Best of 2020 Science summaries

Greetings,

I am currently on a COVID-safe RV vacation, but sending this via the magic of delayed delivery. I hope 2021 is off to a good start for you!

As usual, I'm kicking the new year off by providing summaries for my favorite 15 articles of 2020, so that if you missed any of them you get another chance to check them out. Despite the prominence of COVID-19 in most of our minds, I only included one of the papers I reviewed on the subject, as by now there is a lot of good information on the science available elsewhere. If you have somehow missed it this far, please do check out the summary of the paper we published on research impact last year - I am highly biased but it is my favorite!

Also - building on that paper, I'm moderating a discussion on how scientists can improve their impact with several experts on the subject (Christian Pohl, Lynn Scarlett, Mark Reed, and Yoshi Ota). It will be hosted by OCTO on Jan 28, 2021 11a-12:30p EST (4-5:30p GMT) and should be a lot of fun. You can register here.

Finally - want to work with me? My team is hiring both a human dimensions scientist and an aquatic (mostly marine) scientist. Let me know if you have questions.

If you know someone who wants to sign up to receive these summaries, they can do so at http://bit.ly/sciencejon. Here are the papers in alphabetical order:

Armsworth et al. 2020 looks at the best "bargains" 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: http://www.nimbios.org/press/FS_conservetool. Full disclosure: I'm working with the lead author on some follow-up research about trade-offs between different environmental goals.

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 http://viz.naturalcapitalproject.org/ipbes/

There have been a lot of papers documenting declines in invertebrate populations, from bees to flies, sometimes called the "insect apocalypse." But Crossley et al. 2020 use a large data set (from the Long-Term Ecological Research sites) to show that in much of the U.S., there's no clear trend (up or down) for invertebrate populations. For abundance, some species are declining in some places, others are increasing, and overall the trend is pretty stable on net (See Fig 2 for details, including the exceptions to that pattern). Diversity is similarly flat on net (see Fig 3). The discussion (on the page w/ Fig 3) of possible explanations for why this paper had different results from others is interesting. They include: 4/5 sites this paper included that another seminal paper omitted showed positive trends, total abundance trends across spp. heavily weight the most numerous spp. and dwarf other changes, and this paper relied on more recent data (where others have found a decline is slowing).

Dinerstein et al. 2020 is the latest paper advocating for conserving half of the earth (not all via legal protection). I like that they break down the primary conservation focus of each new area: rare species, distinct species assemblages (beta diversity), intact large mammal populations ('rare phenomena'), intact habitats (driven mostly by the Last of the Wild data which tends to rate rural farms as relatively intact), and high carbon stocks (see Figure 1 for a global map). Interestingly the big mammal cluster is 42% the size of current protected areas but stores 91% as much carbon. There's also a useful connectivity analysis: they find 4.3% of global land area would be needed to connect current protected areas (w/ ~3.5km wide corridors), and if their 50% target was met we'd still need 2.7% more to provide connectivity. About a third of targeted lands are indigenous territories which may already be effectively conserved in some cases. As a reminder, the 50% global target was picked arbitrarily, so describing these as 'science-based targets' is a bit misleading. They used science to identify places that add up to 50%, but the 50% overall target is NOT science-based. Check out their results at https://www.globalsafetynet.app/viewer/

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.

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 (or partners) 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. We put together a package of requested resources (listed below and all available at https://bitly.com/science-impact):

1. The full paper: http://impact.sciencejon.com/ (~6,000 words, but we use simple language so it’s a fairly quick and easy read). It has context for why this matters, specific recommendations, and examples of what each recommendation looks like in practice.
2. The need for this paper is covered in a Science brief on Cool Green Science (~500 words, 2.5 min reading time) –  it briefly explains the idea of the paper and not much else. https://blog.nature.org/science/science-brief/advice-for-scientists-who-want-to-practice-science-for-impact-influence/ 
3. The gist of the paper (a summary of the recommendations and brief examples) is available in a high level overview which also links to all of the products listed in this blog: https://bitly.com/science-impact (~900 words, ~4 min reading time). We also have a downloadable version of this overview to print and share (requested by a professor who wanted a short handout for her students) at https://www.scienceforconservation.org/assets/stories/Fisher2020-research-impact-2pager.pdf.
4. We talk about how we wrote the paper and what surprised us when writing it in an interview with OCTO (Open Communications for the Ocean) (~1,100 words, ~5.5 min reading time). https://meam.openchannels.org/news/skimmer-marine-ecosystems-and-management/how-do-science-so-it-influences-marine-policy-and 
5. There's more on why we wrote the paper and how scientists can start using it in a Cool Green Science interview (~2,500 words, ~12 min reading time). https://blog.nature.org/science/2020/08/17/how-to-practice-science-for-impact/
6. Finally, if you’d prefer video to text, we have a recording of a webinar about our paper which focuses on summarizing our recommendations and how they can help scientists avoid ‘wasting’ their research (22 minute presentation plus 35 minutes of discussion). https://vimeo.com/377150591#t=121s

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.

