually, this happens by improper use of the peer review systems or inventing data, but papers do not normally get into journals while concluding that simple patterns conform to divine intervention.

Such is the case in a recent paper published in the journal PLOS ONE. This is a fairly pedestrian paper about human hand anatomy and they conclude that anatomical structures provide evidence of a Creator. They conclude that since other primates show a slight difference in tendon connections, a Creator must be responsible for the human hand (well at least the slight, minor modification from earlier shared ancestors). Obviously this lazy science and an embarrassment to anyone that works as an honest scientist. But more importantly, it calls into question the Editor who handled this paper (Renzhi Han, Ohio State University Medical Center), but also PLOS ONE's publishing model. PLOS ONE handles thousands of papers and requires authors to pay for the costs of publishing. This may just be an aberration, a freak one-off, but the implications of this seismic f$@k up, should cause the Editors of PLOS to re-evaluate their publishing model.  

Science is a maze

If you want to truly understand how scientific progress works, I suggest fitting mathematical models to dynamical data (i.e. population or community time series) for a few days.

map for science?

You were probably told sometime early on about the map for science: the scientific method. It was probably displayed for your high school class as a tidy flowchart showing how a hypothetico-deductive approach allows scientists to solve problems. Scientists make observations about the natural world, gather data, and come up with a possible explanation or hypothesis. They then deduce the predictions that follow, and design experiments to test those predictions. If you falsify the predictions you then circle back and refine, alter, or eventually reject the hypothesis. Scientific progress arises from this process. Sure, you might adjust your hypothesis a few times, but progress is direct and straightforward. Scientists aren’t shown getting lost.

Then, once you actively do research, you realize that formulation-reformulation process dominates. But because for most applications the formulation-reformulation process is slow – that is, each component takes time (e.g. weeks or months to redo experiments and analyses and work through reviews) – you only go through that loop a few times. So you usually still feel like you are making progress and moving forward.

But if you want to remind yourself just how twisting and meandering science actually is, spend some time fitting dynamic models. Thanks to Ben Bolker’s indispensible book, this also comes with a map, which shows how closely the process of model fitting mirrors the scientific method. The modeller has some question they wish to address, and experimental or observational data they hope to use to answer it. By fitting or selecting the best model for they data, they can obtain estimates for different parameters and so hopefully test predictions from they hypothesis. Or so one naively imagines.

From Bolker's Ecological Models and Data in R,
a map for model selection. 

The reality, however, is much more byzantine. Captured well in Vellend (2010): 

“Consider the number of different models that can be constructed from the simple Lotka-Volterra formulation of interactions between two species, layering on realistic complexities one by one. First, there are at least three qualitatively distinct kinds of interaction (competition, predation, mutualism). For each of these we can have either an implicit accounting of basal resources (as in the Lotka-Volterra model) or we can add an explicit accounting in one particular way. That gives six different models so far. We can then add spatial heterogeneity or not (x2), temporal heterogeneity or not (x2), stochasticity or not (x2), immigration or not (x2), at least three kinds of functional relationship between species (e.g., predator functional responses, x3), age/size structure or not (x2), a third species or not (x2), and three ways the new species interacts with one of the existing species (x3 for the models with a third species). Having barely scratched the surface of potentially important factors, we have 2304 different models. Many of them would likely yield the same predictions, but after consolidation I suspect there still might be hundreds that differ in ecologically important ways.”

Model fitting/selection, can actually be (speaking for myself, at least) repetitive and frustrating and filled with wrong turns and dead ends. And because you can make so many loops between formulation and reformulation, and the time penalty is relatively low, you experience just how many possible paths forward there to be explored. It’s easy to get lost and forget which models you’ve already looked at, and keeping detailed notes/logs/version control is fundamental. And since time and money aren’t (as) limiting, it is hard to know/decide when to stop - no model is perfect. When it’s possible to so fully explore the path from question to data, you get to suffer through realizing just how complicated and uncertain that path actually is. 

What model fitting feels like?

Bolker hints at this (but without the angst):

“modeling is an iterative process. You may have answered your questions with a single pass through steps 1–5, but it is far more likely that estimating parameters and confidence limits will force you to redefine your models (changing their form or complexity or the ecological covariates they take into account) or even to redefine your original ecological questions.”

