Monday, May 3, 2021

May 2021 science summary

Leeta sitting


Unfortunately this is the second month in a row with a memorial dedication. Our dog (and recent office-mate) Leeta recently passed away. She was an extremely sweet 'muttweiler' who lived with us almost 13 years. She provided incredible emotional support during the pandemic, when I was dealing with harsh peer reviews on scientific papers, etc. We're struggling to adjust, so if my email replies are slow or disappointing, that's likely why.

I've just got reviews of three miscellaneous articles I read before she died (on long-distance mammal migration, forest fragmentation & degradation, and remote sensing forest canopy height).

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

Teitelbaum et al. 2015 is an analysis of how far big herbivorous mammals migrate. They looked at 94 populations of 25 species migrating from between 10-1600 km (~6-1000 mi). The longest migrations were from caribou, onagers (a kind of Asian donkeys), and saiga antelope (figure 1). The biggest driver of migration distance was how much green vegetation was present - animals go much farther when food is sparser (table 1, figure 3). There's a nice discussion of other relevant variables, but overall they struggled to accurately model migration distance.

Grantham et al. 2020 estimates how much forests have been fragmented and modified around the world. They look at proximity to infrastructure, agriculture, and tree cover loss, along with lost forest connectivity, to estimate forest modification. The way they defined modification means that only forests in the most remote and sparsely populated areas are scored as having high landscape integrity (see figure 2 and figure 4), although this was still ~40% of global forest area. They find 56% of protected forests have high landscape-level integrity (table 2). I agree with the authors that forest modification and degradation is important, but I don't think the authors made a good case that a) their findings are new / surprising, or that b) just mapping proximity to people is a great way to estimate ecological degradation let alone prioritize conservation action. It's true that being farther from people is generally helpful to forests, but the flip side is that this paper heavily devalues the natural areas that people most appreciate for recreation and ecosystem services, even though with high ecological function.

Li et al. 2020 evaluates the new ICESat-2 LiDAR satellite's forest canopy height product in northeastern China, and uses machine learning applied to radar and optical satellite imagery (Sentinel-1, Sentinel-2, Landsat-8) to fill in the gaps between ICESat-2's sample points. They found ICESat-2 performed well (compared to aerial LiDAR) at moderate resolution (30-500m, with 250m performing best, and 10m and 1km performing relatively poorly). There are some issues (e.g., their aerial data was taken with deciduous leaves present, but ICESat data was taken in winter with leaves absent, and they note the correlations appear to vary by site so will always will require local calibration), but overall this confirms the utility of ICESat and offers some options for producing seamless forest canopy height maps.

Grantham, H. S., Duncan, A., Evans, T. D., Jones, K. R., Beyer, H. L., Schuster, R., Walston, J., Ray, J. C., Robinson, J. G., Callow, M., Clements, T., Costa, H. M., DeGemmis, A., Elsen, P. R., Ervin, J., Franco, P., Goldman, E., Goetz, S., Hansen, A., … Watson, J. E. M. (2020). Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nature Communications, 11(1), 1–10.

Li, W., Niu, Z., Shang, R., Qin, Y., Wang, L., & Chen, H. (2020). High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data. International Journal of Applied Earth Observation and Geoinformation, 92(April), 102163.

Teitelbaum, C. S., Fagan, W. F., Fleming, C. H., Dressler, G., Calabrese, J. M., Leimgruber, P., & Mueller, T. (2015). How far to go? Determinants of migration distance in land mammals. Ecology Letters, 18(6), 545–552.

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

Thursday, April 1, 2021

April 2021 science summary

Evanston, 1990s


I wanted to begin this summary by acknowledging the loss of a very important person in my life. Carol Gaetjens was my biological mom's partner who played a big role in raising me (Carol was my "co-mom" or my "other mom"). She recently died suddenly and unexpectedly, and her passing leaves a big hole in my heart. Here's her obituary for those who are interested.

Among the many things she taught me was to rethink my assumptions age and the elderly (through her work as a gerontologist). So I wanted to include an article she wrote about the perceptions of gerontology students on age diversity in the classroom in this month's summary.

I also wanted to thank her for more recently inspiring me to write a much shorter version of my last research article. When I sent her the full paper, she said she loved it, but wanted "a short version that I could pass out to my students that they'd actually read. You know, get to the good stuff without all that academic citation bullshit!" That kind of enthusiasm paired with clear and useful feedback was a hallmark of Carol's. When I sent her a draft of the short version, she liked it, but also griped about all of the other stuff in the full article she missed, before we concluded that maybe there was no perfect length of a summary.

