Wednesday, November 1, 2017

November science journal article summary

Nihao November!


Fall sumac

I've got a good one for you this month! It's less focused than usual, but there are three key topics, plus a mix of a few others:
First, if you're about to delete this unread, please take this survey (which takes <1 minute) to let me know if you have input on how these summaries could be more useful: https://www.surveymonkey.com/r/BCVDKQR . Thanks to all who responded; results are summarized at the end of this email.

Second, the long-awaited "Natural Climate Solutions" paper from TNC is out. Read it: it's only 5 pages and will be highly relevant to virtually everyone working in conservation. It makes a solid case for how immediately investing in nature to reduce GHGs can buy us much-needed time to bring down emissions and invent new technology.

Third, a new book came out Oct 12: Effective Conservation Science: Data Not Dogma. It includes chapters from myself and several TNC authors, and is full of fascinating stories of how we react to science that counters conventional wisdom. I also share related articles below on how we can work through our biases.

CLIMATE CHANGE / NATURAL CLIMATE SOLUTIONS
Griscom et al 2017 (the natural climate solutions paper) packs a lot of good content in, but two things in particular excite me. First is making the case for massive rapid investment in nature: while we develop new tech and bring down emissions, we can use proven solutions like trees to buy time and make progress (see figure 2: nature could get us 37% of mitigation needs by 2030 at <$100/t CO2e / yr). We need the tech too, but nature is something that works today to bring down GHGs. Second is breaking down their top 20 options for nature-based climate mitigation into the theoretical maximum impact (about 1/2 of which would cost <$100 / t CO2e / yr), what we would need to hit Paris targets of <2 degrees C, and the subset of mitigation which is cheap (<$10/t CO2e / yr). See Figure 1 for this breakdown, which highlights that forests are absolutely critical (2/3 of cost-effective mitigation), and that the biggest opportunities for cheap mitigation are preventing forest loss (and improving forest management), improving fertilizer use on farms, and keeping peatlands intact. The forest goals rely heavily on a small reduction in grazing lands (4%). I'm leaving out lots of important details to keep this short: just read the paper. It's worth it. Read all about it (or watch videos) at https://global.nature.org/initiatives/natural-climate-solutions/natures-make-or-break-potential-for-climate-change

DATA NOT DOGMA:
The book Effective Conservation Science: Data Not Dogma tells stories of scientists whose unconventional and inconvenient results challenge us all to broaden our thinking and consider how we respond to new information that undermines what we think we know. My chapter is around how my analysis and blog post showing that globally agriculture has been taking up a smaller footprint since 1998. You can buy the book here: https://global.oup.com/academic/product/effective-conservation-science-9780198808985?cc=se&lang=en& and read a review of one chapter here: www.slate.com/articles/technology/future_tense/2017/08/conservation_biologists_are_struggling_to_balance_science_and_advocacy.html and read an ugly (unformatted) version of my chapter here: http://fish.freeshell.org/publications/DataNotDogma-Chapter11-preformatted.pdf  

Here are three more papers on the topic of scientific bias:
In 1992 E.O. Wilson asserted that invasive species were the second greatest driver of species extinction (second only to habitat destruction). He did so without providing evidence or details behind his calculations, but this claim was rapidly repeated and taken as gospel by environmental scientists. In fact, TNC played a major role in elevating Wilson's claim by not only citing it (in a BioScience paper and related book), but adding that "scientists generally agree" with Wilson's claim (again without evidence). Chew 2015 tells the captivating story about how this happened, using clear writing, thought-provoking questions, and numerous examples of bias in language that should be neutral and scientific. He also tells us how the idea eventually became subject to critique. I have seen this phenomenon firsthand; I follow a trail of citation breadcrumbs from authors to discover a primary source with an assertion that cannot be supported by what's in the paper (e.g. a book chapter on soil by Rattan Lal). When scientists don't closely read the papers we cite (or read them at all), our biases blossom and spread. If you're interested in invasive species or how spurious claims spread, this is a great read (albeit long).

Warren et al 2017 asks how common it is for scientists to be biased with regard to invasive species: using value-laden language and favoring interpretation that emphasizes the impacts of invasive species even when the data are not clear (as exemplified by the Chew 2015 article). They found bias to be common, but also that it has been declining since a series of papers in 2004-2005 that argued against language vilifying invasive species. This paper is fairly simplistic but gets at a key nuance: even a bias which is generally true is counter-productive in science. This paper shows hope that with awareness of bias, we can make efforts to at least reduce the expression of that bias in our work.

Holman et al 2015 provides more evidence of scientific bias, and argues for the use of "blinding" when conducting research to limit the potential for bias to affect study results. This means scientists collecting data don't know whether the subjects or area they're observing is a treatment or a control. This makes it harder for preconceptions to affect measurements (whether subjective, or even "rounding" seemingly objective metrics to fit bias), and they present evidence that nonblind studies often inflate the effect of the actions being studied. If "working blind" sounds extreme to you, read my blog post about "Clever Hans" - a horse who was believed to be able to do math (but in fact was only skilled at reading when his audience believed he had the right answer): https://blog.nature.org/science/2015/02/12/horses-doing-math-clever-hans-lessons-conservation-science/

As a final thought on bias, check out the Minasny & McBratney article in the Soil section below, which challenges a key assertion for TNC's agriculture work (that boosting soil organic matter improves water holding capacity). Read the summary below, and observe your feelings and reaction if it challenges what you believe.

SOIL
Minasny & McBratney 2017 use a meta-analysis to argue against something generally believed to be true by people working on sustainable agriculture: they provide evidence that increasing soil organic matter has a relatively small effect on water holding capacity (particularly for plant-available water content). If they're right, it reduces TNC's argument that improving soil health via boosting organic matter on farms will substantially improve crop resilience to drought. The authors note that soils that benefit most from increases in organic matter are sandy and very low in organic matter to begin (both of which make sense). They have a good discussion of limitations of their analysis, in particular the fact that they focused only on soil and not what's above it. Cover crops and crop residue / stubble are likely to add to the small benefits shown via soil. There is also a lot of nuance and potential to reframe their analysis in a way that could show larger benefits. At the same time, recognizing that most of us have a bias on this topic, this is a useful reminder to check our assumptions about both the efficacy of practices and the key mode of action and metrics that we should focus on. The authors led a key paper on the "4 per mille" initiative on boosting soil carbon, so are not hostile to the notion of boosting soil carbon. You can read a news article about this one here: https://phys.org/news/2017-10-adding-soil-limited-effect-capacity.html

GENERAL ECOLOGY / BIODIVERSITY
Remember as a kid how many bugs would get splattered on the windshield of your car? Ever notice there are less now? A recent study (Hallman et al 2017) indicates this is a real phenomenon, with dramatic declines in flying insects. The authors tracked the total biomass of insects at 63 locations within nature preserves in Germany; from 1989 to 2016 biomass plummeted by 76%. They sampled several habitat types and found consistent declines. It's alarming to see this within protected areas, although the authors note virtually all are surrounded by agriculture. That could both pull insects away from natural areas, and provide more pesticide drift into the natural areas. Other studies have shown major insect declines, but none this severe, and I don't know of others within protected areas.