I bet there are other processes that have similar aspects of endless, frustrating ability to consider every possible connection between question and data (building a phylogenetic tree, designing a simulation?). And I think that is what science is like on a large temporal and spatial scale too. For any question or hypothesis, there are multiple labs contributing bits and pieces and manipulating slightly different combinations of variables, and pushing and pulling the direction of science back and forth, trying to find a path forward.

(As you may have guessed, I spent far too much time this summer fitting models…)

Taking stock of exotic species in the new wild: Acknowledging the good and the bad.*

Are exotics good or bad? They are neither. They just are. But some exotics cause harm and impede certain priorities, and debates about exotics often ignore reality.

Book review: Fred Pearce. 2015. The New Wild: Why Invasive Species Will Be Nature’s Salvation. Beacon Press

There has been much soul-searching in invasion biology, with attacks, and subsequent rebuttals, on the very nature of why we study, manage and attempt prevent the spread of exotic species (Davis et al. 2011) (Alyokhin 2011, Lockwood et al. 2011, Simberloff 2011). What is needed at this juncture is a thoughtful and balanced perspective on the nature of the discipline of biological invasion. Unfortunately, the book “The New Wild” authored by Fred Pearce, is not that balanced treatment. What is presented in this book is a very one-sided view, where counter-evidence to the thesis that exotics will save nature is most often overlooked, straw men are erected to aid in this goal, and the positions of working ecologists and conservation biologists are represented as simplistic, anachronistic or just plain incorrect.

What Pearce has written is a book-long argument about why exotics shouldn’t be feared, but rather embraced as a partial solution to anthropogenic land use change. I do not wish to undermine the reality that exotics can play important roles in urban landscapes, or that some ecologists and conservation biologists do indeed harbour suspicions of exotics and subscribe to unrealistic notions of purely native landscapes. Exotic policy is at the confluence of culture, science, economics and politics, and this is why the science is so valuable (Sandiford et al. 2014). For Pearce, the truth of what most ecologists do and think seems like an inconvenient reality.  There are a number of pervasive, frustrating problems with Pearce’s book, where bad arguments, logical flaws and empirical slight-of-hand obfuscate issues that desperately need honest and reflective treatment.

A monoculture of the exotic plant Vincetoxicum rossicum that spans open and understory habitats near Toronto, Canada (photo by M. Cadotte). This is a species that interferes with other management goals and needs to be actively managed.

There are major problems with ‘The New Wild’ and these include:

1) A premise built on a non sequitur and wishful thinking. The general premise of the book, that exotics represent a way out of our environmental doldrums, is myopic. Pearce’s reasoning seems to be that he has conflated “the world is not pristine and restoration is difficult…” with the alternative being that exotics are positive and “we should bring them on”. Certainly we can question exotic control efficacy, costs and conservation goals, but that does not mean that exotics are necessarily the solution.

      2)   Underrepresenting the observed effects of some invasive non-indigenous species. Pearce’s book is not balanced. The perceived benefits of exotics in this ‘New Wild’ are extolled while dismissing some of the problems that invasive ones might cause. He says that exotics typically “die out or settle down and become model eco-citizens” (p. xii). But there is a third outcome that Pearce ignores –they move in and become unruly neighbours. When he must acknowledge extinctions, he minimizes their importance. For example when discussing Hawaiian bird extinctions: “The are only 71 known extinctions” (p. 12 –italics mine), or with California: “But only 30 native species are known to have become extinct as a result [of exotics]” (p. 64 –italics mine).

He also implies throughout the book that exotics increase diversity because “Aliens may find new jobs to do or share jobs with natives.” (p. 113). The available evidence strongly suggests that the numbers of species inhabiting communities has not increased over time (Vellend et al. 2013, Dornelas et al. 2014). Which on the surface seems like a good thing, except that many communities are now comprised of 20-35% exotics. This means that there have been losers. Vellend and colleagues (2013) show that the largest impact on native species diversity has been the presence of exotics. So, they do not necessarily find new jobs, but rather outcompete some natives.