Unrelated to that, I just had a couple of blogs about learning from failure published (after we failed to get it published at the first place we tried, appropriately enough). The first one summarizes barriers to learning from failure, and the second one has suggestions to make learning from failure easier and more productive.

I've also got a couple more articles about protected areas that people sent me in response to the March science summary I sent.

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

Gaetjens 1997 is a really interesting read about how gerontology students think about the value of age diversity in their programs. Unlike virtually any other program, in gerontology students vary widely in age (in Carol's sample from Northeastern University where she taught, students in the program ranged from 23 to 77. The paper is a pretty quick and easy read (let me know if you want a copy), but in short it covers their attitudes about the age diversity in the program, and if / how age should be used as a criteria for being accepted into the program and/or for financial aid. She found that most students spoke very positively about the value of age diversity, but a few middle aged and older students thought that people younger than 25 or 30 might not be mature enough to study aging, and that people over 70 may have cognitive issues that would frustrate them. She concluded that gerontology faculty had a key role to play in helping their students identify their ageist beliefs, and to work on reducing their bias through their studies.

Wolf et al. 2021 looks at how well protected areas (PAs) are reducing deforestation (compared to unprotected areas). Some PAs are in places where deforestation isn't happening regardless of protection, and other PAs are losing forests despite protection; neither kind helps conservation much (although the former may be useful if deforestation patterns shift and those PAs are enforced). They found PAs lost 41% less tree cover than similar unprotected areas. I'm not convinced that is an accurate number, but the important thing is that they found PAs are slowing deforestation but still losing a lot of trees. This shows the limits of a quantitative goal like 30 by 30 if it doesn't include a qualitative aspect including efficacy. PAs lost more tree cover when they were large and/or in regions with more deforestation overall. Fig 5 highlights countries with the most forest vertebrate species and the lowest forest protection. Note that this study looks at 'tree cover loss' rather than deforestation as land use change; some of the lost trees may be due to natural events or sustainable timber harvest that will allow the forest to regrow (as opposed to being permanently changed from forest to non-forest). This is important as tree cover loss in protected areas in countries like the U.S. is usually legal since some types of protection allow for timber harvest or other extractive uses.

Mogg et al. 2021 (a preprint, not yet peer reviewed) is an interesting global analysis of what protected areas (PAs) would be needed to minimize extinction risk of land mammals (by ensuring all species have 80% of their range protected and other IUCN Red List criteria). Unsurprisingly they find we would need a lot more protection: 60% of all land outside Antarctica, and >70% in Oceania and South America would have to be protected. But this analysis is solely driven by the needs of wildlife, and ignores the projected human need for food and livelihoods. In fact, they focus on areas suitable for agriculture (given the higher conversion threat) which makes it likely their recommended scenario would be unable to keep up with human demand for food. They also ignore capacity and resources needed for enforcement, which is already an issue, and would be much harder with a rapid expansion of PAs focusing on areas currently being converted. As a result, I imagine this analysis is non-convincing to anyone who isn't already strongly supportive of conservation as a top priority.

Bergstrom et al. 2021 assesses 19 ecosystems in Australia (2 fresh, 4 marine, 13 terrestrial), finding that all either have collapsed or are collapsing (on at least a local scale). Their definition of collapse is pretty broad: a documented transition to a new ecosystem state over >10 years, along with any substantial changes in: population size or keystone or characteristic species, ecosystem biomass, ecosystem engineers, or ecosystem services / functions (they consider the latter to be evidence that recovery is unlikely). They offer detailed information about each ecosystem in the supplement, and note that for collapsed ecosystems, the only options are accept the collapse, "renovate" the ecosystem to intentionally modify some elements to allow recovery, or "adapt" to a novel or highly modified ecosystem.

Provencher et al. 2020 makes a case that in prioritizing where to focus conservation effort, we should consider "biodiversity potential." This is partly about ecosystem condition or how many species are present relative to their potential maximum number of species, partly about threats that reduce that maximum potential, and partly about irreplaceability of the species and systems present. They use greasewood shrublands which live on salty soils as a case study; overall species richness is low but they still serve important functions. They note that groundwater withdrawals are a key threat that reduces the biodiversity potential of greasewood ecosystems, and call for more work on these and other undervalued ecosystems.


