Monday, November 2, 2020

November 2020 Science Article Summary


Happy post-Halloween!

This month I have five big global conservation papers, plus two on wildlife migrations. Also - my team is hiring! You can find out more and apply here: and let me know if you have any questions.

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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

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.

Strassburg et al. 2020 is a global prioritization of where to restore ecosystems on land. As with similar analyses they find we could achieve more at lower cost if we use analyses like theirs to drive the work. Fig 3 has the best comparison of cost and environmental benefits, while Fig 1 has maps of priority areas. However,  Maxwell et al. 2020 is a reminder that these decisions are NOT typically driven like papers like this, and Fig 1e raises immediate concerns about the likelihood of proposing to restore most of the Philippines and Indonesia, or 96% of converted habitat in the Caribbean. Scenario VI in Fig 3 shows how much lower the environmental benefits are (and that the cost is higher) if each country restores their highest priority 15% of lands relative to what's possible by concentrating restoration in relatively few countries (scenarios I-III). Despite the challenges, this paper does make a key point: given the relatively high cost of restoration relative to protecting intact habitat, it's important that we stretch those dollars by picking the right places to restore (including likelihood that restored lands won't get quickly reconverted).

The 5th Global Biodiversity Outlook report has mostly bad news - none of the 20 targets set in 2010 for 2020 have been met, although 6/20 have been partially achieved. Check out page 6 of the summary for policymakers for the results (green means met, yellow some progress, red no progress, and purple negative progress). Some of these are optimistic, e.g., it's very optimistic to assume that not only will 10% of the ocean be protected this year but that they will focus on areas of particular importance for biodiversity and ecosystem services. But you can read more about why they rated it this way on page 82 of the full report. It's worth at least looking at the high level scores for everything, and digging into the ones most relevant to your work.

van Rees et al. 2020 has 14 recommendations to improve freshwater outcomes in  the next version of the Convention on Biological Diversity (CBD) as well as the EU's biodiversity strategy. In brief, they are: don't lump freshwater in w/ lands and ocean when planning, recognize their role in supporting human life, recognize the importance of connectivity and barriers (like dams), manage freshwater ecosystems at the watershed / catchment scale, use systems thinking to consider trade-offs like how hydropower or intensive ag impacts on freshwater systems compared to others, improve existing freshwater protected areas (via restoration, management, and enforcement), use 'flagship umbrella species' to get freshwater biodiversity more attention, do more research on invasive species and how they impact freshwater ecosystems, improve monitoring of freshwater ecosystems, improve freshwater data's accessibility, use novel methods to monitor biodiversity like environmental DNA (eDNA) or digital text analysis, use strategic spatial planning, use more global data (like Red-Listed species) in national and local decision-making, and seek to better integrate top-down decision making by experts (due to technical complexity) with bottom-up stakeholder-driven approaches.

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.

Testud et al. 2020 evaluated crossings of amphibians (newts, frogs, toads, & salamanders) in tunnels under high-speed rail. Shorter tunnels led to more successful (complete) crossings for most species (but not toads), and broadcasting audio of frog mating calls led to a big increase in successful crossings (and crossing speed) for the one frog species who was included in the recordings. It would be interesting to follow up to see if more complex audio representing more species would work better, and even whether this approach might work for mammals as well.

Dinerstein, E., Joshi, A. R., Vynne, C., Lee, A. T. L., Pharand-Deschênes, F., França, M., … Olson, D. (2020). A “Global Safety Net” to reverse biodiversity loss and stabilize Earth’s climate. Science Advances, 6(36), eabb2824.

Greggor, A. L., Berger-Tal, O., & Blumstein, D. T. (2020). The Rules of Attraction: The Necessary Role of Animal Cognition in Explaining Conservation Failures and Successes. Annual Review of Ecology, Evolution, and Systematics, 51(1), annurev-ecolsys-011720-103212.

Maxwell, S. L., Cazalis, V., Dudley, N., Hoffmann, M., Rodrigues, A. S. L., Stolton, S., … Watson, J. E. M. (2020). Area-based conservation in the twenty-first century. Nature, 586(7828), 217–227.

