Tuesday, December 1, 2020

December 2020 science article summary

Cracking pecans with a garlic press


I've been falling behind on my science reading lately, but given the recent fires in the Pantanal (a South American wetland region) I thought I'd include a few papers on that, plus one bonus paper on fencing & wildlife. Next month I'll send out my usual "best of 2020" recap of my favorite articles that I read this year. 

If you know someone who wants to sign up to receive these summaries, they can do so at http://bit.ly/sciencejon

Alho and Sabino 2012 is a nice overview of the hydrology & biodiversity of the Pantanal, and how it has been modified by human activities. It covers the seasonal flooding (which inundates ~40-80% of the area), and how that relates to the species known to occur there (and notes that there are certainly a number of species not yet described by Western science). They flag hydroelectric dams and deforestation (mostly for pasture and farms) as two key threats to the hydrology. Dams reduce the floods in the wet season, and deforestation has reduced water retention, increased evaporative losses, and introduced invasive grass species which make the area more susceptible to fire. They also note overfishing, water pollution, and roads as additional threats.

de Oliveira 2014 compared riparian forests that were unburned to others that had burned roughly every four years, with variation in flooding frequency in both. They found that fire didn't affect species richness, but did reduce stem density and also substantially affected the species composition / proportion (with relatively few species in burned areas accounting for most of the plants in those areas, Fig 4). More days of floods reduced both species richness and stem density.

Lázaro et al. 2020 uses data on rain, streamflow, and satellite imagery to evaluate how the Northern Pantanal's hydrology has changed in recent years. They found that the Northern Pantanal has 13% more days without rain compared to the 1960s, plus a 16% drop in inundated area in August (the peak of the dry season) from 2008 to 2018. These changes are likely driven by both lower rainfall (delayed onset and shorter duration of the rainy season over the last decade) and hydrological modifications. Given the critical role of flooding to the Pantanal's ecology, this is a major threat to the viability of the ecosystems in the Pantanal. They note deforestation, water pollution from agriculture, and dredging for the passage of boats to ship agricultural commodities as additional threats beyond the changing hydrology.

Santos et al. 2020 reviews how one family of bats responded to 2005 fires in the Pantanal. They sampled six small forest patches (0.5-5 ha) both immediately and 3 months after a fire, of which three were completely burned, one was partially burned, and two were entirely unburned. They found that predatory bats were most abundant in burned patches right after the fire, but 3 months later those predators had been entirely replaced with few species of generalist fruit-eating bats (with the predators returning to unburned sites). It's a very small study on one family of bats, so may or may not be representative of fire response in the Pantanal more broadly.

Schulz et al. 2019 is an overview of the Pantanal, including it's environmental history, biodiversity, traditional use and management by humans (for fishing and low-intensity ranching), and current threats. It's a dense read with lots of good information, but the loss of traditional ecological knowledge is flagged as one interesting threat. Other key threats include deforestation, changing hydrology (from hydropower dams, deforestation, and climate change), river dredging, agricultural intensification, water pollution from upland areas near the Pantanal, overfishing (recreational and commercial). They find relatively little quantitative socio-economic data, lack of distinction between different subregions of the Pantanal, and many more research gaps.

McInturff et al. 2020 calls for 'fence ecology' as needing synthesis of existing research as well as more mapping and analysis of fences. The lack of good data on fences mean that barriers to migration and human footprint are likely underestimated. They model fence densities across most of 9 states in the Western US (Figure 2). They have a great summary of which species and ecosystem traits benefit or are harmed by different kinds of fences (Table 1), and even provide a typology of ecological impacts (Table 2), while noting that for every winner there are multiple losers. Finally, they synthesize 446 fencing studies and note several biases and related gaps. They found that research has focused on: economically important medium-sized ungulates (table 3), small plots (as opposed to large landscapes), few countries (the U.S., China, Australia, Botswana, and South Africa account for more than half of studies), conservation fencing (with livestock fencing and other kinds understudied), and impacts on the species a fence was built for (~2/3 of studies only studied impact on the target species as opposed to other species which may be impacted). They close by calling for policy action on wildlife-friendly fencing design and placement, and on removing fencing (and limiting new construction). There's a blog about this paper at https://theconversation.com/fences-have-big-effects-on-land-and-wildlife-around-the-world-that-are-rarely-measured-147797

