Wednesday, December 2, 2015

Paper of the lustrum*

(*lustrum = five years)

I’m co-teaching (with Kendi Davies and Julian Resasco) a graduate seminar focused on current trends and advances in community ecology. It’s been great, and having a small group with varied backgrounds (disease ecology, microbial ecology, restoration, community ecology theory, etc) allows for flexible and interesting discussions. Somehow the topic last week drifted to favourite papers, and we ended up with a plan to choose and defend the paper that was—in our opinion—the best one published in ecology in the last 5 years.

Today we described and defended our choices and tried to decide what the ‘best’ actually means, anyways. I don’t think anyone quite realised just how difficult this exercise would be. First, 5 years isn’t actually a very long time when measured in academic publishing years. That’s only the time of the average PhD, or less than the entire tenure-track period. I immediately thought of several papers I love, only to realize that sadly, they were from before 2010 (e.g. papers like these). 

Nearly everyone started their search the same way: with a Google Scholar search, looking at the most cited papers between 2010-2015. Some people looked at the most popular papers from high impact journals (Ecology Letters, Science, Nature, PNAS, etc); others looked at the output of eminent ecologists during that time period. At least one used his committee members for advice, and for the new grad students this was a nice crash course in the recent literature. Citations, quality journals, or eminent names might have been starting points for finding these papers, but it was interesting how little these actually seemed to matter. When defending their choice of paper, absolutely no one mentioned citations or journal as deciding factors. 

The papers we chose, and why: 
Conceptual synthesis in community ecology. (The Quarterly Review of Biology) Vellend 2010  
This was my choice, although I went back and forth between a short list of papers. For me, the ‘best’ paper had to either change how we do ecology, or how we think about ecology. I think Vellend 2010 has a lot of value as a pedagogical tool, and a device for organizing ecological knowledge. It has the potential to aggregate the varied, context dependent data that ecologists have been collecting for generations. Further, rather than the disjointed approach my undergraduate texts took for community ecology (productivity here, lynx-hare plot there), a single framework should help students understand community ecology as a cohesive set of ideas. And I admire papers that have big ideas.

 This was a cool choice, because it turns out to be a massively important development that many of the less molecularly-inclined knew little about. This paper introduced the use of CRISPR/Cas for gene editing. The CRISPR system is been found in archaea and bacteria, and provides a form of adaptive immunity against viruses. Importantly, it has been developed for use in incredibly precise genome editing that is heritable. It has massive implications for the study of evolution, microbial ecology, disease, population genetics, and everything in between. It is also the source of ethical concerns because it can (and has) be used to modify human embryos. 

Biodiversity loss and its impact on humanity. (Nature) Cardinale et al. 2012 
This was the choice of two students, so it may have been the de facto winner. It is a massively cited paper (>1000), and both students chose it in part because it makes a clear contribution to human welfare and society. It represents a massive undertaking (they analysed more than 1000 papers) reviewing research on how biodiversity relates to a large number of relevant ecosystem services. In particular, Table 1 (below) can be used for applied and basic research, and shows where research and data agree, disagree, or are lacking. This is certainly a must read for ecologists.


This paper helped to concentrate and inspire research on intraspecific variation and to highlight the areas of research that are still poorly studied (and it actually made my short list too). There is obvious variation within species (long acknowledged as important to evolution, starting with Darwin) but this is often ignored in community ecology. Bolnick et al. point out the many possible and important implications that arise from such variation. The writing is clear and highlights extremely well the general mechanisms that might interact with intraspecific variation. For the student who chose it, it was inspiring enough when it first came out, that they changed their research direction. 
Table 1: Bolnick et al. 
This paper was chosen in an opposite fashion: it is brand new, and rather than having inspired current research, the student thought it would inspire future approaches. The paper integrates community ecology and disease ecology in a novel and sophisticated way, advancing an area of research currently receiving a lot of attention. In this paper, mice are ‘mesocosms’ in which the importance of bottom-up versus top-down control of infection (by malaria and a nematode) could be tested. (Quote: "It's a real page-turner"). 