SCIENCE COMMUNICATIONS
I've been pondering what we think we know and how to communicate thorny issues (as per data not dogma). I'd recommend a book I'm reading: "Do I make myself clear?" by Harold Evans, which is helping me. While not for scientists, I saw my writing sins laid bare in this book. I'm looking to simplify my writing in science papers, and to better talk about science in general. I have a long way to go! I'm working on summarizing key lessons amidst all of the stories in the book. One useful tool is the Hemingway app, which helps you identify problematic text and how to improve it: http://www.hemingwayapp.com/

AGRICULTURE:
As noted in my August 2017 review, neonicotinoids (neonics for short) are a class of insecticide currently under close scrutiny for impacts on bees. Mitchell et al 2017 found neonics in 75% of the 198 honey samples they tested, although mostly at very low levels. All neonics were at safe levels for humans, and most were at levels considered safe for bees. This is useful to show both that these pesticides are very common, that they are being consumed by bees, and that they often occur in concert with other neonics (all of which is concerning). But the reporting (and fundraising) around this has glossed over the very low levels. While 48% of samples had total neonic levels over a very conservative threshold for potential harm to bees (0.1 ng / g, a more reasonable (still likely conservative, albeit arbitrary) threshold of 2 ng / g was only detected in 8% of samples. The honey was collected via "citizen science"; the researchers asked colleagues, friends, and family to bring them honey produced in a known location. That also raises the question of whether or not these honey samples are typical.


RESULTS FROM SURVEY ABOUT THESE SUMMARIES:
I'm guessing the folks who didn't respond would have had more critical feedback, but overall here's what I learned from the ~40 respondents:
  • 90% of you usually at least skim these for relevant content
  • 90% of you found the level of detail about right (including some who said they could use less detail but were content to tolerate the current length), the rest found them too long.
  • Several folks especially liked both grouping articles by topic, and focusing each month primarily on one topic. I'll endeavor to keep that up, despite failing to do so this month.
Some opportunities to improve I'll be mulling over:
  • Set up a monthly journal club to talk about the papers (this one is already in the works, stay tuned for more info and let me know if you would like to provide input)
  • Make a lead theme more clear up front and include a short summary of the entire email
  • Tie each article to TNC's shared conservation agenda
  • Each quarter send a list of bullets of main issues under debate in conservation to encourage us to follow up
REFERENCES:
Chew, M. K. (2015). Ecologists, Environmentalists, Experts, and the Invasion of the “Second Greatest Threat.” International Review of Environmental History, 1, 7–41. Retrieved from http://www.academia.edu/14884830/Ecologists_Environmentalists_Experts_and_the_Invasion_of_the_Second_Greatest_Threat 

Evans, H. (2017). Do I make myself clear? Why writing well matters. Little, Brown, and Company: New York, NY. 416p.

Fisher, J. R. B. (2017). Global agricultural expansion – the sky isn’t falling (yet). In Kareiva, P., Silliman, B, and Marvier, M. (Eds), Effective Conservation Science: Data not Dogma. Oxford University Press, Oxford, UK, pages 73-79. https://global.oup.com/academic/product/effective-conservation-science-9780198808985?cc=se&lang=en&

Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., … Fargione, J. (2017). Natural Climate Solutions. Proceedings of the National Academy of Sciences, (6), 11–12. https://doi.org/10.1073/pnas.1710465114

Hallmann, C. A., Sorg, M., Jongejans, E., Siepel, H., Hofland, N., Schwan, H., … de Kroon, H. (2017). More than 75 percent decline over 27 years in total flying insect biomass in protected areas. Plos One, 12(10), e0185809. https://doi.org/10.1371/journal.pone.0185809

Holman, L., Head, M. L., Lanfear, R., & Jennions, M. D. (2015). Evidence of experimental bias in the life sciences: Why we need blind data recording. PLoS Biology, 13(7), 1–12. https://doi.org/10.1371/journal.pbio.1002190

Minasny, B., & Mcbratney, A. B. (2017). Limited effect of organic matter on soil available water capacity. European Journal of Soil Science, (2000), 1–9. https://doi.org/10.1111/ejss.12475

Mitchell, E. A. D., Mulhauser, B., Mulot, M., & Aebi, A. (2017). A worldwide survey of neonicotinoids in honey. Science, 111(October), 109–111. https://doi.org/10.1126/science.aan3684

Warren, R. J., King, J. R., Tarsa, C., Haas, B., & Henderson, J. (2017). A systematic review of context bias in invasion biology. PLoS ONE, 12(8), 1–12. https://doi.org/10.1371/journal.pone.0182502

Wednesday, October 18, 2017

New book: "Effective Conservation Science: Data not Dogma"

I have a chapter in a new book that was just published:
Effective Conservation Science: Data not Dogma (click the link to read more and buy it if you like).

The book has a really cool theme: what happens when we find evidence that contradicts what "everyone knows"? How do people react, and how do we resolve the disconnect?

In my case, while doing research for another book, I discovered that global land used for agriculture had actually been declining since 1998, despite the narrative that ag was rapidly expanding around the world.

I got a lot of pushback when I blogged about it a few years ago, and this chapter tells the story of what I found, what the reaction was, and what it all means going forward.

I really think the book is a great read based on the several chapters I've read so far, so if you're interested I encourage you to buy it. If you're not sure, you can read a review of a different chapter, or read the ugly (unformatted) version of my chapter here:
Global agricultural expansion: the sky isn't falling (yet)

Here's a map showing where around the world agriculture IS expanding, and where it's contracting:

Sunday, October 1, 2017

October science journal article summary

Calf

Here's some science to make your October outstanding! I have a 3-question survey about these summaries that should take a minute or less to answer; please consider taking it (or emailing me if you prefer). I'm trying to get a sense of how often people read them, whether the level of detail is right or not, and get any other feedback people have: https://www.surveymonkey.com/r/BCVDKQR


The focus of this review is on reducing the impacts of animal agriculture (especially cattle). For anyone who missed my June 2016 review, I'll re-recommend Herrero et al 2016 as a fantastic overview of the potential for improving GHG emissions in the livestock sector. Their top picks were improved feed digestibility (including more cereals, distiller's grains, etc. to supplement or replace grass and hay), feed additives, avoiding land use change through intensification, and carbon sequestration from better grazing.