Laskin et al. 2020 compares how different fence designs fare at preventing bison from crossing while letting other wildlife pass through. As you might expect, the most open fence was the most permeable for wildlife (with just 2 wires, 80cm and 100cm off the ground). The authors found that they could adjust the fence to a 5-wire configuration as needed to better contain bison despite being worse for wildlife, then adjust it back to 2-wire when bison are no longer expected to interact with the fence.

Alho, C. J. R., & Sabino, J. (2012). Seasonal Pantanal Flood Pulse: Implications for Biodiversity Conservation – a Review. Oecologia Australis, 16(4), 958–978. https://doi.org/10.4257/oeco.2012.1604.17

de Oliveira, M. T., Damasceno-Junior, G. A., Pott, A., Paranhos Filho, A. C., Suarez, Y. R., & Parolin, P. (2014). Regeneration of riparian forests of the Brazilian Pantanal under flood and fire influence. Forest Ecology and Management, 331, 256–263. https://doi.org/10.1016/j.foreco.2014.08.011

Laskin, D. N., Watt, D., Whittington, J., & Heuer, K. (2020). Designing a fence that enables free passage of wildlife while containing reintroduced bison: a multispecies evaluation. Wildlife Biology, 2020(4). https://doi.org/10.2981/wlb.00751

Lázaro, W. L., & Oliveira-júnior, E. S. (2020). Thematic Section : Opinions about Aquatic Ecology in a Changing World Climate change reflected in one of the largest wetlands in the world : an overview of the Northern Pantanal water regime. Acta Limnologica Brasiliensia, 32, 8.

McInturff, A., Xu, W., Wilkinson, C. E., Dejid, N., & Brashares, J. S. (2020). Fence Ecology: Frameworks for Understanding the Ecological Effects of Fences. BioScience, 70(11), 971–985. https://doi.org/10.1093/biosci/biaa103

Santos, C. F., Teixeira, R. C., Raizer, J., & Fischer, E. (2020). Post-fire phyllostomid assemblages in forest patches of the Pantanal wetland. Mammalia, 1–4. https://doi.org/10.1515/mammalia-2020-0037

Schulz, C., Whitney, B. S., Rossetto, O. C., Neves, D. M., Crabb, L., de Oliveira, E. C., … Saito, C. H. (2019). Physical, ecological and human dimensions of environmental change in Brazil’s Pantanal wetland: Synthesis and research agenda. Science of the Total Environment, 687, 1011–1027. https://doi.org/10.1016/j.scitotenv.2019.06.023

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 http://sciencejon.blogspot.com/

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: https://jobs-pct.icims.com/jobs/6374/job and let me know if you have any questions.

If you know someone who wants to sign up to receive these summaries, they can do so at http://bit.ly/sciencejon

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

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. https://doi.org/10.1126/sciadv.abb2824

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. https://doi.org/10.1146/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. https://doi.org/10.1038/s41586-020-2773-z

Secretariat of the Convention on Biological Diversity. (2020). Global Biodiversity Outlook 5. Montreal, 208 pages. Available at https://www.cbd.int/gbo5

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). https://doi.org/10.1038/s41586-020-2784-9

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. https://doi.org/10.1016/j.gecco.2020.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. https://doi.org/10.1111/conl.12771


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 https://bitly.com/science-impact); please take a look at whatever appeals to you.

  1. The full paperhttp://impact.sciencejon.com/ (~6,000 words, but we use simple language so it’s a fairly quick and easy read). It has context for why this matters, specific recommendations, and examples of what each recommendation looks like in practice.
  2. The need for this paper is covered in a Science brief on Cool Green Science (~500 words, 2.5 min reading time) –  it briefly explains the idea of the paper and not much else.
  3. The gist of the paper (a summary of the recommendations and brief examples) is available in a high level overview which also links to all of the products listed in this blog: https://bitly.com/science-impact (~900 words, ~4 min reading time). We also have a downloadable version of this overview to print and share (requested by a professor who wanted a short handout for her students).
  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)
  7. Wondering what other people think is most important to improve scientific impact? This video recording of a panel discussion with four different research impact experts offers additional insights, and you can also read their advice as a blog. Or here's a recording of a second panel discussion focused on Latin America, the Caribbean, and African contexts.
  8. Finally, I shared some of the challenges in writing this paper (finding co-authors, dealing with critique, etc.) in an interview at Wildhub.