      3)   Conservation biologists and ecologists in the crosshairs. Pearce continually lauds those like-minded, outspoken advocates of exotics while belittling ecologists and conservation biologists who don’t agree with him. His disrespect for the process of science comes in two forms. First, he seldom considers evidence or presents opinions counter to his thesis. He gives a partial reason about this bias; he says that ecologists (except for those few brave pioneering souls) ignore novel ecosystems and the functional contributions of exotics (for example on p. 13). This is demonstrably false (see next section). Pearce has little affection for conservation biologists and mainstream ecologists. Both groups are disparaged and dismissed throughout the book. Conservation biologists get a particularly rough ride, and he never acknowledges the difficulty of their task of balancing multiple priorities: extinction vs. ecosystem function, habitat preservation vs. socioeconomic wellbeing, etc. For example, Pearce states: “Conservation scientists are mostly blind to nature outside of what they think of as pristine habitats and routinely ignore its value” –again a demonstrably false assertion.

In a particularly galling example, Pearce resorts to ‘guilt by association’ as an ad hominem attack to undermine the validity of opposing views. He links conservation with eugenics: “Many conservationists of the first half of the twentieth century were prominent proponents of eugenics” (p. 141). It would be equally valid to state that most journalists were proponents of eugenics in the first half of the twentieth century. Pearce, being a journalist, should see this as a specious argument at best.

Ecologists share in this odd and unfair derision. “Ecologists are tying themselves in knots because they refuse to recognize that these novel, hybrid ecosystems are desirable habitats for anything.” (p. 156). Unfortunately for Pearce, there are more than 4000 papers on ‘novel ecosystems’.

      4)   Misrepresenting modern ecology and conservation. Pearce attacks ecological science throughout the book and as an example Pearce makes observations about the role of disturbance and refusal to acknowledge this by ecologists “intent on preserving their own vision of balanced nature” (p. 144). However, disturbance has been a central component of community ecology for the past five decades. Because of this balance-of-nature view, Pearce says ecologists are not studying degraded, disturbed or recovering systems. For example, with secondary forests, he says: “Yet the blinkered thinking persists. Degraded forests and forests in recovery are almost everywhere under-researched and undervalued.” (p. 157). Yet there are almost 9,500 papers on secondary forests –highlighting the ecological interest in these widespread systems. There are numerous such examples.

      5)   A black and white, either-or dichotomy.  What Pearce provides is a series of stark dichotomies with little room for subtle distinction. He ties resilience and ecosystem wellbeing to the arrival of exotics, without adequately assessing the drawbacks: “Nature’s resilience is increasingly expressed in the strength and colonizing abilities of alien species …we need to stand back and applaud” (p. xii).

Invariably in ecology, debates over ‘either/or’ dichotomies end up with the realization that these dichotomies are endpoints of a continuum. This is exactly the case with exotics. Are they bad or good? The answer is neither. They just are. Some exotics species provide economic opportunity, ecosystem services and help meet other management goals. Some exotics cause harm and impede certain priorities. Modern management needs to be, and in many cases is, cognizant of these realities.

 References

Alyokhin, A. 2011. Non-natives: put biodiversity at risk. Nature 475:36-36.

Davis, M. A., M. K. Chew, R. J. Hobbs, A. E. Lugo, J. J. Ewel, G. J. Vermeij, J. H. Brown, M. L. Rosenzweig, M. R. Gardener, and S. P. Carroll. 2011. Don't judge species on their origins. Nature 474:153-154.

Dornelas, M., N. J. Gotelli, B. McGill, H. Shimadzu, F. Moyes, C. Sievers, and A. E. Magurran. 2014. Assemblage Time Series Reveal Biodiversity Change but Not Systematic Loss. Science 344:296-299.

Lockwood, J. L., M. F. Hoopes, and M. P. Marchetti. 2011. Non-natives: plusses of invasion ecology. Nature 475:36-36.

Sandiford, G., R. P. Keller, and M. Cadotte. 2014. Final Thoughts: Nature and Human Nature. Invasive Species in a Globalized World: Ecological, Social, and Legal Perspectives on Policy:381.

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Vellend, M., L. Baeten, I. H. Myers-Smith, S. C. Elmendorf, R. Beauséjour, C. D. Brown, P. De Frenne, K. Verheyen, and S. Wipf. 2013. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proceedings of the National Academy of Sciences 110:19456-19459.

 *This post is a synopsis of my book review in press at Biological Invasions