Monday, August 3, 2020

August 2020 science article summary

Passion flower


I'm on vacation in the woods with no phone or internet access, but sending this via the magic of delayed delivery. Getting away from people doesn't have as much allure these days, but getting away from the news does!

I've been looking at a lot of papers lately around big global conservation goals (which should be interesting to most), as well as more technical papers around metrics and indicators (with less broad appeal). There's also a very cool paper just out in Science on plastic pollution (and how to reduce it), and a paper from Chile finding that subsidies to plant trees had the side-effect of increasing forest cover loss (while dramatically expanding plantations).

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Lau et al. 2020 is an important analysis of the scope of plastic pollution and how to reduce it. The paper found 29 Mt of plastic enters the environment each year (as of 2016, with ~1/3 going to the ocean), and plastic pollution to the ocean could triple by 2040 without immediate and sustained action. Current commitments by government and industry will only reduce the amount of plastic pollution to the ocean by 7% by 2040, but the report lays out eight measures that could reduce it by 80% by 2040 instead. There is a far better (and more thorough) summary of the paper at

Bhola et al. 2020 sums up four different philosophies or perspectives for setting global conservation goals. 1) extending Aichi biodiversity target #11 (protecting & managing 17% of land and inland water, plus 10% coastal and marine, while considering biodiversity, equity, ecosystem services, and connectivity) to 2030 and ensuring the qualitative piece is achieved. 2) Big area-based goals like 'half earth' or protecting 30% of the earth by 2030 (still ensuring that the right places get protected). 3) ‘New conservation’ (broadening the tent for conservation via ecosystem services, ecotourism, and the private sector). 4) ‘Whole earth’ conservation which attacks root causes of habitat loss like inequality and economic growth (while arguing against separating people from nature). It's a quick read but start w/ Table 1 for a summary of the four perspectives, and Figure 1 which shows how the choice of goal (in this case, biodiversity vs. ecosystem service production) affects which areas you’d want to protect. 

Allan et al 2019 is a preprint (not peer reviewed yet) but has some weight behind it via the author list. Their approach was to start with the union of all Key Biodiversity Areas (KBAs), all wilderness areas, and all current protected areas, then see how much extra land was needed to capture enough of the range of ~29k spp. to enable their persistence. Their answer is that we need 44% of the land on earth for conservation. Note that they do NOT say 44% should be legally protected, but rather than it should be managed via a range of strategies. While there's no one single "right answer" to how much land we need (since it depends on your values, and on the assumptions and data you use), this is one of many defensible ways to approach this.

Gownaris et al. 2019 reviews 10 global analyses (from the UN and NGOs) of which parts of the ocean are the most important for conservation (see Table 1 for a list of criteria used to define importance in each). See Figure 2 for the key results; they found 49% of the ocean was both unprotected and identified as important by at least one analysis. 45% of the ocean wasn't listed as important by any analysis, 40% was important in only 1 analysis, 14% was important in 2-4 analyses (of which 88% was unprotected: not covered by an MPA of any level of protection), and <1% was important in 5 or more (of which 5% was unprotected). Virtually all important area was in blocks larger than 100 km2, and 97% of the area listed by at least two analyses was within exclusive economic zones (EEZs). They note that they couldn't get at efficacy or strength of protection, but this is a useful high level overview of some likely candidates for both new protection and improved management and/or protection in existing MPAs.

Fraser et al. 2006 discusses three case studies where communities were involved in choosing sustainability indicators (both environmental and human), along with external experts. Each case talks about the process they used to choose indicators, and shares example indicators. They found participatory indicator development is complex and slow (sometimes preventing use by policy makers), but empowers communities. Table 1 has some human wellbeing indicators (including some flagged as unmeasurable but representing important gaps in knowledge). Table 3 shows environmental indicators seen as providing early warning of pastoral degradation. Table 4 has a broad suite of categories of metrics (w/o detail on how to measure them) for both human and environmental issues.

Tucker et al. 2017 is an overview of metrics of phylogenetic diversity (which they break into richness, divergence / relatedness, and regularity). There is a highly technical review of 70 specific metrics, followed by a note on other key considerations like abundance, how to weight rare vs common species, and how to deal with correlations related to species richness. This could be a useful reference to someone at a project scale who really wanted to think hard about how to measure biodiversity.

Heilmayr et al. 2020 found that subsidies in Chile to increase tree cover actually led to expansion of exotic plantations (doubling in size from 1986-2011), but decreased native forests (by 13%). They estimate that biodiversity probably declined as well, while aboveground carbon only increased by 2% despite the expansion of plantations.


Allan, J. R., Possingham, H. P., Atkinson, S. C., Waldron, A., Marco, M. Di, Adams, V. M., … Watson, J. E. M. (2019). Conservation attention necessary across at least 44% of Earth’s terrestrial area to safeguard biodiversity. BioRxiv, (November), 839977.

Bhola, N., Klimmek, H., Kingston, N., Burgess, N. D., Soesbergen, A., Corrigan, C., … Kok, M. T. J. (2020). Perspectives on area‐based conservation and its meaning for future biodiversity policy. Conservation Biology, 00(0), cobi.13509.

Fraser, E. D. G., Dougill, A. J., Mabee, W. E., Reed, M., & McAlpine, P. (2006). Bottom up and top down: Analysis of participatory processes for sustainability indicator identification as a pathway to community empowerment and sustainable environmental management. Journal of Environmental Management, 78(2), 114–127.

Gownaris, N. J., Santora, C. M., Davis, J. B., & Pikitch, E. K. (2019). Gaps in Protection of Important Ocean Areas: A Spatial Meta-Analysis of Ten Global Mapping Initiatives. Frontiers in Marine Science, 6(October 2019), 1–15.