ANIMAL AGRICULTURE:
There a lot of discussion on how to shrink the high carbon footprint of cattle (beef and dairy), and one focal area is on enteric methane (mainly cow burps). Hristov et al 2015 is a study showing that a feed additive (3NOP) was able to reduce dairy methane production by ~30% (with oddly similar impact regardless of the dose) without substantially affecting milk yield (although it increased weight gain by 80% over the 12 week period). This is a relatively small study (48 cows) and it would be see what the impact is throughout the life of dairy cattle (as often gut flora adapts to these kinds of additives over time), but this combined with a couple of similar studies they cite are exciting enough to be worth recommending more trails and pilots.

Kinley et al 2016 is a similar paper looking at a different feed additive (this one based on seaweed). This is only an in vitro study (messing with petri dishes rather than actual cows) but they found adding doses of 2% or greater to a grass diet virtually eliminated methane production. Note that a very similar paper (Machado et al 2015) had similar results, but with two key differences: they saw a strong benefit at 1% dose (where Kinley had a weaker response at that dose), and they also saw some side effects that could impact cattle health at 2% and above. While this was only in vitro and only tested for 3 days, it's still worth investigating and comparing to 3NOP for efficacy and potential positive and negative side effects.

Swain et al 2018 (it came out online early) is a paper from the Breakthrough Institute arguing for a shift to more intensive livestock systems, especially switching from grass-finished to grain-finished beef. They make a number of good points; it's not really debatable that feedlot cattle require less land and time, and most scientists agree the GHG emissions are lower per unit of meat in feedlot systems as well. They briefly discuss some of the potential tradeoffs including animal welfare and antibiotic use and how they might be addressed, and the issue of how to ensure that higher productivity actually leads to land sparing as opposed to driving more habitat conversion. While a good read, there are a few things they don't cover that should also be part of the conversation. One is that in some cases we may actually prefer a high land use footprint, if the grazing lands are high-quality natural grasslands that would otherwise be converted to other uses. But it's still a worthwhile read with good food for thought.

Odadi et al. 2017 (authored by a NatureNet fellow, along with TNC's Joe Fargione) looks at the impact of planned grazing (focus on intensive rotational grazing, but including several other factors) on a variety of outcomes in Kenya. They found substantial improvements in vegetation (cover, species richness and diversity, etc.), presence and richness of wildlife, cattle weight gain during dry periods when they were in poor condition, and the amount of cattle supported per unit of land area. The cool thing to highlight here is that they were able to improve cattle condition as well as wildlife habitat. One critical ingredient to success was more active involvement from pastoralists; this does mean more effort for them but it appears that the benefits make it worth promoting.

AGRICULTURE:

Remember the synthesis of evidence for how several agricultural practices impact a suite of outcomes that Rodd Kelsey led (I sent it out last month)? This month I'm sharing one more 2-page document, which has a great chart summarizing the evidence for each of the practices evaluated on each of the outcomes. If you want to explore more deeply, you can do so at http://www.conservationevidence.com/data/index/?synopsis_id[]=22



REFERENCES:
Herrero, M., Conant, R., Havlik, P., Hristov, A. N., Smith, P., Gerber, P., … Thornton, P. K. (2016). Greenhouse gas mitigation potentials in the livestock sector. Nature Climate Change, 6(May), 452–461. https://doi.org/10.1038/nclimate2925

Hristov, A. N., Oh, J., Giallongo, F., Frederick, T. W., Harper, M. T., Weeks, H. L., … Duval, S. (2015). An inhibitor persistently decreased enteric methane emission from dairy cows with no negative effect on milk production. Proceedings of the National Academy of Sciences of the United States of America, 112(34), 10663–10668. https://doi.org/10.1073/pnas.1504124112

Kinley, R. D., De Nys, R., Vucko, M. J., MacHado, L., & Tomkins, N. W. (2016). The red macroalgae Asparagopsis taxiformis is a potent natural antimethanogenic that reduces methane production during in vitro fermentation with rumen fluid. Animal Production Science, 56(3), 282–289. https://doi.org/10.1071/AN15576

Machado, L., Magnusson, M., Paul, N. A., Kinley, R., de Nys, R., & Tomkins, N. (2016). Dose-response effects of Asparagopsis taxiformis and Oedogonium sp. on in vitro fermentation and methane production. Journal of Applied Phycology, 28(2), 1443–1452. https://doi.org/10.1007/s10811-015-0639-9

Odadi, W. O., Fargione, J., & Rubenstein, D. I. (2017). Vegetation, Wildlife, and Livestock Responses to Planned Grazing Management in an African Pastoral Landscape. Land Degradation and Development, (March). https://doi.org/10.1002/ldr.2725

Swain, M., Blomqvist, L., McNamara, J., & Ripple, W. J. (2018). Reducing the environmental impact of global diets. Science of the Total Environment, 610–611, 1207–1209. https://doi.org/10.1016/j.scitotenv.2017.08.125

Friday, September 1, 2017

September science journal article summary

Kein Bett im Maisfeld Photo by Torsten Flammiger under Creative Commons

This month I've got a number of good papers, but I want to highlight two in particular. First, you know how hard it is to keep track of science, and Rodd Kelsey has just put out a book that summarizes the impacts of 20 different agricultural management practices (focused on Mediterranean climates). This will be a great reference for anyone working on ag. The other is a paper of mine that just came out. It's an analysis of the Camboriú water fund in Brazil with broadly useful suggestions on how to pick the right data source in a given context ("how much data is enough").

VALUE OF INFORMATION (VOI):
My new paper (Fisher et al 2017) is essentially an analysis for the Camboriú water fund of how the choice of input data impacts the decision you'd make as a result. We compared a relatively quick analysis on free 30m resolution data to a more complex analysis using 1m data. I'd recommend most people skip most of the paper (which is quite technical) and just start with the two blogs I wrote about it (an overview at https://blog.nature.org/science/2017/08/24/camboriu-data-for-water-funds/, and a more technical one for people working with spatial data at https://rsecjournalblog.wordpress.com/2017/08/25/how-much-data-is-enough-investigating-how-spatial-data-resolution-impacts-conservation-decision-making/). In short, we found that the simpler analysis would have led us to the same decision in Brazil, but that for other water funds the choice of data could be critical (as the ROI was over 1 with 1m data, but below 1 with 30m data). Table 5 and the discussion have several guidelines to consider in how to select whether relatively low or high resolution data is most appropriate for a given context. I'm pretty excited about that part of the paper, and I'd really welcome feedback on it from anyone so inclined.