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 http://bit.ly/sciencejon (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). https://doi.org/10.1038/s41559-020-1269-4

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. https://doi.org/10.1038/s41559-020-1274-7

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. https://doi.org/10.1016/j.biocon.2020.108654

Stahl, K., Lepczyk, C. A., & Christoffel, R. A. (2020). Evaluating conservation biology texts for bias in biodiversity representation. PLoS ONE, 15(7), 1–11. https://doi.org/10.1371/journal.pone.0234877


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 http://bit.ly/sciencejon

Lau et al. 2020 is an important analysis of the scope of plastic pollution and how to reduce it. The paper found 29 Mt of plastic enters the environment each year (as of 2016, with ~1/3 going to the ocean), and plastic pollution to the ocean could triple by 2040 without immediate and sustained action. Current commitments by government and industry will only reduce the amount of plastic pollution to the ocean by 7% by 2040, but the report lays out eight measures that could reduce it by 80% by 2040 instead. There is a far better (and more thorough) summary of the paper at https://pew.org/32KPsgf

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. https://doi.org/10.1101/839977

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. https://doi.org/10.1111/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. https://doi.org/10.1016/j.jenvman.2005.04.009

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. https://doi.org/10.3389/fmars.2019.00650

Heilmayr, R., Echeverría, C., & Lambin, E. F. (2020). Impacts of Chilean forest subsidies on forest cover, carbon and biodiversity. Nature Sustainability. https://doi.org/10.1038/s41893-020-0547-0

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. https://doi.org/10.1126/science.aba9475

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. https://doi.org/10.1111/brv.12252


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 http://bit.ly/sciencejon

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 http://impact.sciencejon.com/, 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. https://doi.org/10.1111/j.1365-2664.2011.01966.x

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. https://doi.org/10.3389/fmars.2020.00409

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. https://doi.org/10.1093/jmammal/gyaa054

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. https://doi.org/10.1002/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. https://doi.org/10.1038/523403a

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. https://doi.org/10.12942/lrlr-2009-1



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 http://bit.ly/sciencejon

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 https://conbio.onlinelibrary.wiley.com/doi/full/10.1111/csp2.210 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: https://www.openchannels.org/webinars/2019/improving-your-impact-guidelines-doing-science-influences-policy-and-management 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 https://www.ifpri.org/blog/leveraging-local-knowledge-map-and-link-agricultural-plots-farmer-practices

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: https://www.nature.com/articles/s41558-020-0738-8.epdf?author_access_token=poj3Fn4fkhP7_SK-yFKaTNRgN0jAjWel9jnR3ZoTv0OGVcM5jAVKvW5GyId6F2q0ve6uY5HlQ2nGzEyTtPTSUIuTOykc5x3bM9HdnsqyTZdAL_YY02dyngC4HUYA6LeqaLA-r26jCXCx1eABw5d_FQ%3D%3D

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. https://doi.org/10.1111/csp2.210

Goldstein, A., Turner, W. R., Spawn, S. A., Anderson-Teixeira, K. J., Cook-Patton, S., Fargione, J., … Hole, D. G. (2020). Protecting irrecoverable carbon in Earth’s ecosystems. Nature Climate Change, 10(4), 287–295. https://doi.org/10.1038/s41558-020-0738-8

Masuda, Y. J., Fisher, J. R. B., Zhang, W., Castilla, C., Boucher, T. M., & Blundo-Canto, G. (2020). A respondent-driven method for mapping small agricultural plots using tablets and high resolution imagery. Journal of International Development. https://doi.org/10.1002/jid.3475