Heilmayr, R., Echeverría, C., & Lambin, E. F. (2020). Impacts of Chilean forest subsidies on forest cover, carbon and biodiversity. Nature Sustainability.

Lau, W. W. Y., Shiran, Y., Bailey, R. M., Cook, E., Stuchtey, M. R., Koskella, J., … Palardy, J. E. (2020). Evaluating scenarios toward zero plastic pollution. Science, 21(1), eaba9475.

Tucker, C. M., Cadotte, M. W., Carvalho, S. B., Jonathan Davies, T., Ferrier, S., Fritz, S. A., … Mazel, F. (2017). A guide to phylogenetic metrics for conservation, community ecology and macroecology. Biological Reviews, 92(2), 698–715.


Wednesday, July 1, 2020

July 2020 science article summary

Vegetation in submerged tree stump


This month I have another short summary: one article on advice for scientists who want to work with policymakers, three on metrics, and two on wildlife migration.

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Hetherington & Phillips 2020 is a clear 10-step "how-to" guide for scientists to engage with policymakers. It focuses primarily on understanding, meeting with, and informing policymakers. It is a nice complement to our recent paper on a similar topic (available at, which focuses more on research design and skimps on how precisely to engage with policymakers).

Banks-Leite et al. 2011 compares how indicator species vs. landscape indicators perform in capturing underlying biodiversity and ecological condition. They found that landscape indicators (e.g. patch area, edge effects [via perimeter and core area using different edhe buffers], connectivity, and % forest cover) worked better than species-based indicators for most applications.

Skidmore et al. 2015 calls for the creation of a global standard for how to measure biodiversity using satellites. The ten variables they recommend are species occurrence, plant traits (e.g. specific leaf area or leaf N content), ecosystem distribution, fragmentation & heterogeneity, land cover, vegetation height, fire occurrence, vegetation phenology (variability), primary productivity & leaf area index, and inundation (presence of standing water).

Uuemaa et al. 2009 is a long and wonky review of landscape metrics used to capture different ecological attributes. Table 1  has a nice list of how well several species-specific variables relate to landscape metrics (e.g. one study found that overall % forest cover was well correlated w/ riparian woody species richness), although it is a list of results from individual  studies rather than a broadly representative meta-analysis or review.

LaCava et al. 2020 studied pronghorn in Wyoming, and found that despite their wide range (including several migration barriers), their genetics show that they are still interbreeding. So despite the challenges, their migration is successful enough to avoid isolation leading to genetic division.

Love Stowell et al. 2020 mapped out the genetics of 244 bighorn sheep in Wyoming (plus 109 more from Oregon, Montana, and Idaho used as sources for sheep brought to Wyoming). Fig 3 has the key results (for nuclear DNA) showing where the different genetically distinct herds live. They note that their mitochondrial results don't show the same pattern, possibly due to translocation or residual effects from formerly connected herds that are now fragmented. They conclude by calling for wildlife management to reflect genetic variation, balancing benefits and risks of translocation in particular (which reduces inbreeding, but can cause disease transmission).

Banks-Leite, C., Ewers, R. M., Kapos, V., Martensen, A. C., & Metzger, J. P. (2011). Comparing species and measures of landscape structure as indicators of conservation importance. Journal of Applied Ecology, 48(3), 706–714.

Hetherington, E. D., & Phillips, A. A. (2020). A Scientist’s Guide for Engaging in Policy in the United States. Frontiers in Marine Science, 7(June), 1–8.

LaCava, M. E. F., Gagne, R. B., Stowell, S. M. L., Gustafson, K. D., Buerkle, C. A., Knox, L., & Ernest, H. B. (2020). Pronghorn population genomics show connectivity in the core of their range. Journal of Mammalogy, (X), 1–11.

Love Stowell, S. M., Gagne, R. B., McWhirter, D., Edwards, W., & Ernest, H. B. (2020). Bighorn Sheep Genetic Structure in Wyoming Reflects Geography and Management. The Journal of Wildlife Management, jwmg.21882.

Skidmore, A. K., Pettorelli, N., Coops, N. C., Geller, G. N., Hansen, M., Lucas, R., … Wegmann, M. (2015). Environmental science: Agree on biodiversity metrics to track from space. Nature, 523(7561), 403–405.

Uuemaa, E., Antrop, M., Roosaare, J., Marja, R., & Mander, Ü. (2009). Landscape Metrics and Indices: An Overview of Their Use in Landscape Research. Living Reviews in Landscape Research, 3(1), 1–28.



Monday, June 1, 2020

June 2020 science journal article summary

Working on the porch with Leeta


I hope you're all doing well. I'm finding working on my porch when I can gives me a chance to safely get in some bird-watching and socially distant contact w/ neighbors.

I hurt my arm, so I'm relying on voice dictation to type which has slowed me down a lot. As a result, I'm only covering a few science articles this month. But they're all good ones! Although I'm biased as I'm an author of two of them (recommendations for scientists to improve their research impact, and a methods paper w/ an easy way for social science surveys to collect more spatial data).

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Fisher et al. 2020 is the paper I wish I had read when I started working as a scientist. It has clear recommendations for scientists to improve the impact of their research. We drew from our successes, failures, and suggestions from other colleagues and the scientific literature. Then we distilled all that into what we hope is a paper that is both practical and accessible to anyone. At a high level we recommend: (a) identify and understand the audience for the research; (b) clarify the need for evidence; (c) gather “just enough” evidence; and (d) share and discuss the evidence. For each we talk about why it matters and how to do it. You can read it at We are still working on a blog and 1-page version, but we do have a recording of a talk based on the paper here: Feedback is welcome!