This was another paper chosen because it inspired the student's current studies. Ladau et al. brought together a massive data set for marine bacterial biodiversity, allowing them to map it on a global scale and develop predictive distribution models. Interestingly, they found that diversity patterns were lower at the equator, contrary to typical findings in other organisms. The student cited the careful methodology, extensive data, and comparison of results to those in macro-scale systems as the paper’s strengths. 
From Ladau et al. "Maps of predicted global marine bacterial diversity. Color scale shows relative richness of marine surface waters as predicted by SDM. Samples were rarefied to 4266 rDNA sequences to enable accurate estimation of relative richness patterns on a global scale from data sets with different sequencing depths. True richness is expected to exceed estimated values. (a) In December, OTU richness peaks in temperate and higher latitudes in the Northern Hemisphere. (b) In June, OTU richness peaks in temperate latitudes in the Southern Hemisphere..."

The final paper was Kendi’s choice. Community ecology has struggled with weak connections between pattern and process. The experimental and quantitative work coming from this research group has provided multiple examples for how to connect theory, statistics, and experimental results in a very rigourous fashion. In this paper, the focus is particularly on functional/trait approaches to community assembly and coexistence, and the authors manage to connect careful experimental data with Chessonian coexistence theory, using trait data to estimate species’ fitness and niche differences, and then using these to predict species coexistence.

After the fact, of course, lots of other great papers came to mind. It isn't really possible to choose one best paper, either. But the characteristics people looked for in a great paper were pretty similar - inspiring, providing novel approaches to particular questions, focused on big questions or ideas, and making contributions that go beyond academic ecology.

Monday, November 23, 2015

Challenges for microbial ecology

It is common in ecology for promising new areas of research to grow rapidly in terms of funding, students, and papers. Sometimes, such growth outpaces supporting development. This can lead to criticisms, which, when properly dealt with, can help such burgeoning subfields to mature. These are challenges currently facing microbial ecology as well. [Note I use the term microbial ecology here to refer to the ecology of microbes, not simply ecology that happens to use microbes as a study organism (e.g. Graham Bell or Lin Jiang’s experimental work).]

Microbes are fascinating. They are a very large and important group that has been under appreciated in ecological research until recently. Now, thanks to ever-improving molecular methods, the ecology of microbes is increasingly accessible. It has formed the basis of some great citizen science and public outreach (microbes in space, your home, your cat). And scientifically, work from this emerging subfield is often excellent, with broad implications to other areas of ecology (just as a couple of cool examples). Microbes are different from other taxa for all sorts of cool reasons - horizontal transfer of genes, tiny genomes, and immense functional plasticity – and this makes for fascinating discoveries.

However, the newness of this subfield is apparent as it attempts to mesh microbiology with the existing body of ecological knowledge and approaches. The result, at times, is that existing ecological theory and methods are applied unquestioningly to microbial datasets, but may not be appropriate. Unfortunately, the assumptions behind such analyses and their limitations with respect to microbial datasets aren’t always recognized, leading to questionable interpretations. There is sometimes also an over-reliance on “pipeline” approaches to microbial research; for example: collect samples, extract DNA, sequence, run through the QIIME pipeline, and present descriptive analyses, particularly beta-diversity metrics (Unifrac), PCoA or NMDS plots, and permutation-based statistical tests (e.g. ANOSIM) to determine whether assemblages of interest differ in composition. These pipelines originally arose because of the difficulties in handling such data sets and the need for specific software for analyses.

Of course, it is important to keep in mind that microbial ecology is in an early phase, where accumulating data and cataloging diversity is a priority. Mostly, issues arise when major questions in ecology are posed but perhaps without quite having appropriate methods or data to answer them. To provide an example, I sometimes see microbial ecology papers attempting to differentiate between niche and neutral processes as the drivers of microbial community assembly. Microbes are often thought of as lacking meaningful dispersal limitation (‘everything is everywhere; the environment decides’ is a common heuristic). As a result, it may be that communities assemble in a highly stochastic fashion (random arrival) or perhaps environmental filters and interactions do matter. But the issue of “niche” versus “neutrality” is a difficult question to answer using observational data in any system. It requires considering the many assumptions that underlie “niche” and “neutral”, making predictions about the patterns that would arise from these mechanisms, and then being able to differentiate these patterns from others that you might observe. This is a tall order for any observational data set, and I think that is especially true for microbial data sets.