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 http://sciencejon.blogspot.com/

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): https://www.caryinstitute.org/news-insights/media-coverage/more-we-lose-biodiversity-worse-will-be-spread-infectious-diseases

If you know someone who wants to sign up to receive these summaries, they can do so at http://bit.ly/sciencejon

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. https://doi.org/10.1126/science.1194694

Bloomfield, L. S. P., McIntosh, T. L., & Lambin, E. F. (2020). Habitat fragmentation, livelihood behaviors, and contact between people and nonhuman primates in Africa. Landscape Ecology, 35(4), 985–1000. https://doi.org/10.1007/s10980-020-00995-w

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. https://doi.org/10.1111/ele.12904

Johnson, C. K., Hitchens, P. L., Pandit, P. S., Rushmore, J., Evans, T. S., Young, C. C. W., & Doyle, M. M. (2020). Global shifts in mammalian population trends reveal key predictors of virus spillover risk. Proceedings of the Royal Society B: Biological Sciences, 287(1924), 20192736. https://doi.org/10.1098/rspb.2019.2736

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. https://doi.org/10.1289/ehp.0901389

Morse, S. S., Mazet, J. A. K., Woolhouse, M., Parrish, C. R., Carroll, D., Karesh, W. B., … Daszak, P. (2012). Prediction and prevention of the next pandemic zoonosis. The Lancet, 380(9857), 1956–1965. https://doi.org/10.1016/S0140-6736(12)61684-5

Ostfeld, R. S., & Keesing, F. (2012). Effects of Host Diversity on Infectious Disease. Annual Review of Ecology, Evolution, and Systematics, 43(1), 157–182. https://doi.org/10.1146/annurev-ecolsys-102710-145022

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

Tessum, C. W., Apte, J. S., Goodkind, A. L., Muller, N. Z., Mullins, K. A., Paolella, D. A., … Hill, J. D. (2019). Inequity in consumption of goods and services adds to racial–ethnic disparities in air pollution exposure. Proceedings of the National Academy of Sciences, 116(13), 6001–6006. https://doi.org/10.1073/pnas.1818859116

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

Wu, X., Nethery, R. C., Sabath, B. M., Braun, D., & Dominici, F. (2020). Exposure to air pollution and COVID-19 mortality in the United States. MedRxiv, 2020.04.05.20054502. https://doi.org/10.1101/2020.04.05.20054502

Zhang, Q., Zheng, Y., Tong, D., Shao, M., Wang, S., Zhang, Y., … Hao, J. (2019). Drivers of improved PM 2.5 air quality in China from 2013 to 2017. Proceedings of the National Academy of Sciences, 116(49), 24463–24469. https://doi.org/10.1073/pnas.1907956116



Wednesday, April 1, 2020

April 2020 Science Journal Article Summary

Cherry tree in bloom

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

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

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

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

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

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

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

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

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

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

Armsworth, P. R., Benefield, A. E., Dilkina, B., Fovargue, R., Jackson, H. B., Le Bouille, D., & Nolte, C. (2020). Allocating resources for land protection using continuous optimization: biodiversity conservation in the United States. Ecological Applications, eap.2118. https://doi.org/10.1002/eap.2118

Hammerschlag, N., Schmitz, O. J., Flecker, A. S., Lafferty, K. D., Sih, A., Atwood, T. B., … Cooke, S. J. (2019). Ecosystem Function and Services of Aquatic Predators in the Anthropocene. Trends in Ecology & Evolution, 34(4), 369–383. https://doi.org/10.1016/j.tree.2019.01.005

Hannah, L., Roehrdanz, P. R., Marquet, P. A., Enquist, B. J., Midgley, G., Foden, W., … Svenning, J. (2020). 30% Land Conservation and Climate Action Reduces Tropical Extinction Risk By More Than 50%. Ecography, 1–11. https://doi.org/10.1111/ecog.05166

Neugarten, R. A., Moull, K., Martinez, N. A., Andriamaro, L., Bernard, C., Bonham, C., … Turner, W. (2020). Trends in protected area representation of biodiversity and ecosystem services in five tropical countries. Ecosystem Services, 42(January), 101078. https://doi.org/10.1016/j.ecoser.2020.101078