Masuda et al. 2020 is a fairly simple methods paper. We show that during household surveys, asking respondents to draw spatial boundaries (e.g. of their farm plots) on digital tablets w/ ArcGIS Collector is relatively easy, accurate, and practical. By making the surveys spatially explicit, we could then both use the survey data to improve remote sensing, and use remote sensing data to spot discrepancies in the survey data. Essentially we felt this method delivered a lot of value for very little effort, and that it should be used much more commonly. There's a blog about it at

You've heard about the climate impacts of habitat destruction, but Goldstein et al. 2020 add a new twist. They identify which ecosystems have the most 'irrecoverable carbon,' which once lost can't be recovered in time to help with climate (see Figure 1). Figure 2 has the results; tropical peatlands followed by mangroves are clear priorities for protection (scored by total irrecoverable carbon rather than carbon density, although it's a similar ranking). The next tier is other peatlands, old-growth forests, marshes, and seagrasses. Other habitats generally have lower irrecoverable carbon. They note that they don't account for impacts of climate forcing, so boreal forest benefits are overestimated and tropical forests are underestimated. They only looked in the top meter of soil for peat, so over the long term the estimates for peat are likely on the low side. Finally, some of this carbon will be lost to climate change (e.g. thawing permafrost soils oxidizing) even without local conversion, so we should ensure protections for habitat actually address the dominant threat. You can read the article for free here:

Fisher, J. R. B., Wood, S. A., Bradford, M. A., & Kelsey, T. R. (2020). Improving scientific impact: How to practice science that influences environmental policy and management. Conservation Science and Practice, e0210.

Goldstein, A., Turner, W. R., Spawn, S. A., Anderson-Teixeira, K. J., Cook-Patton, S., Fargione, J., … Hole, D. G. (2020). Protecting irrecoverable carbon in Earth’s ecosystems. Nature Climate Change, 10(4), 287–295.

Masuda, Y. J., Fisher, J. R. B., Zhang, W., Castilla, C., Boucher, T. M., & Blundo-Canto, G. (2020). A respondent-driven method for mapping small agricultural plots using tablets and high resolution imagery. Journal of International Development.



p.s. If you'd like to keep track of what I write as well as what I read, I always link to both my informal blog posts and my formal publications (plus these summaries) at

Friday, May 1, 2020

May 2020 Science Journal Article Summary

Tartine sourdough bread


I hope you're all staying healthy, well fed, employed, and finding ways to stay connected.

This month I am tackling science articles discussing the relationship between COVID-19 and conservation (plus a couple related ones on air quality). It's not a representative sample of the literature. I drew from 1) articles either sent to me directly or 2) articles cited (in email or twitter or blogs I ran across) to support high-level conclusions, and I reviewed the ones that seemed both the most relevant and relatively high-quality.

I also don't think I'm qualified to weigh in. But the topic is unavoidable, and I would rather make the attempt than ignore it. I was prompted partly by Bob Lalasz' excellent post "The Wrong Kind of Serenity," even though I don't know if my expertise is sufficient to the task.

Most of the blogs and emails I've seen on this topic either seem to be in support of a clear agenda, or were written for a constrained audience and can't be shared. I especially welcome your feedback and perspectives on both this summary and the papers themselves. Please also send other papers & resources you have found the most useful (or problematic).

Overall, I didn't find a lot of consensus conclusions and recommendations. But there is agreement that the more humans (and our domesticated animals) live near to nature (especially when primates and bats are present), spend time in nature, and convert wildlife habitat (for settlements or food production), the more chance there is of being exposed to zoonotic disease (spread between animals and humans). It is also fair to say that reducing air pollution will have strong benefits to human health even if the potential link between COVID-19 mortality and poor air quality is not supported by additional research.

Finally, there is a lot of debate about whether higher biodiversity reduces the risk of disease transmission. The basic idea is that with more species there will be fewer compatible hosts for any given species. On the other side, as biodiversity declines some wildlife species prone to harboring zoonotic disease will be lost. I didn't read enough of this literature to come to an informed conclusion, but I'm not yet convinced. You can read a bit about it here (this article favors the idea that biodiversity loss worsens disease risk but includes a counterpoint):

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Smith & Guégan 2010 is a (long) summary of the origin and location of all human pathogens. It's useful context for thinking about COVID-19 and other zoonotic diseases. For emerging infectious disease in particular (diseases which are new or becoming more significant), about 3/4 of them are zoonotic. They also cite an earlier paper (Woolhouse & Gowtage-Sequeria 2005) that ranks the drivers of emerging pathogens (by # of pathogen species, NOT by impact), which from most to least spp. are: "(1) changes in land use or agricultural practices, (2) changes in human demographics and society, (3) poor population health, (4) hospital and medical procedures, (5) pathogen evolution, (6) contamination of food supplies or water sources, (7) international travel, (8) failure of public health programs, (9) international trade, and (10) climate change." Land-use change has been more key for bacterial and zoonotic disease, and mostly involves people coming into closer contact with nature and wildlife.

Morse et al. 2012 offers ideas from the past to predict and prevent the next zoonotic pandemic. It has a useful summary of how these pandemics emerge (see Panel 1), and Figure 1 has a global risk map which could inform monitoring. However, we have never predicted a pandemic before humans became infected. They recommend continued improvement of global monitoring to quickly identify novel pathogen outbreaks, while noting this has been a top recommendation for decades. Other suggestions seem impractical (better sanitation and biosafety practices in potential hotspots) or too expensive and complicated. For example, modeling which wildlife species are most likely to harbor emerging zoonoses (zoonotic diseases) would enable better monitoring. But there are thousands of potential pathogens to evaluate. The PREDICT program (from USAID) is listed as an example of a successful approach (combining collecting samples from wildlife and identifying the pathogens posing the most potential risk). Ironically the program ended fieldwork in September 2019, and was ended in March 2020 before being given an emergency 6-month extension in April 2020. It will be interesting when later analysis reveals whether or not they had the data needed to predict the emergence of SARS-CoV-2 / COVID-19.