Below I have listed in more detail the challenges arising when attempting to integrate ecology and microbiology. These relate to all sorts of ecological questions and analyses, including but not limited to “niche versus neutral”.

a) True measures of abundance are not typically available, and 16S copy number is incorrectly used as a measure of abundance. 16S ribosomal RNA is the typical target of studies of bacterial ecology. However, counts of 16S copies per taxa are not equivalent to abundances (as say, counts of individuals in macro-systems are): instead, different taxa can have different copy numbers. Where one taxa might have 2 copies, another might have 10. 

Despite this, it is common to see it used as a proxy for abundances; for example, to calculate beta-diversity measures such as Bray-Curtis. Since neutrality predicts patterns related to species' abundance distributions, and changes in diversity through time, when conclusions rely on 16S-based ‘abundance’ metrics, they are suspect. Some attempts are being made to address this – for example, this paper from Steve Kembel et al. (2012) recognizes that copy number is a conserved trait and so could be controlled for in a phylogenetically-informed way. qPCR can also be used to measure true abundances in samples. (See comments).

b) What spatial scale is relevant to microbes? Bacteria are very small (of course). However, sampling methods often involve fairly large samples in relation to bacterial body size. 1 g of soil, although tiny compared to many ecological samples, is a massive amount of material in the context of bacteria. There can be 10^8 cells/g of soil, and by one estimate the interaction distance between individuals is ~20um, and so it is not likely that a 1g sample is equivalent in scale to a “community”.

If a typical observational sample is not representative of a community, community ecology theory, which is dependent on assumptions about local interactions and environmental filters at particular spatial scales may not be relevant. Scale issues are an ongoing problem in ecology, and defining the ‘community’ is a thorn in our sides. It is understandable that this is a problem for a new field. Thinking about the kind of data collected as relating to macroecology may be a fruitful approach (see this paper for similar ideas on the topic). 

c) Temporal scale has similar issues. Unlike in macro-scale systems, microbial time scales are very rapid, with approximately 100-300 generations per year (with some variation between taxa). The scale of environmental variation that affects these communities should be finer as well. This is a benefit and a difficulty of the system. For examples, one can potentially observe a community assemble to equilibrium in a bacterial system. But describing changes in bacterial composition observed over 1 year as succession and placing them in the context of ecological literature on plant succession seems imprecise. The scale of observation is of particular importance.

d) There can be issues in differentiating between active and inactive taxa, since microbes may be present in a sample but dormant. Methods exist to differentiate between these taxa, but when not applied, an apparently rare taxa in an assemblage may actually be an inactive taxa.

e) Sampling artifacts and other biases can arise between labs and runs, including biases related to PCR, primers, DNA extraction, storage, rarefaction, and more. This is an issue equivalent to limitations in methodological approaches in every field, and one that is actively being worked on (for example, developing standardized approaches). Further, the existing technology is pretty amazing.

f) Limitations of the current null models and statistical methods being applied. Null models are still a work in progress for ecology, and need to continue to be developed and perfected. But I think that there are specific issues that need to be considered in applying some of these methods to microbial data in particular, and there is a need for concerted research on developing statistical methods for such massive datasets.

In particular, I suspect there is an issue regarding heightened Type 1 error rates and issues with inadequately randomizing very large data sets. Ulrich and Gotelli (2012) hint at some of these possible issues:
“null model analysis may not be well-suited to such large data sets. The general statistical problem is that with very large data sets, the null hypothesis will always be rejected unless the data were actually generated by the null model process itself. So, large data sets may often deviate significantly from null models in which row and column sums are fixed, regardless of whether species occurrences are random or not (Fayle and Manica 2010). This was not a problem in the early history of null model analysis, when ecologists worried that apparent patterns in relatively small data sets might reflect random processes”
There is not enough time here to delve into most of these issues in detail, but permutation tests/Mantel test type analyses have a number of important limitations and assumptions must be tested for appropriate usage (from Pierre Legendre). From the ANOSIM website
“Recent work…has shown distance-based methods (e.g., ANOSIM, Mantel Test, BIOENV, BEST) are inappropriate for analyzing Beta diversity because they do not correctly partition the variation in the data and do not provide the correct Type-I error rates.” 
If Type I error rates are frequently high in past analyses, or inappropriate statistical models were used, data can be re-analysed as better procedures arise. But we should also recognize that there is uncertainty in past results (particularly weak or barely significant patterns). It should also suggest that we have yet to gain a true understanding of what patterns and relationships in microbial ecology are truly significant.