Bergstrom, D. M., Wienecke, B. C., Hoff, J., Hughes, L., Lindenmayer, D. B., Ainsworth, T. D., Baker, C. M., Bland, L., Bowman, D. M. J. S., Brooks, S. T., Canadell, J. G., Constable, A. J., Dafforn, K. A., Depledge, M. H., Dickson, C. R., Duke, N. C., Helmstedt, K. J., Holz, A., Johnson, C. R., … Shaw, J. D. (2021). Combating ecosystem collapse from the tropics to the Antarctic. Global Change Biology, October 2020, gcb.15539.

Gaetjens, C. A. (1997). Contributions of age diversity among students to graduate education in gerontology: Students’ view. In Educational Gerontology (Vol. 23, Issue 8, pp. 763–774).

Mogg, S., Fastre, C., Jung, M., & Visconti, P. (2019). Targeted expansion of Protected Areas to maximise the persistence of terrestrial mammals. BioRxiv, 3056, 1–22.

Provencher, L., Saito, L., Badik, K., & Byer, S. (2020). All systems are equal: In defense of undervalued ecosystems. Rangelands, 42(5), 159–167.

Wolf, C., Levi, T., Ripple, W. J., Zárrate-Charry, D. A., & Betts, M. G. (2021). A forest loss report card for the world’s protected areas. Nature Ecology & Evolution.


p.s. for my American readers, nothing in this summary is an April Fools' Joke. I don't mess around when it comes to science.

Wednesday, March 17, 2021

Learning from failure


Photo ©Jeff Reed 2.0 Generic (CC BY-NC-SA 2.0)

Do you find it difficult to even talk about failure, let alone learn from it? I've got a couple of new blogs which may be helpful.

The first one summarizes barriers to learning from failure:

The second one has suggestions to make learning from failure easier and more productive:

Monday, March 1, 2021

March 2021 science summary

Ice balls on dead flowers


As I write this, everything is encased in ice, so reading science with a cup of tea is pretty appealing!

I've summarized a few very useful articles on protected areas, all of which have useful insights (where to cite PAs for different goals, how they perform under climate change, and how to measure how well they protect a range of habitat types).

I've also got a paper on how flood damages have changed and how that relates to changing precipitation (and what we can expect with climate change), and one on shipping fuel regulations in China (impact on air quality, cost, and benefit:cost ratio).

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


Jenkins et al. 2015 highlights an inconvenient truth about protected areas in the United States: they are mostly located in places with relatively low species richness and threats of conversion. In other words, if the main goal of protected areas is to prevent as many species as possible from going extent, they're poorly sited. You can compare biodiversity maps in Fig 1 & 2 to PAs in Fig 3 to see the mismatch. Fig 4 has their recommendations for 9 areas where conservation should be focused in the SE and West coast.

Jantke et al. 2019 proposes a clever way to ensure that "% protected" goals like 30 by 30 (protecting 30% of a country on land and water by 2030) don't focus on easy to protect habitat types while other habitat types remain mostly unprotected. They suggest reporting “mean target achievement” where the % protected of each habitat type would be averaged and compared to a habitat-level goal (See section 2.2 for the equation - crucially achievement maxes out at 100% so overprotection in one habitat can't compensate for underprotection in another). They use Australia's Commonwealth Marine Reserve as an example; it protects 43% of the five marine regions it covers, but still falls short of its goal of protecting at least 10% of each of the 53 bioregions within it. This is a great complement to the total % protected indicator, as ecological representation has badly lagged behind total protection, and the rush to protect a lot more area very quickly will make it very tempting to focus on the easiest habitats to protect even though many other habitats have little to no protection.

Simmons et al. 2021 (a non-peer-reviewed white paper) looks at a few options to meet 30 by 30 in the U.S. with four different focal objectives (all also minimizing acquisition cost): area alone, carbon sequestration and avoided emissions, landscape connectivity, and climate-resilient species and habitat. It’s a fairly coarse and simplistic assessment, but it does a good job highlighting the kinds of tradeoffs to consider when deciding which lands we advocate for protecting. Check out Figure 2  which shows how their four scenarios perform (on cost, ecosystem representation, and climate mitigation) and where they would protect across the lower 48 states. They close with recommending clear objectives to prioritize where to protect, focus protections on threatened areas, develop metrics to track progress and impact (including on issues like social equity), and use diverse options (beyond traditional protected areas) to provide protection. Check out the appendix for maps showing which areas are already somewhat protected (as GAP 3).

