This month I have two articles on climate resilience, one on climate mitigation, and one on science-implementation partnerships. Plus a couple articles on AI as usual.
While it's a Canadian science fair project and not peer-reviewed science, I was interested to see this test of "you catch more flies with honey than vinegar." The experiment found that you catch more flies with honey and vinegar than with vinegar alone, which catches more than honey alone. But bringing it back to the saying: please don't be a jerk regardless.
My obligatory article on AI (and specifically Large Language Models [LLMs] like ChatGPT) is extra-fascinating this month. Can (and should) LLMs help us communicate more empathically? Check out this NY times article 'When Doctors Use a Chatbot to Improve Their Bedside Manner.' I love the idea of someone wanting to be kind by using the right words, not knowing what to say, and getting help with that. I have found it's very common for people to stay silent when they can't find 'the right words' around illness and death and grief, so I am all for helping people (including doctors) to get unstuck. I asked Bard (Google's LLM) for advice on how to support a friend who is grieving and found it mostly excellent. Not ideal but really good and something most of us could learn from. The idea of machines helping us to express empathy more effectively is so intriguing and I'm all for it.
The latest AI tool I tried out is "ChatPDF" which lets you upload a PDF and ask the tool questions about it. It works pretty well for some things, but is oddly dull in others. Like for one paper I asked it which species was responsible for most of the primary effect they reported (t CO2e of climate mitigation) and it didn't know. But when I asked it what the contribution was of the species I knew drove >80% of the effect, it reported it numerically. Apparently it was unable to divide the number it knew for the species in question by the total effect size number (which it also knew). But I thought it performed reasonably well with my questions about the IPCC report. TL;DR is that it seems better at finding / extracting info than any kind of reasoning.
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).
Duncanson et al. 2023 estimates how much global forested protected areas may be reducing climate change. They matched forested protected areas to similar forested unprotected areas using data from 2000 (land cover, ecoregion, and biome; with additional control pixels that accounted for population etc. - see Table S1). Then they used the new (2019) GEDI lidar data to estimate aboveground forest biomass in 2020. 63% of forested PAs had significantly higher biomass than matched unprotected areas; on average PAs have 28% more aboveground biomass. Over a third of that effect globally comes from Brazil; Africa had less C dense forests and more human pressures on both PAs and unprotected areas. As you'd guess, most of the difference in unprotected sites was due to deforestation. But in 18% of PAs carbon was higher than unprotected sites even though optical sensors didn't detect deforestation (implying LiDAR is detecting either avoided degradation and/or enhanced growth in PAs). As a final note, other research has shown that both ICESat-2 and GEDI LiDAR satellites tend to underestimate forest canopy heights (mostly irrelevant here given the matching approach, but good to know for other global estimates).
Anderson et al. 2023 is the latest paper supporting the data in The Nature Conservancy's Resilient Land Mapping Tool (https://maps.tnc.org/resilientland/). They looked for overlap in three layers across the US: biodiversity value (the union of TNC's ecoregional priority areas), resilient sites (places with diverse and connected microclimates), and 'climate flow' (a circuit theory analysis of where wildlife is likely to shift in response to climate change). See Fig 1 for their main results, or the web map is better since you can separate out the three main layers. The way 'biodiversity value' was assessed varies a lot by ecoregion, and some are more ambitious than others (e.g., the biggest biodiversity patch is in the Nebraska Sandhills, but other ecoregions also have some big blocks of intact habitat). So not every green blob is equally high-priority, but collectively it does have representation across all ecoregions which is good. On the main map, both blue and dark green blobs offer the most value for climate resilience, but again the web map lets you see the continuous data.
Rubenstein et al. 2023 is a systematic review of how documented range shifts (by plants and animals, presumably in response to climate change) compare to predictions. Across 311 papers, only 47% of shifts due to temperature were in expected directions (higher latitudes & elevations, and marine movement to deeper depths was seen but was non-significant). See Fig 4 for how results varied by taxonomic group, ecoystem type, and type of shift. Not many studies looked at precip but of those that did only 14% found species moving to stay in a precip niche. Note: this means simple assumptions of how species will move are of limited value, but NOT that local or regional predictions are inherently flawed. The authors note that considering local predictions of changing temp and precip will often depart from these simple assumptions, and other factors like water availability, fire, etc. are likely to be relevant. A final note on the last page was helpful: not all range shifts have equal relevance to management. In some cases a few individuals are moving to new places but most of the wildlife population doesn't shift at all. Both shifts AND non-shifts have implications for how management should change to keep species and ecosystems healthy! This paper has a LOT of nuance and variation in this paper, and a very detailed methods section with good recommendations for how scientists should continue these investigations.
Carter et al. 2020 is a call for better coordination of science across landscapes in the Western US to better inform land management. They walk through 5 examples of how it has worked (standard monitoring for national parks, tools to help restore arid & semi-arid landscapes, predictive soil maps of where reclaiming disturbed land could work, frameworks for sage-grouse monitoring, and targeted interventions to improve big-game connectivity). They ask agencies to better support boundary-spanning partnerships w/ scientists, and to make more specific asks to scientists about what information they need. I'm not convinced that's likely; White et al. 2019 and others have found land managers don't always see science as a key input, they're often too busy to even know where they need help, and a high-engagement partnership may not always be the best pitch to agency staff who are stretched thin.
Anderson, M. G., Clark, M., Olivero, A. P., Barnett, A. R., Hall, K. R., Cornett, M. W., Ahlering, M., Schindel, M., Unnasch, B., Schloss, C., & Cameron, D. R. (2023). A resilient and connected network of sites to sustain biodiversity under a changing climate. Proceedings of the National Academy of Sciences, 120(7), 1–9. https://doi.org/10.1073/pnas.2204434119
Carter, S. K., Pilliod, D. S., Haby, T., Prentice, K. L., Aldridge, C. L., Anderson, P. J., Bowen, Z. H., Bradford, J. B., Cushman, S. A., DeVivo, J. C., Duniway, M. C., Hathaway, R. S., Nelson, L., Schultz, C. A., Schuster, R. M., Trammell, E. J., & Weltzin, J. F. (2020). Bridging the research-management gap: landscape science in practice on public lands in the western United States. Landscape Ecology, 35(3), 545–560. https://doi.org/10.1007/s10980-020-00970-5
Duncanson, L., Liang, M., Leitold, V., Armston, J., Krishna Moorthy, S. M., Dubayah, R., Costedoat, S., Enquist, B. J., Fatoyinbo, L., Goetz, S. J., Gonzalez-Roglich, M., Merow, C., Roehrdanz, P. R., Tabor, K., & Zvoleff, A. (2023). The effectiveness of global protected areas for climate change mitigation. Nature Communications, 14(1), 2908. https://doi.org/10.1038/s41467-023-38073-9
Rubenstein, M. A., Weiskopf, S. R., Bertrand, R., Carter, S. L., Comte, L., Eaton, M. J., Johnson, C. G., Lenoir, J., Lynch, A. J., Miller, B. W., Morelli, T. L., Rodriguez, M. A., Terando, A., & Thompson, L. M. (2023). Climate change and the global redistribution of biodiversity: substantial variation in empirical support for expected range shifts. Environmental Evidence, 12(1), 7. https://doi.org/10.1186/s13750-023-00296-0
p.s. The photo is of food we made for a little mermaid party, including crab cupcakes (no crab in them, they were vegan), mermaid tail ice cream cones, sugar cookies, and veggie sushi