AGRICULTURE:
Rodd Kelsey and his team just released a synopsis of the evidence synthesis they did for 20 different ag management practices and their effect on several ecosystem services (Shackelford et al 2017). It's a reference for finding information on a practice of interest, and a peer-reviewed version is forthcoming which will include expert assessment and scoring of the evidence as well. Everything in this book is also available and searchable online at http://www.conservationevidence.com under the Mediterranean Farmlands set of practices. I think this is a big step forward for TNC, especially for agriculture, but also as an example of what stepping up our evidence as part of CbD 2.0 can look like. Contact Rodd with any questions or comments you have.

Snyder 2017 is a useful reference with a lot of data on nutrient losses in the Mississippi River Basin and hypoxia in the Gulf of Mexico (as well as some global info, see Fig. 16). They show that overall the amount of nitrogen exported to the Gulf has trended down over the last 35 years (although with tons of annual variation, largely due to changes in precipitation) but phosphorous has trended up (Figs 10-13). Hypoxia in the Gulf is expected to lag way behind stream nutrient levels, and again is highly variable based on several climate variables each year, but Figure 14 shows that the average hypoxic zone from 2010-2015 is almost triple the size of the recently revised target for 2035 (<5,000 km2). So while it's not a surprise, this is more evidence that we really need to step up our game, especially given the projected impacts of climate change (see Sinha et al 2017 below) which will make our task significantly harder.


CLIMATE CHANGE:
You've probably heard about "payment for ecosystem services" (PES) where a land owner / manager is paid to do something (e.g. change how they farm) or to NOT do something (e.g. not cutting down trees they would otherwise clear). Until now there hadn't been a robust, fully randomized experiment to test how well they work. Jayachandran et al 2017 is a study looking at 121 villages in Uganda, half of which were paid for two years to not cut trees (with payments tied to area of intact forest as measured via remote sensing). The good news is that overall it worked well: participating villages deforested half as much as control (4.2% forest loss vs 9.1% loss), and there didn't seem to be leakage (cutting down other neighboring forests). It also appeared to be cost-effective (based on assumptions about how villagers would respond after the 2-year program ended). Remaining questions: what would happen under a long-term version of this program (or if it was actually abandoned after 2 years), could the program be adjusted to reduce deforestation even more from participants, how can program overhead costs (1/2 of total) be cut, and could there be side-effects on biodiversity or humans? The bigger question is whether or not this would scale, the authors note that only 1/3 of people they approached agreed to participate, that if scaled up nationally it could impact timber prices which could cause some rebound, and that weaker enforcement or monitoring in a large-scale effort could impact efficacy. The calculations on costs and benefits in particular are a bit tricky, let me know if you'd like to discuss further. I'd recommend only people involved in PES schemes actually read the paper (and a longer version I can share), for others check out https://www.theatlantic.com/science/archive/2017/07/paying-people-to-preserve-their-trees/534351/?utm_source=atltw and/or http://www.nber.org/digest/aug16/w22378.html for a good overview of the paper.

Several of you sent me articles about Harwatt et al 2017, which calculated how much impact replacing all beef consumed in the US with beans would have on climate change. Note that this paper doesn't model a real world scenario, rather it performs a very simple calculation by first calculating the GHG impact of switching from beef to beans (they ran it two ways, keeping total calories the same, and keeping protein intake the same), and then comparing that to the US 2020 GHG reduction targets under the Paris agreement. They found that this switch could meet between 46-75% of the US obligations (which is a lot), based almost entirely on Nijdam et al 2012 which provided the data on emissions. I have a few concerns about the methods of this paper; I don't see the US-specific data in the Nijdam paper they cite for it, and this paper's assertion that emissions in the US per kg of beef are almost double a global average appears contrary to the underlying paper's findings that intensive systems have much lower emissions. I'm guessing this may be due to inappropriately weighting culled dairy in Europe but I can't tell b/c they don't provide the detail. So while the general idea (we should eat more beans and less beef to fight climate change) is sound, I wouldn't trust these specific numbers.


CLIMATE CHANGE & AGRICULTURE:
Kim et al 2017 argues that especially warm weather in the Arctic has led to reduced vegetation growth (from forests to crops) in Canada and some of the U.S., primarily via colder temperatures (as well as less rain in South-central U.S.). In the U.S. crop yields were 1-4% lower on average as a result, up to 20% lower for corn yields in Texas (but with the majority of states unaffected, and only a few showing a very strong relationship). As with much of climatology, this is more about concerning patterns than ironclad proof of trouble ahead. But it makes a good point about some of the complex and unexpected impacts of climate change for us to watch out for.
There's a news article about the paper here: https://www.washingtonpost.com/news/energy-environment/wp/2017/07/10/the-stubbornly-persistent-idea-about-climate-change-that-just-wont-go-away/?utm_term=.9a374e5ce552 and you can read the full paper here.

Zhao et al 2017 also looks at how climate change may reduce crop yields, although through the lens of how global temperature increases will affect wheat, rice, maize, and soy yields. They draw on and summarize four independent analytical methods (historic data, field trial data, and both global and local crop models), which is a cool trick to increase confidence in the findings. On average, they predict each degree C increase will drop wheat yields roughly 6%, rice by 3%, maize by 7%, and soy by 3%. As you'd expect, results are quite spatially heterogeneous (including a few isolated positive effects), see Fig 3 for details. There are a lot of somewhat simplistic assumptions necessary to make these estimates work but they make a good case for temperature increases causing yields to drop on existing farms. Note that they did not account for shifting cultivation (e.g. moving plantings north to reflect new conditions) or other forms of adaptation.