Zhu et al. 2021 analyzes forested protected areas (PAs) in the Appalachians and asks how well they will provide future habitat to birds, mammals, reptiles, and amphibians after climate change (allowing for migration). They found that climate change would worsen suitable habitat that PAs provide for mammals and amphibians, while improving habitat suitability for birds and reptiles (if they are freely able to migrate, if not all groups of species would fare worse). They also found that threatened species are more likely to have their habitat worsen (see Table 2); NatureServe's global ranks G1->G4 would see a decline in suitable habitat (although only G3 was statistically significant, and G5 significantly improved) while endangered species did significantly worse (with vulnerable and other less threatened species seeing smaller and non-significant changes). They recommend focusing protection on areas of high species richness in the Blue Ridge (and the Cumberland plateau) to a lesser extent) which will remain suitable habitat under climate change. One key caveat: the main paper assumed a high emissions scenario (RCP 8.5), with a more moderate climate scenario (RCP 4.5) modeled in Appendix S4 / Table S2.


Davenport et al. 2021 analyzed historic data on precipitation and flood damages in the US. They found that as extreme precipitation (with maximum monthly precip being the most important) has increased over the past 30 years, so have flood damages. By comparing the recent past (30 yrs) to the prior 130 years, they estimate about 1/3 of recent flood damages ($73 billion) are attributable to the change in precipitation (Fig 3). They don't estimate how much of that is specifically due to anthropogenic climate change. Fig 5 has estimates of what we can expect in the future as climate change continues: the two left columns in the bottom row are the most useful. They represent the optimistic (column A) and pessimistic (column B) change in the top 1% of monthly precip. Note that even in regions expected to get dryer, the MAXIMUM rainfall will also increase (think flashier and more variable rainfall).


Zhu & Wang 2021 looks at the impact of regulations on shipping fuel in China. They found that ports w/ no penalty for non-compliance did not see improvements in air pollution, but others did. The total cost of the regulations was 4 times the price difference of the fuels, but the health benefits from the reduced pollution were >30 times as high as the cost.



Davenport, F. V., Burke, M., & Diffenbaugh, N. S. (2021). Contribution of historical precipitation change to US flood damages. Proceedings of the National Academy of Sciences, 118(4), e2017524118.

Jantke, K., Kuempel, C. D., McGowan, J., Chauvenet, A. L. M., & Possingham, H. P. (2019). “Metrics for evaluating representation target achievement in protected area networks.” Diversity and Distributions, 25(2), 170–175.

Jenkins, C. N., Van Houtan, K. S., Pimm, S. L., & Sexton, J. O. (2015). “US protected lands mismatch biodiversity priorities.” Proceedings of the National Academy of Sciences, 112(16), 5081–5086.

Simmons, B.A., Nolte, C., McGowan, J. (2021). Delivering on Biden’s 2030 Conservation Commitment. GDPC Working Paper 001/2021. Global Development Policy Center, Boston University.

Zhu, J., & Wang, J. (2021). “The effects of fuel content regulation at ports on regional pollution and shipping industry.” Journal of Environmental Economics and Management, 106, 102424.

Zhu, G., Papeş, M., Giam, X., Cho, S., & Armsworth, P. R. (2021). “Are protected areas well-sited to support species in the future in a major climate refuge and corridor in the United States?” Biological Conservation, 255(March), 108982.




Monday, February 1, 2021

February 2021 science summary

Broken apple slicer


I couldn't resist sharing the image above. When my apple slicer broke, the result seemed very nightmarishly 2020 (a piece of fruit full of sharp metal)!

I've got 5 articles on freshwater this month, plus one on conservation planning across land and sea. Also, if you missed the panel discussion I hosted about how scientists can improve their impact (with Lynn Scarlett, Yoshi Ota, Christian Pohl, and Mark Reed), I learned a lot so recommend it! The recording is available here: and their combined high-level advice is here:

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


The findings of Leal et al. 2020 may seem obvious, but they're important to highlight: conservation planning focused on terrestrial species only does a poor job at protecting freshwater biodiversity. They did some modeling in Brazil to look at trade-offs between freshwater and terrestrial species, and how to improve planning. Their low-bar recommendation is that even without data on freshwater biodiversity, just considering aquatic connectivity in additional to terrestrial species roughly doubles the benefit to freshwater species with almost no decrease in terrestrial benefits (Fig 3e & 3f, purple lines). If planning considers both terrestrial and freshwater biodiversity data, about a 5% decrease in terrestrial benefits leads to a ~400% increase in freshwater benefits (Fig 3e & 3f, aqua lines). This represents a strong case against assuming terrestrial work will do a good job at protecting freshwater ecosystems, and the idea of just including aquatic connectivity is an appealing entry point in places where better freshwater data are unavailable.