I skimmed quite a few articles on the "dilution effect" arguing that higher biodiversity leads to lower disease risk before settling on Ostfeld & Keesing 2012 to review here. I like that they are methodical in exploring the issue, but it's a long article. I found the first half fairly unconvincing: modeling 'adding diversity' yields different results than much more likely cases of losing diversity, and the agricultural example doesn't apply well to zoonoses. But starting with p10 of the PDF (p166) there are useful case studies showing that diseases w/ generalist animal hosts like rodents will tend to be higher risk as diversity goes down. But despite showing biodiversity loss can raise disease risk, on p19 of the PDF (p175) they cite a metaanalysis finding that species richness had very little effect on zoonotic disease emergence. Instead human population density was the key factor. I look forward to reading more on this topic to have a more informed opinion.

Johnson et al. 2020 is a timely analysis of which mammals have the most potential to transmit disease to humans (estimated by the number of zoonotic virus species, an imperfect but useful proxy). Overall, the more common a species was, the more viruses they shared with humans. Domesticated mammals (12 species) were the highest-impact variable in their model, and they hosted 50% of all zoonotic virus species (not necessarily exclusively). 75% of virus species were hosted by either rodents, bats, and/or primates. Primates and bats host more viruses per mammal species, and bats in particular have traits that make transmission to other species more likely (rodents are significant partly because there are many common rodent species who live near humans). Finally, while they note that among threatened species virus richness goes up when the mammals are threatened by habitat loss, those species still have fewer virus species than more common mammals.

Faust et al. 2018 models how different rates and amounts of habitat loss impact the risk of zoonotic disease. The primary finding is intuitive: risk is fairly low when habitat loss is either very low (few humans in contact w/ nature) or very high (few wild populations in contact w/ people). So it's the mix of humans and natural habitat that poses more risk. In general, faster land conversion reduces exposure and thus risk. However, they note that fast conversion can also rarely lead to the largest outbreaks (where a lot of displaced species interact with a large pool of human hosts who are likely to mix with other humans). Figure 2 has interesting case studies of zoonotic diseases with different transmission modes, and Figure 5 shows how infection rates vary over time depending on rate of habitat loss.

Bloomfield et al 2020 asks what factors are associated with physical contact between humans and wild nonhuman primates (and thus potential concerns with disease exposure). They looked at smallholder farmers in Uganda living near forest patches (which they call "core") in an area with ongoing deforestation to create new farms and pastures (which along with settlements they call "matrix"). The results are not surprising: people who go to forests (for hunting & foraging for food, and/or gathering small trees for construction) or live in areas with more forest fragmentation have slightly higher chances of contacting primates.

Mills et al. 2010 summarizes the scant information on how climate change can affect zoonotic disease. They list four ways climate change can impact vector-borne zoonotic disease via changes to the host and/or vector: range shifts that result in contact w/ new human populatoins, changes in population density (leading to more or less human contact), changes in prevalence of infection (leading to more or less contact w/ infection), and changes in pathogen load (leading to more or less chance of transmission per contact). They list case studies of each, and note that while there is evidence of climate change having increased risk, there are many confounding variables not accounted for. For example, despite the mosquito host of Dengue and Zika becoming established in Texas, the diseases remain relatively rare despite nearby epidemics in Mexico (the difference may be due to more air conditioning lowering exposure in Texas). They close with a research agenda for the kinds of studies most needed to better understand how climate will impact zoonotic disease.

How does migration impact the risk of zoonotic disease? Altizer et al. 2011 find that it's complicated. Migrations can spread pathogens including to other species. They can also increase risk via reducing host immune function, and increasing exposure to pathogens for the migratory species. But migrations can also cause disease risk to go down by leaving parasites behind (and making it harder for parasites to reproduce in their absence), or removing infected animals from the population (since they're not fit enough to migrate, which may also provide selection pressure favoring less virulent pathogens). On net, migration may be bad for specialist pathogens, parasites that build up over time, pathogens transmitted via biting vectors or intermediate hosts, and pathogens transmitted mainly from adults to juveniles during breeding. Migration may help generalist parasites where there are shared stopover areas or wintering grounds, or help specialist pathogens that spread better with dense populations common during migration.

While poor air quality is a leading global health risk, I've only seen one study so far directly looking at how it impacts COVID-19 mortality (and it's a preprint, so it hasn't been peer-reviewed yet). Wu et al. 2020 looked at correlation between long-term U.S. air quality (specifically PM2.5: tiny particles < 2.5 μm in diameter) and found that fairly small increased in PM2.5 concentration (1 μg/m3) were associated with a 15% increase in COVID-19 death rate (compared to a 0.7% increase in the rate of all-cause mortality). They control for quite a few confounding variables (e.g. hospital beds, population, obesity, smoking, poverty, etc.), but note that limited testing means they can't properly control for outbreak size (which could be the primary driver of their results). Initial discussion has identified some other missing variables (like accounting for respiratory diseases like COPD or lung cancer), but this is still a useful data set to inform discussions and more research. The authors are also report they are publishing similar results for China and Italy. It's not clear whether short term improvements in air quality from the lockdown would make any difference. There are interesting comments on the initial version of the paper.