Richter, B. D., Bartak, D., Caldwell, P., Davis, K. F., Debaere, P., Hoekstra, A. Y., … Troy, T. J. (2020). Water scarcity and fish imperilment driven by beef production. Nature Sustainability. https://doi.org/10.1038/s41893-020-0483-z

Wilhere, G. F. (2008). The how-much-is-enough myth. Conservation Biology, 22(3), 514–517. https://doi.org/10.1111/j.1523-1739.2008.00926.x

Wilhere, G. F., Maguire, L. A., Scott, J. M., Rachlow, J. L., Goble, D. D., & Svancara, L. K. (2012). Conflation of Values and Science: Response to Noss et al. Conservation Biology, 26(5), 943–944. https://doi.org/10.1111/j.1523-1739.2012.01900.x

Stay safe, vigilant, and healthy,


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

Monday, March 2, 2020

March 2020 Science Journal Article Summary

Frozen waterfall


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

If you know someone who wants to sign up to receive these summaries, they can do so at http://bit.ly/sciencejon

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

Global estimates of % protection hide the fact that protection varies widely for different  cosystems and habitat types. Sayre et al. 2020 splits that up into 278 natural ecosystems (based on temperature, moisture, elevation, land cover, etc). If you limit protection to IUCN 1-4 (stricter protection), 9 of those 278 were totally unprotected and 206 were below 8.5% protected (half way to Aichi targets). If you use IUCN 1-6 (including  areas allowing more human use) only 1/3 of ecosystems are below 8.5%. Table 5 shows how much of each major land cover group (forests, grasslands, etc.) has been lost, Table 4 has the details for the 278 ecosystems. Some figures are easier to see online: https://www.sciencedirect.com/science/article/pii/S2351989419307231?via%3Dihub

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

Lehmann, J., & Rillig, M. (2014). Distinguishing variability from uncertainty. Nature Climate Change, 4(3), 153. https://doi.org/10.1038/nclimate2133

Sanderson, J. S., Beutler, C., Brown, J. R., Burke, I., Chapman, T., Conant, R. T., … Sullivan, T. (2020). Cattle, conservation, and carbon in the western Great Plains. Journal of Soil and Water Conservation, 75(1), 5A-12A. https://doi.org/10.2489/jswc.75.1.5A

Sayre, R., Karagulle, D., Frye, C., Boucher, T., Wolff, N. H., Breyer, S., … Possingham, H. (2020). An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems. Global Ecology and Conservation, 21(December), e00860. https://doi.org/10.1016/j.gecco.2019.e00860



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

Monday, February 3, 2020

February 2020 science journal article summary

Ice crystals on windshield


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

Also, a 12-minute video came out recently about the 2017 March for Science, and I show up in it a few times. It's called "SciComm: Raising Our Voice for Science and Public Policy," it's directed & produced by Larry Kirkman (larry@american.edu) and Shannon Shikles at the Center for Environmental Filmmaking at American University, and you can watch it here: https://vimeo.com/383209723

Sippel et al. 2020 bucks the pattern where climatologists emphasize how distinct climate and weather are. Past research has shown the impact of climate change on certain weather events, but this paper actually detects the impact of climate change on any given day since late March 2012! By summarizing weather data all over the world, they found that we’ve been clearly outside of natural variability for the past 8 years on a daily basis. On a monthly basis, climate change has been detectable from global weather data since 2001. You can read a newspaper article about the paper at https://www.washingtonpost.com/weather/2020/01/02/signal-human-caused-climate-change-has-emerged-every-day-weather-study-finds/  I love the description of this paper from meteorologist Maria LaRosa: “[it’s] like looking closely at an impressionist painting –  you can't say what the picture is until you step back and look at the whole”

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

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

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

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

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

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

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

Brancalion, P. H. S., Niamir, A., Broadbent, E., Crouzeilles, R., Barros, F. S. M., Almeyda Zambrano, A. M., … Chazdon, R. L. (2019). Global restoration opportunities in tropical rainforest landscapes. Science Advances, 5(7), eaav3223. https://doi.org/10.1126/sciadv.aav3223