Improving water quality in agricultural landscapes (and downstream water bodies like the Gulf of Mexico) can be accomplished via changing inputs (e.g., using less fertilizer, or applying more stable forms at the right times), soil management to keep soil and nutrients in the field, edge-of-field practices like riparian buffers that intercept runoff, and through in-stream wetlands. Cheng et al. 2020 modeled how much nitrogen (N) wetlands remove from streams in the US (860,000 metric tons / year, and found that 10% more wetlands (+5 million hectares) could double N removal if they were in the right places (See Figs 3 & 5, although there are no big surprises here). This builds on other work about the essential role of wetlands in removing water pollution in concert with work on farms (e.g. Tomer et al. 2015 in JEQ), but highlights the need for landscape-scale planning and optimization for restoration to be as effective as possible. But one reason there are fewer wetlands in watersheds losing lots of N is that farmland tends to be productive and expensive there, and their proposal to double N removal requires losing 2% of total US cropland area. The authors also don't account for the potential increase in nitrous oxide which is a potent greenhouse gas. If you don't have the appetite for the whole article, this 1-page summary (Finlay 2020) has more detail:

King et al. 2021 offer a model of the costs and benefits of removing river barriers (dams, culverts, canal locks, and even natural waterfalls) in southern England. They find the benefits of barrier removal exceed the costs, but note that benefits are only estimated via reported willingness to pay for improved species richness and abundance (which were lumped in with more publicly accessible river bank, which I think could skew the data). They estimated a cost of >53 million pounds to remove all 650 barriers on the river Wey. My real take away is that removing barriers is expensive, but if we trust reported WTP, there may be support for fees that go to barrier removal if it is likely to lead to better recreational opportunities.

Lin et al. 2020 is a overview of how historic canals impact aquatic ecosystems (both positively and negatively), and opportunities to improve their management for conservation. It's global but focused mostly in Europe and North America. Canals can harm biodiversity by providing entry to non-native species and pathogens, allowing interbreeding which reduces genetic diversity, and serve as 'ecological traps' by attracting species that will die or be heavily stressed during drought or other events. On the other hand, canals can help biodiversity by providing connectivity and migratory pathways when rivers are fragmented, as well as provide refuges from human disturbance and climate change in some cases. Regardless, thoughtful management (or intentional abandonment) can improve environmental outcomes if done well. See Fig. 3 for broad examples,  Table 1 for variables that can inform management, and Fig. 4 for which management options relate to different objectives. The authors note that canals can be challenging to balance the human needs that the canals were originally built for with conservation objectives.

Tulloch et al. 2021 used Marxan w/ Connectivity for a case study (in Papua New Guinea) that looks at connections across land and sea and highlights intersections (like how forests and inshore reefs are connected). The idea was to improve on planning focused on a single realm (marine or terrestrial or freshwater). Fig 1 is a flowchart of the process they used. While the title mentions freshwater, they had no freshwater goals, and instead only used rivers as a connection between ecosystems on land and sea.

Cheng, F. Y., Van Meter, K. J., Byrnes, D. K., & Basu, N. B. (2020). Maximizing US nitrate removal through wetland protection and restoration. Nature, 588(7839), 625–630.

Finlay, J. (2020). Making the most of wetland restorations. Nature, 588, 592–593.

King, S., O’Hanley, J. R., & Fraser, I. (2021). How to choose? A bioeconomic model for optimizing river barrier mitigation actions. Ecological Economics, 181(March), 106892.

Leal, C. G., Lennox, G. D., Ferraz, S. F. B., Ferreira, J., Gardner, T. A., Thomson, J. R., Berenguer, E., Lees, A. C., Hughes, R. M., Mac Nally, R., Aragão, L. E. O. C., de Brito, J. G., Castello, L., Garrett, R. D., Hamada, N., Juen, L., Leitão, R. P., Louzada, J., Morello, T. F., … Barlow, J. (2020). Integrated terrestrial-freshwater planning doubles conservation of tropical aquatic species. Science, 370(6512), 117–121.