Microbial research produces some of the most complex and large datasets that ecology has ever had to deal with. As a result, developing specific theory and appropriate methods for this data should be a priority alongside discovery-focused research. Fortunately, this creates opportunities for ecologists to develop methods for complex systems, which should be beneficial for the entire ecological discipline. And many people are already attempting to fill these knowledge gaps, so this is not to underplay their accomplishments. Hopefully there will continue to be developments in microbe-specific theory, with appropriate assumptions regarding temporal and spatial scale. Microbial ecologists can do better than co-opt standard ecological approaches, they can improve on them (e.g. Coyte et al. (2015)).

Conservation’s toughest decision

Guest post by Shelby Hofstetter, currently enrolled in the Professional Masters of Environmental Science program at the University of Toronto-Scarborough

“We should have thrown in the towel years ago!”- the dinner-table conversation takes a drastic turn from gushing over new panda bear cubs at the Toronto Zoo to a more pessimistic view of the state of global panda conservation efforts. The speaker of these words is recalling a program that aired on the CBC when the pandas were first arriving at the Toronto Zoo. In it, host Amanda Lang acknowledged herself as a “panda hater” and expressed her disapproval of the money wasted on continued panda conservation efforts that are based solely on their appearance (link to video below). As someone who queued in line for the chance to take far too many pictures of the adorable bears, I blanch at some of Lang’s comments that pandas are “big and stupid“ and “want to be extinct”. But as a student of conservation, I recognise the underlying truth that we as a society have a bias for spending our conservation dollars on big, fluffy animals, regardless of their likelihood of survival.

(Photo taken by Shelby Hofstetter at the Toronto Zoo)

But what are the alternatives? With the realisation that funds for biodiversity conservation are finite, there has been a long history of debate over the best methods for choosing worthy species. The umbrella species concept seems to be the logical response to this conundrum – the classic 2 for 1 sale where conservation efforts for one species have the added bonus of protecting various other species that share the same ecosystem. This is the reason why some claim that the “big, fluffy” species are often highlighted in conservation projects, because the large, continuous tracts of land that are a necessity for their protection become a safe haven for many more.

The reality of the umbrella species concept may not be as simple however- there is some debate over how well it actually works. In some cases the large habitats required for the umbrella species do not overlap with biodiversity hotspots for other types of organisms like invertebrates, plants, amphibians or reptiles[1]. And unfortunately, even in cases where these pieces of habitat would provide protection for additional species, safeguarding the large amount of land necessary is often unrealistic[2].

Figure 1. Based on phylogenetic diversity, species A would be a higher conservation priority than species B or C as it has fewer close relatives that would be similar genetically[4]


Another response to this conservation riddle is aptly named the “Noah’s Ark Problem”, and is a framework for choosing species for conservation based on cost and likelihood of survival, but also on phylogenetic diversity[3]. This objective focus on phylogenetic diversity, or the amount of genetic history that a species contains, has gained momentum in recent years and is aimed at saving species that encapsulate high amounts of Earth’s evolutionary life history. The hope is that phylogenetic diversity is correlated with genetic diversity in general, which could also give these species a better chance of adapting to a changing planet[4].

Another notion that is becoming more prevalent is the consideration of ecosystem services, or the benefits that humans derive from a species or ecosystem, when planning for conservation projects. This concept is not necessarily centered around a specific species, but is more focused on the ecosystem as a whole. The emphasis on ecosystem services may help increase the perceived relevance of conservation projects, as the benefit to society is being highlighted. The uptake of this idea within global conservation efforts has been slow however, with less than 10% of conservation assessments including ecosystem services as part of their rationale for conservation[5]. There also seems to be a push for determining the corresponding economic and monetary value of the services that ecosystems provide to society. This is a science that, in a world focused on dollars and cents, may become very important to determining which species or areas are worthy of conservation efforts.