Zhang et al. 2019 shows how many lives can be saved by reducing air pollution, using an initiative in China (2013-2017) as a case study. The authors estimate that "national emissions of SO2, NOx, and PM2.5 decreased by 59%, 21%, and 33%, respectively." This reduction in PM2.5 (by ~20 μg/m3) avoided ~410,000 premature deaths. They have lots of detail about all the actions that made this possible, and Fig 4 shows how much each change contributed to avoided deaths; the biggest contributions came from stronger industrial emissions standards and upgrades to industrial boilers. Note that this study was published pre-COVID-19, so doesn't include potential benefits of reduced complications from the disease (although even the reduced levels in China are still much higher than the US).

Tessum et al. 2019 is a very short but useful look at racial inequity of PM2.5 air pollution. It is also a great overview of sources and risks of PM2.5 (see Fig 1), which they note is responsible for about 2/3 of US deaths from environmental causes. But their key finding is that black and Latinx people are exposed to 56% and 63% more PM2.5 than the relative amount of pollution caused by the goods and services they consume. Conversely, non-Latinx white people & other races (they lump whites with Asians, Native Americans, and all other races) are exposed to 17% less PM2.5 relative to their consumption. From 2003-2015, overall PM2.5 exposure dropped ~50% on average, while inequity decreased for black people but remained similar for others. Given the findings of the Wu 2020 preprint (that PM2.5 exposure increases COVID-19 mortality), this disease could further racial inequity. However, to date the CDC has found that while COVID hospitalized patients are disproportionately black, that there are fewer Latinx patients than in surrounding communities, so there are clearly other factors at play.

Altizer, S., Bartel, R., & Han, B. A. (2011). Animal Migration and Infectious Disease Risk. Science, 331(6015), 296–302.

Bloomfield, L. S. P., McIntosh, T. L., & Lambin, E. F. (2020). Habitat fragmentation, livelihood behaviors, and contact between people and nonhuman primates in Africa. Landscape Ecology, 35(4), 985–1000.

Faust, C. L., McCallum, H. I., Bloomfield, L. S. P., Gottdenker, N. L., Gillespie, T. R., Torney, C. J., … Plowright, R. K. (2018). Pathogen spillover during land conversion. Ecology Letters, 21(4), 471–483.

Johnson, C. K., Hitchens, P. L., Pandit, P. S., Rushmore, J., Evans, T. S., Young, C. C. W., & Doyle, M. M. (2020). Global shifts in mammalian population trends reveal key predictors of virus spillover risk. Proceedings of the Royal Society B: Biological Sciences, 287(1924), 20192736.

Mills, J. N., Gage, K. L., & Khan, A. S. (2010). Potential Influence of Climate Change on Vector-Borne and Zoonotic Diseases: A Review and Proposed Research Plan. Environmental Health Perspectives, 118(11), 1507–1514.

Morse, S. S., Mazet, J. A. K., Woolhouse, M., Parrish, C. R., Carroll, D., Karesh, W. B., … Daszak, P. (2012). Prediction and prevention of the next pandemic zoonosis. The Lancet, 380(9857), 1956–1965.

Ostfeld, R. S., & Keesing, F. (2012). Effects of Host Diversity on Infectious Disease. Annual Review of Ecology, Evolution, and Systematics, 43(1), 157–182.

Smith, K. F., & Guégan, J.-F. (2010). Changing Geographic Distributions of Human Pathogens. Annual Review of Ecology, Evolution, and Systematics, 41(1), 231–250.

Tessum, C. W., Apte, J. S., Goodkind, A. L., Muller, N. Z., Mullins, K. A., Paolella, D. A., … Hill, J. D. (2019). Inequity in consumption of goods and services adds to racial–ethnic disparities in air pollution exposure. Proceedings of the National Academy of Sciences, 116(13), 6001–6006.

Woolhouse, M. E. J., & Gowtage-Sequeria, S. (2005). Host range and emerging and reemerging pathogens. Emerging Infectious Diseases, 11(12), 1842–1847.

Wu, X., Nethery, R. C., Sabath, B. M., Braun, D., & Dominici, F. (2020). Exposure to air pollution and COVID-19 mortality in the United States. MedRxiv, 2020.04.05.20054502.

Zhang, Q., Zheng, Y., Tong, D., Shao, M., Wang, S., Zhang, Y., … Hao, J. (2019). Drivers of improved PM 2.5 air quality in China from 2013 to 2017. Proceedings of the National Academy of Sciences, 116(49), 24463–24469.



Wednesday, April 1, 2020

April 2020 Science Journal Article Summary

Cherry tree in bloom

I'm guessing that most of you are reading more science while stuck at home, but that you're focusing on science related to the pandemic. I certainly am.

But for now, I figured I'd send the usual kind of summary (focused on protected areas this month), since this is where my expertise lies. I thought of reviewing some articles on how conservation can both help and hurt infectious disease transmission (depending on context), but that felt crass.

If you have thoughts on these summaries (if they should pause, change, etc. during the pandemic) please let me know. If you know someone who wants to sign up to receive these summaries, they can do so at

Hannah et al. 2020 estimates that effectively conserving 30% of tropical land could cut predicted species extinction by ~1/2-2/3 (if the conserved areas are both cited ideally and managed well: this is not about legal protection alone). Conserving 50% could reduce extinction by more like 2/3-80% (see Table 1 for details including how this varies by region). This is useful to understand how effective conservation can be at different scales. But it's important to note that citing PAs in ideal locations continues to be elusive, this model relies on fairly simple assumptions using species-area curves, and the fact that the results didn't vary much with climate change (RCP2.6 vs RCP 8.5) is concerning. Nonetheless, this could be motivating to highlight the importance of protecting and managing enough of the right places on earth to slow species extinction.

