Thursday, January 25, 2024

Best of 2023 science summaries

Luna resting her head on Kong (on Jon's lap)

Happy new year!

As usual here are my favorite 15 articles from 2023, and a few other things you may have missed.

But first - if you or someone you know are looking for a sweet and loving dog in your life (or two?), both of our foster dogs Luna (top head above) and Kong (supporting head) are still up for adoption! You can read about Kongsee how cute he isread about Luna, and see how cute she is.

  1. A summary of the IPCC's latest report (AR6) came out, click here for my brief summary of that summary
  2. The earth had its first day that was 2C warmer than the historic pre-industrial average (1850-1900). We're still a ways from an AVERAGE of 2C warmer than pre-industrial, but still a bummer.
  3. Last month I shared some (not very well-informed) thoughts about how I'm using artificial intelligence (AI), specifically large language models (LLMs) like ChatGPT and Google's Bard. Scroll to the bottom of my December summary  to see them

On to the science articles!

Chaplin-Kramer et al. 2022 is a great global summary of 14 ecosystem services I've been waiting to see for years! Their big finding is that 90% of nature's local contributions to people within each country come from a total of 30% of global land area and 24% of coastal waters (EEZs). Globally only 15% of those places are protected. The land and water needed is uneven by country (e.g., the US needs 37% of land and 15% of coastal waters), and protection varies too. See Fig 1 for the areas that provide the most benefit per unit area. The land required would be lower if optimizing globally, but at the cost of less equitable benefit distribution. The 2 global ones (carbon storage and moisture recycling) need 44% of land (optimized globally) to stay at 90% of current levels, mostly overlapping with the 30% (see Fig 3). Roughly 87% of the world population benefits from at least one of the ecosystem services, but benefits are not distributed equally (see Fig 2). The local services include: water quality (regulating nitrogen and sediment), crop pollination, livestock fodder, production of timber and fuelwood, flood regulation, fish harvest (from rivers and oceans), recreation (on land and oceans), and coastal risk reduction. If interested you can get combined GIS data from or one of the authors (Rachel Neugarten) is happy to send individual maps and data.

Cinner et al. 2019 is a 16 year study of rotational fishing / closure in Papua. They found success in compliance with the system (due to strong social cohesion driven by leaders sharing info, a "carrot and stick" approach, and lots of community participation) BUT even though closed areas rebounded, over the study period fish biomass dropped by about half. So even though the closure program worked as intended, it wasn't enough to offset overfishing when areas were open.

Grenz & Armstrong 2023 is an article criticizing "pop-up restoration," a term they coin for ecological restoration that 1) lacks long-term engagement and monitoring, 2) denies people use of lands (even Indigenous people who have been there for millennia), and 3) sets fixed ecological baselines or goals even for ecosystems which historically were highly managed and dynamic. They describe two use cases where  management outcomes preferred by Indigenous people were ignored, instead managing for outcomes preferred by non-Indigenous ranchers or residents. They call for restorative justice being the norm, and ethical engagement with communities in each specific place (rather than coopting and misusing Indigenous knowledge). They also call for more openness to evolving needs and conditions of both people and ecosystems, and acknowledging failures and wrongdoing.

James et al. 2023 asked over 900 science & conservation staff of The Nature Conservancy about their careers and influence, and how they perceived their gender as impacting that. We found that women had less influence, experienced many barriers to their careers (including harassment, discrimination, and fear of retaliation for speaking out), and that men overestimated gender equity. Only have 5 minutes? Skip to the recommendations on page 7 (we ask orgs to: show public leadership on equity, improve transparency and accountability, diversify teams and improve career pathways for women, be flexible, include training and mentoring as part of broader change, help women connect, address sexual discrimination and harassment, and consider intersectionality). If you have 15 minutes more, read the quotes in Table 2 (p5-8) because they're really compelling and illustrative. Or if you're with the half of men and 3/4 of women in our sample who think we have more to do on gender equity (rather than that we've already "gone overboard" or that it's not an issue as some men reported), just read the whole damn paper because there's a lot of interesting detail and nuance in the results. I learned a ton while helping out on it, and I'm excited to start advocating for the recommendations. You can read it at: or a short blog at 

Toomey et al. 2023 is a nice reminder that just sharing information doesn't usually change minds. They challenge the idea that facts & scientific literacy lead to research being applied, and that broad communications targeting as many individuals as possible are the most effective way to share those facts. Instead they recommend appealing to values and emotions, and strategically targeting audiences by considering social networks (drawing on science about behavior change) and social norms. I love the conclusion that "this article may not change your mind" but that they hope it will inspire reflection. I also like the use of the backronym WEIRD (Western, Educated, Industrialized, Rich, Democratic) to describe countries like the US.