One concern about climate change is the shift to more intense rain (causing more runoff, erosion, and flooding than steadier weaker rain), as well as increased rain in some areas (including the US). Sinha et al 2017 does some modeling based on climate projections to predict global changes in nitrogen loads in rivers (which leads to eutrophication in coastal waters, e.g. the dead zone in the Gulf), finding that they will increase substantially in 2070-2100 (with some increase 2031-2060). There are a lot of scenarios in the paper, but under "business as usual" for climate change they predict an overall increase in N loading of 19% for 20170-2100 (driven primarily by the Northeast, Upper Mississippi, and Great Lakes regions (see Fig 1 for details, Fig 2 is less useful since it groups areas with opposing trends). They note that simply to offset that increase, we would need to reduce nitrogen inputs to farms by 33%; to actually make progress on reducing eutrophication we would have to do substantially more. They also show other countries at risk of increasing N loading, especially India, parts of China, and SE Asia. It's worth noting there are a lot of assumptions in this paper, but the overall trend that moving to flashier rain is likely to make the problem with nutrient runoff from agriculture worse is something we need to be thinking about, especially if we are unsuccessful in limiting climate change. There's a news article about the paper at https://www.nytimes.com/2017/07/27/climate/nitrogen-fertilizers-climate-change-pollution-waterways-global-warming.html


REFERENCES:
Fisher, J. R. B., Acosta, E., Dennedy-Frank, P. J., Boucher, T., Kroeger, T., & Giberti, S. (2017). The impact of satellite imagery’s spatial resolution on land use classification and modeled water quality. Remote Sensing in Ecology and Conservation, 1–13. https://doi.org/10.1002/rse2.61

Harwatt, H., Sabaté, J., Eshel, G., Soret, S., & Ripple, W. (2017). Substituting beans for beef as a contribution towards US climate change targets. Climatic Change, 143 (1-2)(July), 261–270. https://doi.org/10.1007/s10584-017-1969-1

Jayachandran, S., de Laat, J., Lambin, E. F., Stanton, C. Y., Audy, R., & Thomas, N. E. (2017). Cash for carbon: A randomized trial of payments for ecosystem services to reduce deforestation. Science, 357(6348), 267–273. https://doi.org/10.1126/science.aan0568

Kim, J.-S., Kug, J.-S., Jeong, S.-J., Huntzinger, D. N., Michalak, A. M., Schwalm, C. R., … Schaefer, K. (2017). Reduced North American terrestrial primary productivity linked to anomalous Arctic warming. Nature Geoscience, 10(8), 572–576. https://doi.org/10.1038/ngeo2986

Shackelford, G. E., Kelsey, R., Robertson, R. J., Williams, D. R., & Dicks, L. V. (n.d.). Sustainable agriculture in California and other Mediterranean ecosystems. Synopses of Conservation Evidence Series. University of Cambridge, Cambridge, UK.

Sinha, E., Michalak, A. M., & Balaji, V. (2017). Eutrophication will increase during the 21st century as a result of precipitation changes. Science, 357(6349), 405–408. https://doi.org/10.1126/science.aan2409

Snyder, C. S. (2017). Progress in Reducing Nutrient Loss in the Mississippi River Basin – But Effects on Gulf Hypoxia Still Lag. IPNI: Peachtree Corners, Georgia.

Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., … Asseng, S. (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences, 201701762. https://doi.org/10.1073/pnas.1701762114

Friday, August 25, 2017

New paper and two blogs asking "how much data is enough?"



My new paper (Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality) is essentially an analysis for the Camboriú water fund of how the choice of input data impacts the decision you'd make as a result. We compared a relatively quick analysis on free 30 m resolution data to a more complex analysis using 1 m data. I'd recommend most people skip most of the paper (which is quite technical) and skip to the discussion, or even the two blogs I wrote about it.

The first blog explains the overall project and the paper at a high level here:
Camboriú Conservation Field Test: How Much Data is Enough?

I also wrote a second blog aimed specifically at people who actually do spatial analysis to guide them in picking the right source of remotely sensed imagery:
How much data is enough? Investigating how spatial data resolution impacts conservation decision making

In short, we found that the simpler analysis would have led us to the same decision in Brazil, but that for other water funds the choice of data could be critical. The return on investment was over 1 with 1m data, but below 1 with 30m data, meaning if financial return was the dominant factor this distinction would be critical.

Table 5 and the discussion have several guidelines to consider in how to select whether relatively low or high resolution data is most appropriate for a given context. I'm pretty excited about that part of the paper, and I'd really welcome feedback on it from anyone so inclined.


Tuesday, August 1, 2017

August science journal article summary

Bee (likely female Anthidium manicatum) on anise hyssop

Two significant articles came out in Science in June providing evidence for how neonicotinoids (a type of pesticide used for crop protection) are harming bees in field trials (there is some nuance, but the findings are concerning); I'm including reviews of those plus a few other  articles on the topic of pesticides and bees. Read to the end for an article of cattle intensification in Brazil, and a plea for scientists to write journal articles as if they wanted human beings to actually read and understand them.


BEE HEALTH / NEONICOTINOIDS / PESTICIDES:
If you don't want to read the two new studies, here are three stories about them (the one in the Guardian has more quotes from Syngenta pushing back on the findings, the Greenpeace one has a response to that critique from one of the lead authors). As background, it may help to know that in addition to honeybees (which most people are familiar with, they live in large hives) there are bumblebees (which live in much smaller colonies), and solitary wild bees (like the one shown in the photo above, taken in my garden). So when we talk about impacts of neonicotinoids or other pesticides (fungicides, other insecticides) on bees they are sometimes split by impacts on the colony (whether the colony dies out or not), lethal impacts on individual bees, and sublethal impacts (see below for details). So the science here is much broader than just colony collapse disorder in honeybees, which makes the results a bit more complex. For this summary I'm focusing only on bees as there is less science on impacts on other pollinators like butterflies and flies.
http://www.latimes.com/science/sciencenow/la-sci-sn-bees-pesticides-neonicotinoids-20170629-htmlstory.html
https://amp.theguardian.com/environment/2017/jun/29/pesticides-damage-survival-of-bee-colonies-landmark-study-shows
https://energydesk.greenpeace.org/2017/07/17/syngenta-bayer-ceh-study-neonicotinoids/

Tsvetkov et al. 2017 has three significant findings. The first is that some (not all) apiaries >3km from fields planted with neonicotinoid-treated seed still show neonicotinoids; the pollen analysis indicates that the contaminated pollen is coming from wildflowers (meaning that the neonicotinoids are being taken up by untreated plants relatively far from where the pesticide is applied). The second is that the lethality of neonicotinoids (clothianidin and thiamethoxam in this case) is significantly higher in the presence of a common fungicide called boscalid; boscalid on its own didn't harm bees but it made two neonicotinoids roughly twice as toxic when both pesticides were present in the same field. Third, they demonstrated several specific negative impacts on bees (mortality, "queenlessness," and declines in hygenic behavior) of exposure in the field to neonicotinoids at realistic doses. What makes this study different from earlier work showing harm is that rather than being lab-based they studied actual realistic doses and duration of exposure in the field. The best response to this research is tricky; simply banning neonicotinoids could potentially cause a shift to other pesticides that have been less studied (and may or may not be less toxic), and additional crop losses could potentially drive up food prices and lead to more habitat conversion. So more analysis on the trade-offs is needed, but this also appears to be the strongest evidence yet that in the real world neonicotinoids are harming bees (along with several other factors increasing their susceptibility).