The jury is still out on how to best make conservation’s toughest decision-   determining which struggling species on this planet should be the lucky winners of our conservation resources. In the meantime the importance of this issue is becoming very clear, as many suggest that Earth is currently experiencing a sixth mass extinction. Smart and timely decision-making is vital for which species limited conservation efforts should be focused on. I wouldn’t go so far as to call myself a “panda hater”, or suggest that we “throw in the towel” on conservation efforts for big fluffy species that may not be likely to recover, but I do agree that these decisions should go beyond visual appearances.
Additional Links:
link to Amanda Lang video: https://www.youtube.com/watch?v=0bm-kEnK3yk


References:
1. Marris, E. (2013, December 24). Charismatic mammals can help guide conservation. Nature | News.
2. Fleishman, E., Blair, R., & Murphy, D. (2001). Empirical Validation Of A Method For Umbrella Species Selection. Ecological Applications, 11(5), 1489-1501.
3. Weitzman, M. (1998). The Noah's Ark Problem. Econometrica, 66(6), 1279-1298.
4. Owen, N. (2014). Life on the edge. Significance, 26-29.
5. Egoh, B., Rouget, M., Reyers, B., Knight, A., Cowling, R., Jaarsveld, A., & Welz, A. (2007). Integrating ecosystem services into conservation assessments: A review. Ecological Economics, 63(4), 714-721.

Monday, November 16, 2015

Where is south? Uncovering bird migration routes

Guest post by John Viengkone, currently enrolled in the Professional Masters of Environmental Science program at the University of Toronto-Scarborough
Wilson’s Warbler http://www.utahbirds.org/birdsofutah/BirdsS-Z/WilsonsWarbler.htm
There are approximately 450 native migrating bird species that for at least part of the year reside in Canada, but where do they go when they aren’t in the True North Strong and Free? If you ask just about anyone, they’ll tell you that birds fly south for the winter, but where exactly is south? South could be as close as the next city, the USA or as far as Tierra Del Fuego. Also do they make stops on their way to this “south” and do they mix with other populations? The truth is there isn’t much information on where many migrating species go or the route they take to get there.

But why should we care where they go when they leave Canada, they seem to always come back in the spring. The truth is not all birds are coming back, there has been a marked decline in the population size of many migrating neotropical bird species. As the leading cause of species loss, humans need to figure out whether these bird populations are facing stressors in their breeding, wintering, stopover range or some combination of the three so we can help manage them. The first step in doing this is learning the birds’ migration route. 

The effort to understand the movement patterns of birds began in North America during the 1800s when the famous ornithologist John James Audubon started tying silver string to the legs of eastern phoebes, Sayornis phoebe, to see if individuals that left in the fall returned in the spring. Of all the birds Audubon marked, 2 returned in the spring. This little experiment transformed into the bird banding/ringing program we know today with different coloured metal bands replacing the pieces of silver string.

Though the bird banding program has been essential to the understanding of bird ecology, life history and migration it is has one major flaw. This flaw is that banded birds must be spotted again and it’s estimated that only 1 in 10,000 banded birds are recaptured, leaving a large data gap. So why use bands, why not use GPS tracking devices? Well, they do for larger birds but for many bird species the size and weight of a tracker is too much of stress so a better solution is needed. This solution is up and coming from Dr. Kristen Ruegg’s lab at UCLA and it has been dubbed The Bird Genoscape Project.

Ruegg and company have taken on the task of creating a protocol that will allow them to identify where a migrating bird has come from by using just a feather. To get a full comprehensive understanding of this protocol please refer to Ruegg et al. 2014 but I’ll briefly explain their methods here: Variation in DNA is what makes individuals unique but a huge portion of an organism is actually shared with the individuals of the same species. As groups or populations of a species become more isolated and breed with other individuals in their populations more, the populations start to diverge, this is population differentiation. Individuals in a breeding population will be more similar to each other than to other populations.