Dickson et al. 2023 piqued my curiosity by breaking down different causes of conservation failure and how to respond. I generally dislike taxonomy papers, and find them academic and hard to apply. But understanding how to respond to different kinds of failure seems helpful, especially for the most common causes (including lacking a sufficiently robust theory of change. see table 2 for more). Their taxonomy has 59 (!) root causes, grouped into 6 categories: 1) planning, design, or knowledge (e.g., inadequate theory of change); 2) team dynamics (e.g., disagreements on what priorities should be); 3) project governance (e.g., lack of a technical advisory group); 4) resources (e.g., staff overloaded or lack needed technical expertise); 5) stakeholder relationships (e.g., lack of buy-in from gov't); and 6) unexpected external events (e.g., natural disaster, war, disease, etc.). After reading all the ways to fail, my main take away is that failure will happen sometimes and we need to focus on how to learn and pivot. The other big one is that while teams often resent spending a few hours developing and refining a theory of change (ToC), that is likely time well spent given that how often an insufficient ToC was listed as a cause of failure.

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).

Knauer et al. 2023 has good news - better modeling estimates forests could sequester more carbon than we thought. But it's likely very small good news. Their best case is 20% more "gross primary productivity" (GPP, energy captured by photosynthesis), BUT a) that's using an extremely unlikely 'worst case' cliamte scenario which is actually hard to achieve, (RCP8.5) and b) only a fraction of GPP ends up sequestered as carbon (see Cabon et al. 2022 for more). Since forests offset roughly 25% of annual human emissions, the results likely mean <1% of annual emissions could be offset. I'll take it, but we still need to reduce gross emissions as fast as possible.

Rubenstein et al. 2023
 is a systematic review of how documented range shifts (when plants and animals change where they live, 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

Dethier et al 2023 (briefly summarized in Walmsley 2023) finds that mining in tropical countries is dramatically increasing sediment in rivers. 80% of the 173 rivers affected by mining that they studied had sediment concentrations more than double what they were prior to mining. This is a pretty coarse estimate using satellite data, so the actual sediment estimates are very rough, but the general pattern should be valid.

NatureServe's 2023 Biodiversity in Focus US report is a high level look at threatened species (imperiled or vulnerable) in the US. It's short and worth reading the whole thing. They find 34% of plant species and 40% of animal species are threatened, and 41% of the ~400 ecosystem groups in the US are at risk of "range-wide collapse" (meaning being replaced or substantially transformed). Figure 1 and 2 have breakdowns of averages for plants and animals by subgroups. For plants cacti are the worst off at 48% threatened and sedges are the least threatened at 14%. Freshwater snails are the most threatened animals (75%, and other FW groups are all more threatened than average) while birds are the least threatened (12%) and bees are about average (37%). Note that % of species that are threatened is different than looking at % of individual organisms or biomass that is threatened (all are useful metrics, Audubon's State of the Birds report looks at trends in bird population size). Figure 3 shows the most and least threatened ecosystems; unsurprisingly virtually all tropical ecosystems are threatened (they had relatively small extents originally, and are valuable for agriculture), while cliffs / rock and alpine and tundra ecosystems fare the best due to less threat of conversion to other land uses and higher rates of protection (Figure 5). They don't provide details but I would guess these are relatively short-term predictions, as climate change will threaten a lot of alpine and tundra ecosystems in the long term. Figure 4 shows how protected different species groups and ecosystems are. Almost 30% of vascular plant species are protected >50% of their range, but only 15% of vertebrate species are that protected. Finally, Figure 9b shows which states have the highest % of their area in at-risk ecosyetsms (NE, MT, and SD score the highest due to large at-risk grasslands), and Figure 11 shows priority areas for conserving imperiled species. With some exceptions (like FL) Figures 9 and 11 highlight different priority areas; Fig 11 focuses on relatively small and irreplaceable places that the most threatened species rely on, while Fig 9 focuses on more intact and lower diversity ecosystems that are at risk of being transformed (but with less potential for species extinctions). The authors conclude that the Restoring America's Wildlife Act (RAWA) guided by State Wildlife Action Plans (SWAPs) is our best bet to catalyze massive investment in conservation of the places that need it most.

Jewell et al. 2023 surveyed directors and board members in charge of state wildlife agencies in the SE U.S. about future conservation challenges and how they plan to respond. They found that the respondents were focused on funding and 'agency relevance' (including changing values and fewer hunters) but less concerned about climate change (see Table 2). One quote stuck out at me, which was that they saw climate change impacts as important at time-scales beyond decades, and thus not urgent to act on (they also saw it as too political). By comparison, they saw education and outreach as critical to recruit hunters and tell the public the value of hunting and fishing. Agency directors average 5 years in office, so short-term things they can do may be more appealing. The authors call for engaging decision makers around the science of how climate change is already affecting wildlife, how that is expected to shift over time, and what actions or preparations can be taken now to help.