Secretariat of the Convention on Biological Diversity. (2020). Global Biodiversity Outlook 5. Montreal, 208 pages. Available at

Strassburg, B. B. N., Iribarrem, A., Beyer, H. L., Cordeiro, C. L., Crouzeilles, R., Jakovac, C. C., … Visconti, P. (2020). Global priority areas for ecosystem restoration. Nature, (August 2019).

Testud, G., Fauconnier, C., Labarraque, D., Lengagne, T., Lepetitcorps, Q., Picard, D., & Miaud, C. (2020). Acoustic enrichment in wildlife passages under railways improves their use by amphibians. Global Ecology and Conservation, e01252.

van Rees, C. B., Waylen, K. A., Schmidt‐Kloiber, A., Thackeray, S. J., Kalinkat, G., Martens, K., … Jähnig, S. C. (2020). Safeguarding freshwater life beyond 2020: Recommendations for the new global biodiversity framework from the European experience. Conservation Letters, (April), 1–17.


Sunday, September 13, 2020

How scientists can improve their impact

Dog resting her head on her paws

This May a paper we've been working hard on for about 2.5 years finally came out! The basic idea is to provide tips for scientists to improve the chances that their research will have its desired impact. Essentially it's the paper my co-authors and I wish we had when we were starting as scientists. The dog picture above is 100% unrelated, sorry.

We have talked about this paper with well over a hundred people, and they all liked different things, and had different requests for accompaniments to it! Some wanted more context, some wanted a super-short version of it, some wanted video, etc. So we put together a whole package of resources (listed below and all available at; please take a look at whatever appeals to you.

  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).
  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)

Tuesday, September 1, 2020

September 2020 science article summary

Millipede with witches butter fungus 


This is another short summary with just four articles on biodiversity (bugs in the US, global indicators, tropical moist forest quality, and bias in conservation textbooks in terms of which taxa etc. get featured).

If you know someone who wants to sign up to receive these summaries, they can do so at (no need to email me).

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 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).

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.

Hoban et al. 2020 argue that new indicators are needed for a post-2020 CBD global framework for biodiversity. They recommend three new indicators: 1) # populations with effective population size above 500, 2) # current populations / # historic baseline of populations, 3) # species & populations w/ DNA-based genetic diversity monitoring, as well as keeping two existing CBD indicators (comprehensiveness of conservation of all species; and # of resilient, representative, and replicated plant genetic resources secured in medium or long-term conservation facilities). It's a fairly simple approach (albeit hard to empirically measure) for genetic biodiversity indicators.
Stahl et al. 2020 looked at 7 recent conservation textbooks and bias in what they focus on relative to natural prevalence (Fig 5 has a good summary). Some bias comes from underlying factors (research doesn't focus on species in proportion to their prevalence, more funding goes to charismatic species and richer countries), but regardless of the source they compared the proportion of examples to their prevalence on Earth. As you'd expect, the books favor examples using mammals over amphibians, North America over other continents, forests & coral reefs over other ecosystems, and tropical over temperate regions. It's an interesting topic, but there is at least one error (they claim only 3 of the textbooks mention ecoregions, but one of the other 4 discusses them at some length including an ecoregional map I created) which makes me wonder what else they could have gotten wrong (the author is looking into it and will get back to me). Ironically, the authors don't comment on potential bias in how they selected textbooks (e.g. only English language) or the methods they used (a focus on proportion of examples regardless of their value in explaining concepts). 

Crossley, M. S., Meier, A. R., Baldwin, E. M., Berry, L. L., Crenshaw, L. C., Hartman, G. L., … Moran, M. D. (2020). No net insect abundance and diversity declines across US Long Term Ecological Research sites. Nature Ecology & Evolution, (Table 1).

Hansen, A. J., Burns, P., Ervin, J., Goetz, S. J., Hansen, M., Venter, O., … Armenteras, D. (2020). A policy-driven framework for conserving the best of Earth’s remaining moist tropical forests. Nature Ecology & Evolution.

Hoban, S., Bruford, M., D’Urban Jackson, J., Lopes-Fernandes, M., Heuertz, M., Hohenlohe, P. A., … Laikre, L. (2020). Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biological Conservation, 248, 108654.

Stahl, K., Lepczyk, C. A., & Christoffel, R. A. (2020). Evaluating conservation biology texts for bias in biodiversity representation. PLoS ONE, 15(7), 1–11.


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).

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

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.

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

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).

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

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.

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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.