Greggor et al. 2020 argues that for conservation interventions to influence wildlife, it can help to think through the lens of animal cognition. It seems funny, but check out Fig 3 on “Why did (or didn’t) the chicken cross the road?” – they ask a really useful set of questions (like does the chicken see habitat on the other side and perceive it as better, does it see the road and see it as a danger, are danger cues masked, does it see the overpass and perceive it as safer, etc.). Fig 2 offers a decision tree to pick the right intervention, and the paper proceeds to offer several rules about how animal cognition and decision making tends to work to explain those recommendations. They note some limits, like omitting how animals deal w/ novelty, and how much is unknown about perception in many species.

Hansen et al. 2020 is a global analysis of moist tropical forest ecological quality and a great read. They use forests with high structural condition (meaning tall forests with several layers of understory trees and other plants, and high variation in plant size) and low human pressures as a proxy for overall ecological integrity (which typically also includes composition and function). The argument is that these forests have more habitat niches and can support more species, and that degraded structure is often due to stresses like logging which can have broad impacts (although they note limits of their approach up front). Fig 1 is a map w/ their results (& Fig 2 is a more helpful chart): they found 47% of remaining tropical moist forests had high integrity (both high structural condition and low human pressure, mapped as dark green), 33% had low structural condition (mapped as brown), and 20% had high structural condition but substantial human pressures (mapped as light green). 76% of the intact forest is in the Americas. In good news, forest w/ the best structure is being lost more slowly than more degraded forest (likely due to their remoteness, see fig 3). They have an ambitious suite of spatial recommendations in fig 4: extending protection to all remaining high integrity forests, plus restoration and working to reduce human pressure on the other forests.

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.

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 https://pew.org/32KPsgf

Maxwell et al. 2020 reviews how effective the last 10 years of new protected areas (PAs) have been in covering underprotected species and areas. The key finding is that PAs are not being added in the highest priority areas, and while some species are doing better than average in new protection, protection overall remains badly inadequate relative to the needs of species and ecosystems. On land PAs expanded by ~9% but only contributed to very small increases in representation (only increases in wilderness were significantly better than that 9%, while carbon and terrestrial key biodiversity areas expanded less than 9%, Fig 3b). At sea PAs more than doubled in area (+160%), with corals, cartilaginous fishes (like sharks), marine wilderness, and pelagic (open ocean) areas doing even better than that. But the expansion of marine PAs underperformed in increasing representation of marine reptiles & mammals, bony fishes, key biodiversity areas, and several others. The authors call for more transparency around decisions to add or expand (or shrink) PAs, improved recognition and management of Other Effective area-based Conservation Measures, better planning for climate change, more financing for protection and management, and more.

Global estimates of % protection hide the fact that protection varies widely for different ecosystems 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: https://www.sciencedirect.com/science/article/pii/S2351989419307231?via%3Dihub

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).

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.

REFERENCES:
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. https://doi.org/10.1007/s10980-020-00995-w

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. https://doi.org/10.1038/s41558-020-0738-8

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. https://doi.org/10.1098/rspb.2019.2736

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. https://doi.org/10.1002/jid.3475

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. https://doi.org/10.1016/S0140-6736(12)61684-5

Smith, K. F., & Guégan, J.-F. (2010). Changing Geographic Distributions of Human Pathogens. Annual Review of Ecology, Evolution, and Systematics, 41(1), 231–250. https://doi.org/10.1146/annurev-ecolsys-102209-144634

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. https://doi.org/10.1073/pnas.1818859116

Woolhouse, M. E. J., & Gowtage-Sequeria, S. (2005). Host range and emerging and reemerging pathogens. Emerging Infectious Diseases, 11(12), 1842–1847. https://doi.org/10.3201/eid1112.050997

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. https://doi.org/10.1101/2020.04.05.20054502

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. https://doi.org/10.1073/pnas.1907956116


Sincerely,
 
Jon