The UCLA team used the concept of population differentiation to find the small bits of DNA, called single nucleotide polymorphisms (SNPs), that are unique to each breeding population, a genetic fingerprint some might say. For their study they looked at the Wilson’s warbler, Cardellina pusilla, taking small blood samples from individuals in each breeding population and each population’s genetic fingerprint was made.

With a genetic fingerprint for each breeding population Dr. Ruegg and her collaborators were able to collect feathers from Wilson’s warblers across North America and identify where it came from with an 80-100% success rate. So a feather collected in Colorado in the late fall could be traced back to the British Columbia breeding population, meaning Colorado is a stop off point. This solves the major problem that banding had; you don’t need to come in contact with the same bird to get information, any bird in the species will work. 

From Ruegg et al. 2014. Each colour depicts a breeding population, arrows are stopovers and circles are wintering grounds
An interesting finding from UCLA’s study was that there are 6 breeding populations of Wilson’s warblers opposed to the 2-3 that biologist previously thought and that 3 of the breeding populations actually share a wintering ground and flight path. Two of these three breeding populations are stable but one population is declining, suggesting the cause of decline stems from the declining population’s breeding ground. If the issue stemmed from the wintering ground of the flight path, the other populations should be affected too.


So what’s next? Ecological managers now know where the issue is likely originating from for the Wilson’s warbler but still need to identify the root cause. As for The Bird Genoscape Project, Dr. Ruegg has moved on to repeating this study with the American Kestrel. There is also work being done with museum samples to see if ranges and flight paths have shifted with time. It’s looking like The Bird Genoscape Project can only get bigger, spreading to more migrating bird species and become an essential tool for bird conservation just as bird banding did in the past.

For more information see:
Ruegg K.C., Anderson E.C., Paxton K.L., Apkenas V., Lao S., Siegel R.B., Desante D.F., Moore F., and Smith T.B. 2014. Mapping migration in a songbird using high-resolution genetic markers. Molecular Ecology 23:5726-5739.
Kristen’s interview with Podcast Eye’s on Conservation is available on iTunes

Tuesday, November 10, 2015

Culling Koalas for Conservation

Guest post by Stefanie Thibert, who is currently enrolled in the Professional Masters of Environmental Science program at the University of Toronto-Scarborough


Euthanizing diseased koalas may be the most effective management strategy to save koalas from extinction in Queensland. A recent study published in the Journal of Wildlife Disease suggests that if 10% of terminally diseased and sterile koalas were culled while other infected koalas were treated with antibiotics, chlamydial infections could be completely eliminated and population sizes could increase within four years. 
The beloved koala relaxing in a eucalyptus tree
(Source:
http://www.onekind.org/be_inspired/animals_a_z/koala/) 

Although koalas are under pressure from habitat degradation, dog attacks and road accidents, disease burden is the largest threat to its population sizes. It is estimated that 50% of the current koala population in South-East Queensland is infected with the Chlamydia spp. The sexually transmitted disease causes lesions in the genitals and eyes, leading to blindness, infertility, and ultimately death. Rhodes et al. (2011) suggest that reversing the observed population decline in Queensland koalas would require either entirely eliminating deaths from cars and dogs, complete reforestation, or reducing deaths caused by Chlamydia by 60%. It is clear that the best conservation tool is to reduce the prevalence of chlamydial infection.