How well does the current network of protected areas represent both biodiversity and the provision of ecosystem services in the tropics? Neugarten et al. 2020 has answers for five countries (Cambodia, Guyana, Liberia, Madagascar, and Suriname). They found that PAs are doing pretty well on biodiversity, forest protection, and forest carbon stocks, although with lots of room for improvement (Table 3). But PAs are not doing well on protecting non-timber forest products (like food and medicine) nor freshwater ecosystem services, both of which are mostly protected at about the same rate as land overall in each country (except Cambodia which did somewhat better on freshwater ecosystem services). Identifying opportunities to improve like this is critical to inform where to cite future PAs. They are up front about a few caveats: they looked only at designation of formal protected areas (rather than effective management on the ground), this may not be reflective of PAs across the tropics more broadly, and they had to rely on some squishy data (e.g. a mix of data sources and expert input to identify biodiversity priority areas). But it's still a good step to inform citing the next wave of PAs as interest in doing so ramps up across the globe. The authors have shared their data here: and are happy to help others to access and use it.

Wilhere 2008 makes an important point about analyses of how much conservation "is enough." He argues there's no single answer, since it depends on society's values for things like what risk of extinction is acceptable. Another key point is that the inputs into these models (which spp. or habitats to model and prioritize) are inherently value-driven as well. He recommends that these kinds of analyses: are transparent about the role of ethics / values (outside of science) in choosing conservation targets, recognize that any modeled policy options are only one of many possible choices, consider alternative targets to prioritize, and work with economists to produce cost estimates of any recommendations.

Wilhere et al. 2012 is a critique of one of the many 'half earth' papers arguing we need to effectively conserve at least half of the earth to avoid unacceptable biodiversity loss (Noss et al. 2012). The critique is similar to the Wilhere 2008 paper: the half earth target is presented as a "strict scientific point of view" without recognizing the value judgments that inform the results. They call for papers like Noss' to clearly articular the values of the author, and evaluate multiple policy options reflecting different values.

Finally, Armsworth et al. 2020 looks at  the best "bargains" exist for conservation: where the most species can be protected (from projected land conversion) for the lowest cost of land acquisition. In other words, how can we prevent the most species loss with a fixed budget for protection?
The new spatial prioritization model this is based on goes beyond binary models (which recommend protection or not), and instead allocates funding as a continuous variable. It also considers complementarity to avoid concentrating funding in areas rich with the same species. When they run the model for the coterminous U.S., attempting to conserve all species equally leads to the Southwest being a priority (since there's lots of cheap, intact habitat). But focusing on vertebrates vulnerable to extinction, priorities pop out in Texas (due to cave ecosystems with many unique & threatened species in small places) and the Southern Appalachians. There's a great discussion of how different assumptions and data inputs impact the results. There's a blog about this article here:

Richter et al. 2020 has two key points about water scarcity (and the resulting impact on freshwater ecosystems) in the Western United States. First, cattle feed is the biggest driver - 1/3 of water consumed in 17 Western states is for cattle feed, and in the Colorado River basin it's 55% (Table 1). But in good news, there is a proven affordable solution - paying farmers to temporarily fallow (stop growing crops) some or all of their land used for cattle feed. We also would need to reduce some of the water transferred between basins to fully address the over-allocation of water. The paper also has good data on which cities are driving the most scarcity via demand for beef, impact of water scarcity on fish (including extinction risk), and the cost of payments to farmers for fallowing ($82-241 million / year). Finally, one of the authors (Arjen Hoekstra) passed away last year, and I wanted to express how much I appreciate his pioneering work on water footprinting, and how much influence he had on me as a scientist. He will be sorely missed.

Hammerschlag et al. 2019 is a great overview of the many ecological functions and ecosystem services provided by aquatic predators (both marine and freshwater). It's well written enough to serve as a good introduction to the topic even for people like me with very little marine ecology background. Most of the benefits are fairly obvious, but benefits to climate mitigation (by reducing herbivores that can reduce carbon sequestration and storage) and inspiring products like boat coatings to reduce drag were especially interesting.

Armsworth, P. R., Benefield, A. E., Dilkina, B., Fovargue, R., Jackson, H. B., Le Bouille, D., & Nolte, C. (2020). Allocating resources for land protection using continuous optimization: biodiversity conservation in the United States. Ecological Applications, eap.2118.

Hammerschlag, N., Schmitz, O. J., Flecker, A. S., Lafferty, K. D., Sih, A., Atwood, T. B., … Cooke, S. J. (2019). Ecosystem Function and Services of Aquatic Predators in the Anthropocene. Trends in Ecology & Evolution, 34(4), 369–383.

Hannah, L., Roehrdanz, P. R., Marquet, P. A., Enquist, B. J., Midgley, G., Foden, W., … Svenning, J. (2020). 30% Land Conservation and Climate Action Reduces Tropical Extinction Risk By More Than 50%. Ecography, 1–11.

Neugarten, R. A., Moull, K., Martinez, N. A., Andriamaro, L., Bernard, C., Bonham, C., … Turner, W. (2020). Trends in protected area representation of biodiversity and ecosystem services in five tropical countries. Ecosystem Services, 42(January), 101078.

Richter, B. D., Bartak, D., Caldwell, P., Davis, K. F., Debaere, P., Hoekstra, A. Y., … Troy, T. J. (2020). Water scarcity and fish imperilment driven by beef production. Nature Sustainability.

Wilhere, G. F. (2008). The how-much-is-enough myth. Conservation Biology, 22(3), 514–517.

Wilhere, G. F., Maguire, L. A., Scott, J. M., Rachlow, J. L., Goble, D. D., & Svancara, L. K. (2012). Conflation of Values and Science: Response to Noss et al. Conservation Biology, 26(5), 943–944.

Stay safe, vigilant, and healthy,


p.s. If you'd like to keep track of what I write as well as what I read, I always link to both my informal blog posts and my formal publications (plus these summaries) at

Monday, March 2, 2020

March 2020 Science Journal Article Summary

Frozen waterfall


This month I've been focused on science practice and science writing (and some vacation) rather than science reading. So this is a mini-review with just three articles. Sorry!