Lin, H. Y., Cooke, S. J., Wolter, C., Young, N., & Bennett, J. R. (2020). On the conservation value of historic canals for aquatic ecosystems. Biological Conservation, 251(February), 108764.

Tulloch, V. J. D., Atkinson, S., Possingham, H. P., Peterson, N., Linke, S., Allan, J. R., Kaiye, A., Keako, M., Sabi, J., Suruman, B., & Adams, V. M. (2021). Minimizing cross-realm threats from land-use change: A national-scale conservation framework connecting land, freshwater and marine systems. Biological Conservation, 254(July 2020), 108954.


Friday, January 15, 2021

Panelist recommendations on how to improve research impact

As part of a webinar with different perspectives on how scientists can improve the impact of their research (recording available at, I asked each panelist to share some advice and resources. Here are the answers from each of them.

Lynn Scarlett:

One complexity in exploring this science-decision maker intersection is that decision "types" vary significantly. Building issue awareness is different from informing regulatory analysis, which is different from developing, say, public sector resource management objectives and metrics (as in, for example, Everglades Restoration), and so on. The forms and processes and content of effective science-decision making interfacing vary significantly across different decision types.

I recommend three pdfs: the first is a slide deck of a speech I have given on science and decision making. It is shaped from the vantage point of a decision maker (rather than that of a scientist) but might offer food for thought of interest to the audience.  Because it is a slide deck, the points are in high-level bullet point form, but I think the points can be grasped, nonetheless. 

The other is a copy of Chapter 26 of the National Climate Assessment (US), 2014, on decision support, of which I was co-lead author with Richard Moss. While this is focused on decision support, it is, nonetheless, relevant to discussions of science impact on decision making. 

I also find a National Academy report "Informing Decisions in a Changing Climate" particularly insightful. 

This diagram shows that depending on the kind of science being done, the amount of co-development needed varies. So for example, foundational science on things like sea level rise can be impactful and useful even without much involvement from non-scientists.

Yoshi Ota:

My advice is as follows:

1. Consider what is the impact that you want. We often do not think about the large picture when we are not forced to do so. But this is important for both your motivation and for long-term planning. 

2. Consider the link between the impact and your activities. This is very difficult and many of us actually depend on “publishing in high impact journals''. However, this misses your opportunity to scope the work clearly and to discourage sequential thinking for problem solving. Think of this as an opportunity to open new ideas and impacts, so don't outsource!

3. Engage with the critical narrative. To engage with the process of Point 2 to explore new questions and perspectives as well as working on self-development, it is good to engage with critical narratives. Try not to think too much about their applications and be aware of simple logic. Embrace the complexity and enjoy the process of exploring. 

4. Be polite and be just: most important. I have been in many meetings where participants open their laptop while someone else is presenting. I even see this in stakeholder meetings where community representatives are speaking their opinion (in a second language) while scientists and NGO representatives are opening their laptops. During Nereus, the only person who never did this was Professor Jorge Sarmiento - a professor at Princeton and the most prominent scholar in our network. Also be just, meaning try to be the model for representing others and be aware of the unjust in the world. We feel vulnerable in these types of conversations but it is our duty not to dismiss them. 

Resources I recommend:

1) Nexus website

2) Nereus book 

3) Nereus website

4) COVID research report  

5) Recent paper: 

Mark Reed:

Put yourself in the shoes of those you want to help. 

More advice at

Christian Pohl:

Think of impact as something that starts with problem framing, then can happen through multiple planned and unplanned pathways and that might have unexpected outcomes.

More advice at “Ten Reflective Steps for Rendering Research Societally Relevant” 

Toolbox for co-producing knowledge:

Jon Fisher:

There are many small steps you can take to start improving your impact, and don’t be afraid to ask for help from others with complementary expertise.

More advice (based on our paper “Improving your impact: how to practice science that influences environmental policy and management”) at

Friday, January 1, 2021

Best of 2020 Science summaries

Sarah, Jon, and Leeta in the RV (Lumba)


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 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: 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

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

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

  1. The full paper (~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. 
  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: (~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
  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). 
  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).
  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).

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

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:

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.

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.

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.

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.

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.

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.

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.