In the study, Wilson et al. (2015) examined the potential impact of euthanizing koalas infected with Chlamydia. As shown in Figure 1, computer simulation models were used to project koala population sizes based on four separate intervention programs: “no intervention”, “cull only”, “treat only”, and “cull or treat”. In the “cull or treat” program, sterile and terminal koalas were euthanized, while infected kolas that were not sterile or terminal were treated with antibiotics. It was concluded that the “cull or treat” is the most successful intervention program for increasing long-term population growth and eliminating chlamydial infections. 
The projected numbers of koalas in the Queensland population under different intervention programs.(From Wilson et al. 2015)
Without intervention, it is estimated that merely 185 koalas will persist in 2030. Under both the “cull only” and “treat only” intervention, it would take seven years before there would be greater koalas numbers than there would be without intervention. Under the “cull or treat” program, the population size was projected to overtake the no-intervention population after four short years. The population size in 2030 is also greatest under the “cull or treat” intervention. The increase in koala numbers in the “cull or treat” strategy is due to the considerable decrease in the prevalence of Chlamydia.
As expected, the proposal received considerable attention and was scrutinized by the public. Some argue that it is inhumane, while others suggest alternative management strategies. However, when it comes down to it, the science is clear. Euthanizing can be done in a humane way, and it is the most effective method for conservation of the species. The only real alternative to culling is treatment with antibiotics, which is costly, requires an immense amount of monitoring, and has been shown to take much longer to eliminate the disease and increase population sizes.
The question we must ask ourselves is: we cull other species, so why not koalas? For instance, in the United States, the culling of four million cattle successfully prevented bovine tuberculosis from spreading to humans. Even when based on sound scientific research, culling has always been dismissed as a management option for the iconic Australian marsupial. In 1997, culling was suggested as a method to protect the overabundant koala population on Kangaroo Island, but sterilization and relocation was used instead. It is amazing that a program that was significantly more expensive and less effective was chosen because the public could never think of killing the adorable and innocent koala.
Managing koala populations is clearly a case in which science intersects with emotion. However, it is essential that we put our emotions aside, and make a decision that is based on scientific evidence. Let us remember that the study only suggests culling or treating 10% of the population each year, which is equivalent to approximately 140 koalas. It is also important to improve the communication of science to the public. It needs to be made abundantly clear that without culling, the koala populations will continue to decrease.


To read the full article visit: http://www.bioone.org/doi/full/10.7589/2014-12-278 

References:

Oliver, M. (2015, October, 20). Proposal to euthanise koalas with chlamydia divides experts. The Guardian. Retrieved from: http://www.theguardian.com/world/2015/oct/20/proposal-to-euthanise-koalas-with-chlamydia-divides-experts.

Olmstead, A.L., & Rhode, P.W. (2004). An impossible undertaking: The eradication of bovine tuberculosis in the United States. Journal of Economic History, 64, 734-772.

Rhodes, J.R., Ng, C.F., de Villiers, D.L., Preece, H.J., McAlpine, C.A., & Possingham, H.P. (2011). Using integrated population modeling to quantify the implications of multiple threatening processes for a rapidly declining population. Biological Conservation, 144, 1081–1088.

Wilson, D., Craig, A., Hanger, J., & Timms, P. (2015). The paradox of euthanizing koalas to save populations from elimination. Journal of Wildlife Diseases, 51, 833-842.


Friday, November 6, 2015

Science in China –feeding the juggernaut*

For those of us involved in scientific research, especially those that edit journals, review manuscripts or read published papers, it is obvious that there has been a fundamental transformation in the scientific output coming from China. Both the number and quality of papers have drastically increased over the past 5-10 years. China is poised to become a global leader in not only scientific output, but also in the ideas, hypotheses and theories that shape modern scientific investigation.

I have been living in China for a couple of months now (and will be here for 7 months more), working in a laboratory at Sun Yat-sen University in Guangzhou, and I have been trying to identify the reasons for this shift in scientific culture in China. Moreover, I see evidence that China will soon be a science juggernaut (or already is), and there are clear reasons why this is. Here are some reasons why I believe that China has become a science leader, and there are lessons for other national systems.

The reasons for China’s science success:

1.      University culture.

China is a country with a long history of scholarly endeavours. We can look to the philosophical traditions of Confucius 2500 years ago as a prime example of the respect and admiration of scholarly traditions. Though modern universities are younger in China than elsewhere (the oldest being about 130 years old), China has invested heavily in building Universities throughout the country. In the mid-1990s, the government built 100 new universities in China, and now graduates more than 6 million students every year from undergraduate programs.
Confucius (551-479 BC), the grand-pappy of all Chinese scholars

This rapid increase in the number of universities means that many are very modern with state-of-the-art facilities. This availability of infrastructure has fostered the growth of new colleges, institutes and departments, meaning that new faculty and staff have been hired. Many departments that I have visited have large numbers of younger Assistant and Associate Professors, many having been trained elsewhere, that approach scientific problems with energy and new ideas.
My new digs


2.      Funding

From my conversations with various scientists, labs are typically very well funded. With the expansion of the number of universities seems to have been an expansion in funds available for research projects. Professors need to write a fair number of grant proposals to have all of their projects funded, but it seems that success rates are relatively high, and with larger grants available to more senior researchers. This is in stark contrast to other countries, where funding is inadequate. In the USA, National Science Foundation funding rates are often below 10% (only 1 in 10 proposals are funded). This abysmal funding rate means that good, well-trained researchers are either not able to realize their ideas or spend too much of their time applying for funding. In China, new researchers are given opportunities to succeed.