If you know someone who wants to sign up to receive these summaries, they can do so at

Sanderson et al. 2020 make a straightforward but often overlooked point about soil carbon and grazing lands. In semiarid rangelands (like the Western Great Plains in the U.S.), the best way to maximize soil carbon is to prevent rangelands from being converted (to farms, housing, etc.) rather than changing grazing practices. Soil C increases from management are typically small and variable, while soil C losses from conversion are large and consistent. They do a great job of making this point and explaining why it's true. The only caveat is that they focus on soil C and not total GHG balance; considering methane and nitrous oxide of both rangelands and alternative land uses makes the net GHG impact more complex.

Global estimates of % protection hide the fact that protection varies widely for different  cosystems and habitat types. Sayre et al. 2020 splits that up into 278 natural ecosystems (based on temperature, moisture, elevation, land cover, etc). If you limit protection to IUCN 1-4 (stricter protection), 9 of those 278 were totally unprotected and 206 were below 8.5% protected (half way to Aichi targets). If you use IUCN 1-6 (including  areas allowing more human use) only 1/3 of ecosystems are below 8.5%. Table 5 shows how much of each major land cover group (forests, grasslands, etc.) has been lost, Table 4 has the details for the 278 ecosystems. Some figures are easier to see online:

I've been referring to the concept in Lehmann & Rillig 2014 for years but never actually reviewed it. Essentially they argue that we should distinguish between uncertainty and variation. Variation we can explain is not uncertainty: if we plant cover crops on 100 farms, and soil organic goes up in some and stays the same in others, but we can explain that with soil type and climate, it's not uncertainty. We just have to recognize that results will vary depending on a set of variables we can describe. Variation we CANNOT explain is uncertainty, e.g. if we run the same cropping experiment and farms with the same values for the variables we think are relevant still have different results, that represents uncertainty (that we don't yet know what drives outcomes). It's a useful framework in many context, for example the impact of conservation practices on water quality is extremely variable by context, but true uncertainty is fairly low.

Lehmann, J., & Rillig, M. (2014). Distinguishing variability from uncertainty. Nature Climate Change, 4(3), 153.

Sanderson, J. S., Beutler, C., Brown, J. R., Burke, I., Chapman, T., Conant, R. T., … Sullivan, T. (2020). Cattle, conservation, and carbon in the western Great Plains. Journal of Soil and Water Conservation, 75(1), 5A-12A.

Sayre, R., Karagulle, D., Frye, C., Boucher, T., Wolff, N. H., Breyer, S., … Possingham, H. (2020). An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems. Global Ecology and Conservation, 21(December), e00860.



p.s. If you'd like to keep track of what I write as well as what I read, I always link to both my informal blog posts and my formal publications (plus these summaries) at

Monday, February 3, 2020

February 2020 science journal article summary

Ice crystals on windshield


This month is a mix of topics; some papers came out recently that were too cool for me to wait to review with other similar ones, and I couldn't resist plugging the latest paper I worked on (Hamel et al. 2020). If you know someone who wants to sign up to receive these summaries, they can do so at

Also, a 12-minute video came out recently about the 2017 March for Science, and I show up in it a few times. It's called "SciComm: Raising Our Voice for Science and Public Policy," it's directed & produced by Larry Kirkman ( and Shannon Shikles at the Center for Environmental Filmmaking at American University, and you can watch it here:

Sippel et al. 2020 bucks the pattern where climatologists emphasize how distinct climate and weather are. Past research has shown the impact of climate change on certain weather events, but this paper actually detects the impact of climate change on any given day since late March 2012! By summarizing weather data all over the world, they found that we’ve been clearly outside of natural variability for the past 8 years on a daily basis. On a monthly basis, climate change has been detectable from global weather data since 2001. You can read a newspaper article about the paper at  I love the description of this paper from meteorologist Maria LaRosa: “[it’s] like looking closely at an impressionist painting –  you can't say what the picture is until you step back and look at the whole”

Chaplin-Kramer et al. 2019 produced global maps summarizing ecosystem services (sort of) for coastal protection, water quality regulation, and crop pollination, now and in 2050 (under three different scenarios). One twist is that they go beyond the usual definition of ecosystem services (benefits provided by nature and received by people who need them) to also look at the 'benefit gap' where there are people with needs nature is not currently meeting (see Fig 2, bottom row, pink / lavender color). There's a lot to explore here, but one finding is that both SE Asia and Africa are expected to have increasing gaps for all three services. There's plenty of uncertainty, but this is a great set of data to think about trade-offs under different future paths. You can explore their results in a web map at

Johnson et al. 2019 analyzed where it makes economic sense to protect undeveloped land within 100-year floodplains across the U.S. They compared  expected flood damages (over the next 30-50 years) to land acquisition cost (to prevent development and avoid damages). They found benefits exceeded acquisition cost for about 1/3 of unprotected natural areas, and that the strongest benefits were within the 20-year floodplain but outside of the 5-year floodplain. Compared to the 5-year floodplain, these areas are more likely to get developed even though they flood less often, leading to more potential damages. Figure 3 has a map of the counties with the highest benefit:cost ratio, focused in Appalalachia, Arizona, and a mix of other places. Note that buying undeveloped lands avoids the controversy associated with asking or forcing people already living within floodplains to move.