Chaplin-Kramer, R., Sharp, R. P., Weil, C., Bennett, E. M., Pascual, U., Arkema, K. K., … Daily, G. C. (2019). Global modeling of nature’s contributions to people. Science, 366(6462), 255–258. https://doi.org/10.1126/SCIENCE.AAW3372

Fahrig, L. (2017). Ecological Responses to Habitat Fragmentation Per Se. Annual Review of Ecology, Evolution, and Systematics, 48(1), annurev-ecolsys-110316-022612. https://doi.org/10.1146/annurev-ecolsys-110316-022612

Fahrig, L., Arroyo-Rodríguez, V., Bennett, J. R., Boucher-Lalonde, V., Cazetta, E., Currie, D. J., … Watling, J. I. (2019). Is habitat fragmentation bad for biodiversity? Biological Conservation, 230(October 2018), 179–186. https://doi.org/10.1016/j.biocon.2018.12.026

Hamel, P., Bremer, L. L., Ponette-González, A. G., Acosta, E., Fisher, J. R. B., Steele, B., … Brauman, K. A. (2020). The value of hydrologic information for watershed management programs: The case of Camboriú, Brazil. Science of The Total Environment, 135871. https://doi.org/10.1016/j.scitotenv.2019.135871

Johnson, K. A., Wing, O. E. J., Bates, P. D., Fargione, J., Kroeger, T., Larson, W. D., … Smith, A. M. (2019). A benefit–cost analysis of floodplain land acquisition for US flood damage reduction. Nature Sustainability. https://doi.org/10.1038/s41893-019-0437-5

Jones, P. F., Jakes, A. F., Telander, A. C., Sawyer, H., Martin, B. H., & Hebblewhite, M. (2019). Fences reduce habitat for a partially migratory ungulate in the Northern Sagebrush Steppe. Ecosphere, 10(7). https://doi.org/10.1002/ecs2.2782

Samanta, A., Eanes, F. R., Wickerham, B., Fales, M., Bulla, B. R., & Prokopy, L. S. (2019). Communication, Partnerships, and the Role of Social Science: Conservation Delivery in a Brave New World. Society & Natural Resources, 0(0), 1–13. https://doi.org/10.1080/08941920.2019.1695990

Sippel, S., Meinshausen, N., Fischer, E. M., Székely, E., & Knutti, R. (2020). Climate change now detectable from any single day of weather at global scale. Nature Climate Change, 10(1), 35–41. https://doi.org/10.1038/s41558-019-0666-7



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 http://sciencejon.blogspot.com/

Monday, January 6, 2020

Can science boost credibility even if it isn't used?

Hamel et al. 2020 ("The value of hydrologic information for watershed management programs: The case of Camboriú, Brazil") looked at how scientific information was perceived and used in decision making for a water fund in Brazil (where a water treatment company pays for upstream conservation to reduce the costs of treating the water).

Through interviews, we determined that the hydrological modeling and monitoring data that we provided was NOT used in designing and implementing the water fund. But counter-intuitively, having done the analysis using complex models and high-resolution data was seen as important for the water fund to be seen as scientifically credible.

So ironically, even though the credible models were not actually used, their existence helped build support for the overall water fund. Despite this, as long as monitoring data was used to calibrate and validate the model, a simpler model (InVEST, as opposed to SWAT) and coarser data resolution (30m, as opposed to 1m) would have met the information needs of the users. We should have had more frank discussions up front with the ultimate users of the information to produce a model seen as credible and actually used, while avoiding over-investment in model complexity that wasn't needed.

You can read the full article here: https://www.sciencedirect.com/science/article/pii/S0048969719358668

Hamel, P., Bremer, L. L., Ponette-González, A. G., Acosta, E., Fisher, J. R. B., Steele, B., … Brauman, K. A. (2020). The value of hydrologic information for watershed management programs: The case of Camboriú, Brazil. Science of The Total Environment, 135871. https://doi.org/10.1016/j.scitotenv.2019.135871