Woodcock et al. 2017 has a lot more replication and their findings are less clear; they looked at 33 sites in the UK, Germany, and Hungary (all oilseed rape aka canola) that had seeds either untreated, treated with clothianidin, or treated with thiamethoxam (in addition to being treated with fungicides, other pesticides, and fertilizer as normal). They were looking for one of several potential impacts on honeybees, bumblebees, and solitary bees. Figure 2 shows how noisy the data is (a * indicates a significant effect); the two neonicotinoids often had a different effect across several metrics, and even stranger while they found negative effects of neonicotinoids in Hungary and the UK on honeybees, they also found positive effects in Germany (plus thiamethoxam had a positive effect on storage cells in the UK despite the negative impact of clothianidin there). They also found that reproductive impacts on wild bees were not well correlated with seed treatment, there was some correlation with total nest neonicotinoid residues (some of which appear to have come from earlier applications that remained in the landscape, indicating that impacts may persist for several years even if neonicotinoid use is halted). While there are some differences across the countries that could help to explain the difference in impacts, it's unclear to me why they would have seen positive impacts on honeybees in Germany, and makes me wonder what other variables may have been present that the researchers may not have accounted for. While this study doesn't present evidence as strong as the Tsvetkov paper, it also doesn't show that neonicotinoids are harmless, which makes me want to see more studies like this with lots of replicates but that are more tightly controlled. The lead author pushed back hard against the response from Bayer and Syngenta that this paper doesn't provide strong evidence of negative impacts: https://energydesk.greenpeace.org/2017/07/17/syngenta-bayer-ceh-study-neonicotinoids/

Rundlöf et al. 2015 is another important study of how neonicotinoids affect bees under real field conditions (as distinct from bees artificially fed neonicotinoids). They found impacts on wild bees (reduced density, total elimination of solitary bee nesting, and reduced bumblebee colony growth and reproduction) but did NOT see impacts on honeybees (unlike Tsvetkov). The authors note that some other research has found that honeybees do better than bumblebees with detoxifying after neonicotinoid exposure, and they also found bumblebees collected a higher percentage of pollen from the crop. Specifically this study looked at the neonicotinoid clothianidin in combination with the pyrethroid (insecticide) b-cyfluthrin and the fungicide thiram, based on common practice in Sweden.

Traynor et al. 2016 is another real-world study that looked at exposure to pesticides (measured by sampling bees, beeswax, and pollen) and how that related to colony survival and queen replacement. This is a complicated one so be warned. They found residues of 93 pesticides, and they provide detailed breakdowns of how common each one was, and how toxic it was to bees at the level detected (estimated via "hazard quotient" or HQ which is a model of lethality). Unsurprisingly, they found that when different pesticides that have the same  method of action (e.g. lumping organophosphates together as they work the same way) occured in the same sample they had a stronger effect. In addition to hazard quotient, they considered total number of pesticides each colony was exposed to, and the number of "relevant" pesticides (the ones at high enough levels they are expected to have a significant effect on bee mortality), and several different ways to measure impacts (it's a rich data set) but primarily having to do with lethality and queen replacement (they don't have the suite of sublethal effects the studies above report on). Anyway, the findings are complicated but they found a strong relationship between the total number of "relevant" pesticides and colony mortality within a month, overall number of pesticides exposed to over the study period was related to colony survival, and HQ was related to queen replacement. The strange thing is that this is a very simplistic model (as the authors acknowledge) but the findings could indicate that there are synergies between pesticides that are currently not well understood. Note that they did NOT find significant concentrations of neonicotinoids in the colony, which on the one hand means they couldn't evaluate the impact on colony health, but on the other hand simply finding low doses in hives is arguably good news. They DID find significant risk from two groups of fungicides (including chlorothalonil) and an insecticide group generally considered "bee-safe" (ecdysone receptor agonists). My take away from this study is that there are likely a ton of confounding effects and syngergies in these real-world studies, and that similar to the finding of Tsvetkov with boscalid and neonicotinoids together being much more toxic than separately, there are likely other combinations we're not aware of. This emphasizes the need for both lab studies to evaluate single chemicals in a controlled environment, but also more real-world studies which get at actual risk but will tend to have a lot more variation.

Simon-Delso et al. 2014 is a Belgian study similar to Traynor, comparing healthy honeybee colonies to colonies with a variety of disorders (e.g. dying out, queen loss, etc.) and looking for possible drivers or associated factors. They found that the virus load was not different between healthy and disordered colonies, and they did not see a relationship between disorders and the total number of insecticides or the total pesticide load (µg/kg). However, they did find a strong relationship between the number of fungicides present and disorders: they built a model estimating that ~26% of colonies without any fungicide would have disorders, vs. ~88% of colonies with 4 different fungicides. They also found that higher cropland area near the apiary increased the chance of disorders, while higher grassland area decreased it. Boscalid, cyprodinil, iprodione, and pyrimethanil were the most commonly detected fungicides; some of these are known to have synergistic effects with some insecticides, and/or to have metabolites which are significantly more toxic than the original formulation.

There's one more really interesting aspect to this research I couldn't resist including, as it provides a provocative twist. There has been a lot of debate and attention to the role of disesase in honeybee colony disorders, in paritcular viruses introduced via Varroa mites (as well as unrelated pathogens like Nosema ceranae). Sánchez-Bayo et al. 2016 is a review summarizing evidence that insecticides (neonicotinoids and fipronil) actually suppress the immune system of bees, so it's not as simple as asking whether the problem is insecticides or disease given the potential synergy. They reinforce the challenges in studying the impacts of a single stressor like neonicotinoids given relationships between Varroa mites, viruses, fungicides, insecticides, and other stressors. This is a well-written and engaging article, and if you're interested in bee diseases it'll be worth your time. If you're short on time skip to Figure 1 (a flow chart of how different stressors are related).