Brancalion et al. 2019 looks at opportunities to restore lowland tropical rainforests around the world. They evaluate both the benefits (including biodiversity, climate mitigation & adaptation, and water security) and feasibility (land opportunity cost, ecological uncertainty, and chance of forest persistence). Figure 2 shows where there's the most opportunity, both by area (Brazil) and by combined benefit and feasibility (Madagascar, Tropical Andes). Where to focus depends on your goal - the places with the most total benefits are generally less feasible (higher land costs and competition, e.g. areas where habitat loss is recent and ongoing). But Figure 3 shows several examples of countries with restoration commitments who appear to have large areas with relatively high benefits and feasibility.

Fahrig 2017 & Fahrig et al. 2019 are challenging but important reviews on the ecological impact of habitat fragmentation at the landscape scale (big areas). Their key findings are that with the amount of habitat loss being equal, fragmentation per se typically (70% of the time) didn't significantly impact biodiversity or ecological function at all. Even stranger, when it did have a significant impact, 3/4 of the time it was positive (even for threatened and rare species)! Section 5 in the 2017 paper summarizes the different explanations that authors of the primary studies provided, from fragmentation boosting functional connectivity (by reducing distance between patches) to edge effects and others. She concludes that in most cases we confound habitat loss with fragmentation, and that most of the time our intuition (that fragmentation per se is bad) is incorrect. She also notes that authors sometimes bury or caveat their findings of positive effects of fragmentation, which is one reason her findings continue to seem so wrong. My key take-aways are: 1) it's very hard (but very important) to examine our biases and deeply held beliefs when reading contrary science, 2) there is a good case made in the 2019 paper that small patches are under-protected, given their importance in many landscapes.

Jones et al. 2019 used GPS collars to track both migratory and resident pronghorn, and to model what features they avoided and which they ignored. They found that pronghorn were very reluctant to cross fences (consistent with under studies - they tend to crawl under rather than jump over), and avoided roads, but mostly ignored oil and gas well pads. There's a lot of other findings in their model, but the one I found most interesting was the concern that if ranches add more fencing to allow rotational grazing, it could have serious negative impacts on pronghorn and  mule deer unless wildlife-friendly fences are used.

Hamel et al. looks at how scientific information was perceived and used in decision making for a water fund in Brazil. Through interviews, we determined that the hydrological modeling and monitoring data was NOT used in designing and implementing the water fund. But counter-intuitively, having done the analysis using complex models and high-resolution data was seen as important for the water fund to be seen as scientifically credible. So ironically, even though the credible models were not actually used, their existence helped build support for the overall water fund. Despite this, as long as monitoring data was used to calibrate and validate the model, a simpler model (InVEST, as opposed to SWAT) and coarser data resolution (30m, as opposed to 1m) would have met the information needs of the users. We should have had more frank discussions up front with the ultimate users of the information to produce a model seen as credible and actually used, while avoiding over-investment in model complexity that wasn't needed.

Samanta et al. 2019 is a paper about a program in Michigan to improve water quality issues from agriculture via a really well thought out collaboration (w/ scientists, practitioners, universities, farmers, and industry). Lack of public funding has pushed many farmers to rely on private crop advisors, who don't always share conservation opportunities (like cover crops, reduced tillage, and nutrient management) with farmers (especially as tied to programs involving lots of paperwork). They found it was critical to improve active communication & trust at all levels (especially about funding available).  Conservation was constrained by funding, and potentially by the shift of crop advisors to often be less comprehensive and represent a single company. The authors emphasize the need to integrate social science like this from the very beginning of projects.

Brancalion, P. H. S., Niamir, A., Broadbent, E., Crouzeilles, R., Barros, F. S. M., Almeyda Zambrano, A. M., … Chazdon, R. L. (2019). Global restoration opportunities in tropical rainforest landscapes. Science Advances, 5(7), eaav3223.

Chaplin-Kramer, R., Sharp, R. P., Weil, C., Bennett, E. M., Pascual, U., Arkema, K. K., … Daily, G. C. (2019). Global modeling of nature’s contributions to people. Science, 366(6462), 255–258.

Fahrig, L. (2017). Ecological Responses to Habitat Fragmentation Per Se. Annual Review of Ecology, Evolution, and Systematics, 48(1), annurev-ecolsys-110316-022612.

Fahrig, L., Arroyo-Rodríguez, V., Bennett, J. R., Boucher-Lalonde, V., Cazetta, E., Currie, D. J., … Watling, J. I. (2019). Is habitat fragmentation bad for biodiversity? Biological Conservation, 230(October 2018), 179–186.

Hamel, P., Bremer, L. L., Ponette-González, A. G., Acosta, E., Fisher, J. R. B., Steele, B., … Brauman, K. A. (2020). The value of hydrologic information for watershed management programs: The case of Camboriú, Brazil. Science of The Total Environment, 135871.

Johnson, K. A., Wing, O. E. J., Bates, P. D., Fargione, J., Kroeger, T., Larson, W. D., … Smith, A. M. (2019). A benefit–cost analysis of floodplain land acquisition for US flood damage reduction. Nature Sustainability.

Jones, P. F., Jakes, A. F., Telander, A. C., Sawyer, H., Martin, B. H., & Hebblewhite, M. (2019). Fences reduce habitat for a partially migratory ungulate in the Northern Sagebrush Steppe. Ecosphere, 10(7).

Samanta, A., Eanes, F. R., Wickerham, B., Fales, M., Bulla, B. R., & Prokopy, L. S. (2019). Communication, Partnerships, and the Role of Social Science: Conservation Delivery in a Brave New World. Society & Natural Resources, 0(0), 1–13.

Sippel, S., Meinshausen, N., Fischer, E. M., Székely, E., & Knutti, R. (2020). Climate change now detectable from any single day of weather at global scale. Nature Climate Change, 10(1), 35–41.



p.s. If you'd like to keep track of what I write as well as what I read, I always link to both my informal blog posts and my formal publications (plus these summaries) at