3.      Collaboration

Chinese researchers are very collaborative. There are several national level ecological research networks (e.g., dynamic forest plots) that involve researchers from many institutions, as well as international collaborative projects (e.g., BEF China). In my visits to different universities, Chinese researchers are very eager to discuss shared research interests and explore the potential for collaboration. Further, there are a number of funding schemes to get students, postdocs and junior Professors out of China and into foreign labs, which promotes international collaboration. Collaborations provide the creative capital for new ideas, and allow for larger, more expansive research projects.

4.      Environmental problems

It is safe to say that the environment in China has been greatly impacted by economic growth and development over the past 30 years. This degradation of the environment has made ecological science extremely relevant to the management of natural resources and dealing with contaminated soil, air and water. Ecological research appears to have a relatively high profile in China and is well supported by government funding and agencies.

5.      Laboratory culture

In my lab in Canada, I give my students a great deal of freedom to pursue their own ideas and allow them much latitude in how they do it. Some students say that they work best at night, others in spurts, and some just like to have four-day weekends every week. While Chinese students seem equally able to pursue their own ideas and interests, students tend to have more strict requirements about how they do their work. Students are often expected to be in the lab from 9-5 (at least) and often six days a week. This expectation is not seen as demanding or unreasonable (as it probably would be in the US or Canada), but rather in line with general expectations for success (see next point).

Labs are larger in China. The lab I work in has about 25 Masters students and a further 6 PhD students, plus postdocs and technicians. Further, labs typically have a head professor and several Assistant or Associate Professors. When everyone is there everyday, there is definitely a vibe and culture that emerges that is not possible if everyone is off doing their own thing.

The lab I'm working in -"the intellectual factory"

Another major difference is that there is a clear hierarchy of respect. Masters students are expected to respect and listen to PhD students, PhD students respect postdocs and so on up to the head professor. This respect is fundamental to interactions among people. As it has been described to me, the Professor is not like your friend, but more like a father that you should listen to.

What’s clear is that lab culture and expectations are built around the success of the individual people and the overall lab. And success is very important –see next point.


6.      Researcher/student expectations

I left the expectations on researchers for last because this needs a longer and more nuanced discussion. My own view of strict expectations has changed since coming to China, and I can now see the motivating effect these can have.

For Chinese researchers it is safe to say that publications are gold. Publishing papers, and especially the type of journal those papers appear determine career success in a direct way. A masters student is required to publish one paper, which could be in a local Chinese journal. A PhD student is required to publish two papers in international journals. PhD students who receive a 2-year fellowship to travel to foreign labs are required to publish a paper from that work as well. For researchers to get a professor position, they must have a certain number of publications in high-impact international journals (e.g., Impact Factor above 5).

Professors are not immune from these types of expectations. Junior professors are not tenured, nor are they able to get tenure until they qualify for the next tier, and they need to constantly publish. To get a permanent position as a full professor or group leader, they need to have a certain number of high impact papers. For funding applications, their publication records are quantified (number and impact factors of journals) and they must surpass some threshold.

Of course in any country, your publication record is the most important component for your success as a researcher, but in China the expectations are clearly stated.

While there are pros and cons of such a reward based system, and certainly the pressure can be overwhelming, I’ve witnessed the results of this system. Students are extremely motivated and have a clear idea what it means to be successful. To get two publications in a four year PhD requires a lot of focus and hard work; there is no time for drifting or procrastinating.

So why has Chinese science been so successful? It is because a number of factors have coalesced around and support a general high demand for success. Regardless of the number of institutional and funding resources available, this success is only truly realized because of researchers' desire to exceed strict expectations. And they are doing so wonderfully.  

*over the next several months I will write a series of posts on science and the environment in China