AGRICULTURE (RANCHING):
Merry & Soares-Filho is a study on cattle intensification in the Amazon and caused quite a splash. The authors argue (based on data from the US and Brazil, plus some conjecture about what is likely to occur in Brazil) that intensifying cattle production does not lead to conservation outcomes, BUT that conservation measures (removing land from production, better enforcement of laws, and eliminating subsidies and incentives that encourage expanding pasture) will actually lead to cattle intensification. They also note that aside from land use, intensification in the US has raised additional environmental and animal welfare concerns, and that to some degree significantly reducing beef consumption may be the most sure way to reduce beef impacts. Note that this study only shows data up to 2013, and in the last two years deforestation has substantially increased again in Brazil. As additional context, the CFA project that TNC is working on views deforestation-free corporate committments as the key driving conservation strategy, with support for intensification partly as a way to get buy-in from the cattle sector (who would oppose an approach limited to constraining production) and also to reduce leakage to other places with less regulated supply chains. So while we agree that intensification on its own wouldn't make sense, many TNC staff do see intensification as part of a successful strategy to address deforestation. You can read a story about the study here: https://news.mongabay.com/2017/06/is-intensification-of-beef-production-really-a-solution-to-amazonian-deforestation/


SCIENCE COMMUNICATIONS:
Doubleday and Connell 2017 argue that if scientists put more effort into writing well (not just accurately, but clearly and in a way that captivates readers) it would save us all time in reading these articles, and facilitate better understanding and collaboration. It's not a new point, but they make it well, and I especially like how they provide an alternative version of their abstract written in "The Official Style." They also do a good job talking scientists down from the immediate reaction that writing well means stooping to sensationalism, and provide good examples of the middle path. When I read articles like this, I am inspired, but I definitely will need help in actually overhauling my science papers prior to submission into something that would read well for a broad audience (but will not trigger peer reviewers to dismiss the paper as fluff). I imagine many of the non-scientists reading these summaries would be thrilled if the studies listed were easier to digest!


REFERENCES:
Doubleday, Z. A., & Connell, S. D. (2017). Publishing with Objective Charisma : Breaking Science’s Paradox. Trends in Ecology & Evolution. https://doi.org/10.1016/j.tree.2017.06.011

Merry, F., & Soares-filho, B. (2017). Will intensification of beef production deliver conservation outcomes in the Brazilian Amazon? Elementa: Science of the Anthropocene, 5(24).

Rundlöf, M., Andersson, G. K. S., Bommarco, R., Fries, I., Hederström, V., Herbertsson, L., … Smith, H. G. (2015). Seed coating with a neonicotinoid insecticide negatively affects wild bees. Nature, 521(7550), 77–80. https://doi.org/10.1038/nature14420

Sánchez-Bayo, F., Goulson, D., Pennacchio, F., Nazzi, F., Goka, K., & Desneux, N. (2016). Are bee diseases linked to pesticides? - A brief review. Environment International, 89–90(January), 7–11. https://doi.org/10.1016/j.envint.2016.01.0091

Simon-Delso, N., Martin, G. S., Bruneau, E., Minsart, L. A., Mouret, C., & Hautier, L. (2014). Honeybee colony disorder in crop areas: The role of pesticides and viruses. PLoS ONE, 9(7), 1–16. https://doi.org/10.1371/journal.pone.0103073

Traynor, K. S., Pettis, J. S., Tarpy, D. R., Mullin, C. A., Frazier, J. L., Frazier, M., & Vanengelsdorp, D. (2016). Inhive Pesticide Exposome: Assessing risks to migratory honey bees from inhive pesticide contamination in the Eastern United States. Nature Scientific Reports, 6(33207), 1–16. https://doi.org/10.1038/srep33207

Tsvetkov, N., Sood, K., Patel, H. S., Malena, D. A., Gajiwala, P. H., Maciukiewicz, P., … Zayed, A. (2017). Chronic exposure to neonicotinoids reduces honey bee health near corn crops. Science, 356(6345), 1395–1397.

Woodcock, B. A., Bullock, J. M., Shore, R. F., Heard, M. S., Pereira, M. G., Redhead, J., … Pywell, R. F. (2017). Country-specific effects of neonicotinoid pesticides on honey bees and wild bees. Science, 356(6345), 1393–1395.

Saturday, July 1, 2017

July journal article summary

This month I focused mostly on studies about the value of information, and if you're short on time I'd start with McGowan et al 2016 and Runge et al 2011.

If you're super excited about the book "data not dogma" (previewed a few months ago, it includes chapters from several TNC authors including myself), you can now pre-order it. It should be published in mid-October: https://global.oup.com/academic/product/effective-conservation-science-9780198808985?facet_narrowbybinding_facet=Paperback&lang=en&cc=se
The chapters I've read so far are very interesting, so hopefully it's worth checking out.

VALUE OF INFORMATION (VOI):
I have a paper coming out soon that examines the value of using high resolution (1m) vs coarser resolution (30m) spatial data in a water funds context, asking the question of whether or not it's worth buying the high-res imagery and spending a lot more time to analyze it, or if the coarse free data would lead you to make the same decision (stay tuned for details). It turns out there is a whole field around this called Value of Information (or VOI) - thanks to Timm Kroeger and especially Hugh Possingham for getting me started on this, as it is a theme in much of my research. For this month's review I've started getting up to speed on existing literature around this. I'm going to be doing a lot of thinking about VOI in the near to mid future so let me know if you'd like to discuss further.

McGowan and Possingham 2016 is a short commentary article on the topic of value of information (VOI), specifically looking at how movement ecology (related to wildlife tracking) can inform decision making. They emphasize the importance of translating broad goals (e.g. reversing the decline in salmon stocks) into quantitative objectives (e.g. boost salmon population to X by time Y, or intermediate objectives like removing river barriers so Z% of salmon enters upstream spawning habitat), and they provide a flow chart to help decide when to collect additional data vs. making a decision with the data you have (although a similar flow chart in the following article is more clear).

McGowan et al 2016 explores the idea of the article above more fully. The abstract actually sums up the paper quite nicely; it centers around asking two questions about animal telemetry data (although the concept applies much more broadly): 1) would (or could) I take a different action if I had more data, and 2) is the expected gain of making the different decision worth the cost ($ and time) to collect more data? She provides a continuum for how data is expected to be used from more abstract to highly concrete: pure research, engaging the public, raising awareness, tactical research, active adaptive management, and state-dependent management (e.g. quota-setting for harvestable species).

Runge et al 2011 shows a real-world example of applying VOI to whooping crane conservation (figuring out why it wasn't working), and I think it will really help conservationists to see how incorporating VOI can actually be useful (it's a good read), and not too technical. Essentially, there was a lot they didn't know, and many options for taking action. They evaluated many hypotheses for why whooping crane nests were failing (based on expert input), along with accompanying management actions to address each. The cool thing is that they found optimal strategies for each hypothesis, but also an optimal strategy if we had no additional information (suboptimal under any hypothesis, but useful across all of them). They also looked at the potential value of investigating each of the hypotheses and were able to determine which hypotheses were the most important to resolve, and what data would be most useful to resolve it.

Maxwell et al 2015 is an example of why considering the value of information is important. They looked at how to best manage a hypothetical declining koala population using a theoretical modeling framework that examined which management actions would be ideal depending on how much data you had (what was known and what was uncertain). They found that the optimal management decisions were fairly fixed (based on how cost efficient those options were), and that the value of collecting data on things like koala survival and fecundity (as well as how habitat cover affects mortality threats) was fairly low since it wouldn't lead you to make a different decision. The point is not that additional information is generally not useful, but rather that if more information won't lead you to make a different decision in support of your specific objectives, it's likely not worth spending much time and money on it.

If you have the patience to work through the equations and concepts in the two case studies, Canessa et al 2015 does a really nice job of explaining VOI in a quantitative way. Essentialy using expected probabilities for a range of variables (e.g. whether or not a disease is actually present at a given site, the chance of false positives or negatives of a test for the disease, etc.) and the expected outcomes of different scenarios, you can calculate how much value collecting data is likely to have in terms of your objective. Fig 1 makes the point that with more uncertainty the VOI is higher, and as our sampling density increases the VOI also increases. However, as the authors note, they do not include the issue of cost. There is the cost of collecting the actual information you need to support the decision, the time cost of actually running a formal VOI analysis, and potentially the cost of providing input data into the VOI analysis (e.g., if you don't even have credible guesses). Nonetheless, this is a great paper for understanding the key concept, and they provide spreadsheets for the two case studies.


GENERAL:
There is an increasing trend of greater transparency in science, and for the most part that's a very good thing. With more requirements to share data in public repositories we get better peer review, make it easier for researchers to build on each other's work, and improve the credibility of science. But a new essay (Lindenmayer & Scheele, 2017) makes a point near to TNC's heart: by sharing information on rare and endangered species (especially online) scientists are making it easier for poachers to find those species. TNC and NatureServe have dealt with this issue for a long time; our ecoregional portfolio sites (aka conservation areas) that were based primarily on rare species are typically buffered and sometimes only shared with other conservation organizations (removed from the public version of our data). This essay argues that in addition to facilitating poaching, it's upsetting landowners (who may be angry at scientists if trespassers start looking for rare species), and that even well-intentioned tourists can cause habitat damage in their search. Accordingly we should always be thinking about potential benefits vs harms in publishing this kind of data.

AGRICULTURE:
Roy et al 2009 is a good overview of life cycle assessments (LCAs), specifically in an ag context. They explain what they are (essentially a cradle to grave assessment of all of the inputs and outputs/impacts involved in producing a given product) & what the components of them are, give examples, list standards, etc.

REMOTE SENSING:
Mello et al 2013 uses a Bayesian network to estimate where current soybean production is most likely in Mato Grosso, Brazil. A Bayesian approach relies on expert input (and training data) to infer a variable of interest (in this case, soybean production) based on known context variables (e.g. distance to road, soil suitability, slope, etc.). Their accuracy ~90% was a lot higher than I'd expect; it's not clear to me whether the model is that good, or if the model is over-trained. Typically these kinds of models perform pretty well once you train them as long as drivers of the outcome variable don't shift much (e.g. if soy expands into smaller new fields in different areas, the model is much less likely to find them until it is updated). But it's a good overview of how Bayesian models work, and it looks like an approach worth replicating where we need crop maps that don't exist.

SOIL:
Minasny 2017 provides more detail on the "4 per mille" soil organic matter program (aiming to increase soil organic matter by 0.4% each year), including a suite of 20 case studies around the world showing what this target would look like in different places. They also provide a nice overview of different management strategies, key limitations, and compare what implementation would look like in different contexts.

REFERENCES:
Canessa, S., Guillera-Arroita, G., Lahoz-Monfort, J. J., Southwell, D. M., Armstrong, D. P., Chadès, I., … Converse, S. J. (2015). When do we need more data? A primer on calculating the value of information for applied ecologists. Methods in Ecology and Evolution, 6(10), 1219–1228. https://doi.org/10.1111/2041-210X.12423

Lindenmayer, B. D., & Scheele, B. (2017). Do not publish. Science, 356(6340), 800–801. https://doi.org/10.1126/science.aan1362

Maxwell, S. L., Rhodes, J. R., Runge, M. C., Possingham, H. P., Ng, C. F., & Mcdonald-Madden, E. (2015). How much is new information worth? Evaluating the financial benefit of resolving management uncertainty. Journal of Applied Ecology, 52(1), 12–20. https://doi.org/10.1111/1365-2664.12373

McGowan, J., & Possingham, H. P. (2016). Commentary: Linking Movement Ecology with Wildlife Management and Conservation. Frontiers in Ecology and Evolution, 4(March), 1–3. https://doi.org/10.3389/fevo.2016.00030

McGowan, J., Beger, M., Lewison, R. L., Harcourt, R., Campbell, H., Priest, M., … Possingham, H. P. (2016). Integrating research using animal-borne telemetry with the needs of conservation management. Journal of Applied Ecology, 54(2), 423–429. https://doi.org/10.1111/1365-2664.12755

Mello, M. P., Risso, J., Atzberger, C., Aplin, P., Pebesma, E., Vieira, C. A. O., & Rudorff, B. F. T. (2013). Bayesian networks for raster data (BayNeRD): Plausible reasoning from observations. Remote Sensing, 5(11), 5999–6025. https://doi.org/10.3390/rs5115999

Minasny, B., Malone, B. P., McBratney, A. B., Angers, D. A., Arrouays, D., Chambers, A., … Winowiecki, L. (2017). Soil carbon 4 per mille. Geoderma, 292, 59–86. https://doi.org/10.1016/j.geoderma.2017.01.002

Roy, P., Nei, D., Orikasa, T., Xu, Q., Okadome, H., Nakamura, N., & Shiina, T. (2009). A review of life cycle assessment (LCA) on some food products. Journal of Food Engineering, 90(1), 1–10. https://doi.org/10.1016/j.jfoodeng.2008.06.016

Runge, M. C., Converse, S. J., & Lyons, J. E. (2011). Which uncertainty? Using expert elicitation and expected value of information to design an adaptive program. Biological Conservation, 144(4), 1214–1223. https://doi.org/10.1016/j.biocon.2010.12.020