I couldn't resist reading Clark et al. 2023 right away despite my sad backlog. I once had a native plant garden guy tell me "at best non-native plants offer no value to pollinators and other wildlife, and most are harmful." Obviously false as an absolute! But how do they compare? Clark looked at 10 species in a Connecticut forest and found some invasive species (like honeysuckle) had more bugs (mass and protein) than the average for natives, but others (Japanese barberry) had fewer bugs. But birds seemed to forage both equally. It's a tiny study and I wish they hadn't pooled all native species, but I do like a study that counters "it depends!" to a truism in conservation.

Prichard et al. 2021
 is a review of several questions related to fire in US western forests (see Table 1 for the summary of questions & answers). They include whether and when/ how to use cutting trees and prescribed burns as tools for reducing wildfire risk and/or climate mitigation and/or ecological restoration. The authors argue that many dry forests (and some moist forests mixed into dry forest landscapes) historically experienced more frequent fires of low to moderate intensity (often set by Native Americans), but that these forests are now denser and more likely to have severe crown fires (especially as summers become warmer and drier). That in turn will cause some forests to be lost and shift to grasslands or other ecosystems. Read Table 1 for key takeaways, including that for many (not all) Western forests, thinning and prescribed burning are important tools. Side note: given the active debate on this topic, I asked for input from a few forest scientists deep in the lit, and they recommended this article.

Breznau et al. 2022
 has some scary news about science - not only is it less reproducible than we think, we can't even figure out why results vary so much. To be fair, they note that natural sciences and/or experimental research should have less variation than social science based on existing surveys (what this study looked at). But it's still concerning! Or preliminarily concerning but waiting for many more replicas, to take their message to heart. The models the 73 teams built were: 17% positive (more immigration increases support for social policies), 25% negative (more immigration reduces support for social policies), and 58% did not find a clear effect (the confidence interval included zero, although they may have had a positive or negative average effect). 61% of researchers concluded that immigration does not reduce support for social policies, 26% concluded it DOES reduce support (the text says 28.5% but it's a typo, reinforcing the core message of the paper), and 13% concluded it couldn't be tested w/ the given data. And Fig 2 shows that not only are results and conclusions all over the place, the variation isn't explained by the variables they tracked like expertise or prior beliefs. That means researcher bias is only part of the problem. I have some questions about the metanalysis itself that make me suspect they could have explained more of the variance with different methods (ironically, that is consistent with their core findings about how small method changes can drive results). But the paper reveals two problems: 1) scientists can produce different quantitative results from the same data and hypotheses, and 2) scientists' conclusions are often not well tied to their results (this paper found only ~1/3 of variation in conclusions came from how consistent the set of models each team used were). I see a lot of #2 when I peer review papers. Let's all remain humble and skeptical, and look for more replication in 2023!

Breznau, N., Rinke, E. M., Wuttke, A., Nguyen, H. H. V, Adem, M., Adriaans, J., Alvarez-Benjumea, A., Andersen, H. K., Auer, D., Azevedo, F., Bahnsen, O., Balzer, D., Bauer, G., Bauer, P. C., Baumann, M., Baute, S., Benoit, V., Bernauer, J., Berning, C., … Żółtak, T. (2022). Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. Proceedings of the National Academy of Sciences, 119(44), 1–8.

Chaplin-Kramer, R., Neugarten, R. A., Sharp, R. P., Collins, P. M., Polasky, S., Hole, D., Schuster, R., Strimas-Mackey, M., Mulligan, M., Brandon, C., Diaz, S., Fluet-Chouinard, E., Gorenflo, L. J., Johnson, J. A., Kennedy, C. M., Keys, P. W., Longley-Wood, K., McIntyre, P. B., Noon, M., … Watson, R. A. (2022). Mapping the planet’s critical natural assets. Nature Ecology & Evolution, 7(1), 51–61.

Cinner, J. E., Lau, J. D., Bauman, A. G., Feary, D. A., Januchowski-Hartley, F. A., Rojas, C. A., Barnes, M. L., Bergseth, B. J., Shum, E., Lahari, R., Ben, J., & Graham, N. A. J. (2019). Sixteen years of social and ecological dynamics reveal challenges and opportunities for adaptive management in sustaining the commons. Proceedings of the National Academy of Sciences, 116(52), 26474–26483.

Clark, R. E. (2023). Are native plants always better for wildlife than invasives? Insights from a community-level bird- exclusion experiment. Preprint available at

Dethier, E. N., Silman, M., Leiva, J. D., Alqahtani, S., Fernandez, L. E., Pauca, P., Çamalan, S., Tomhave, P., Magilligan, F. J., Renshaw, C. E., & Lutz, D. A. (2023). A global rise in alluvial mining increases sediment load in tropical rivers. Nature, 620(7975), 787–793.

Dickson, I., Butchart, S. H. M., Catalano, A., Gibbons, D., Jones, J. P. G., Lee‐Brooks, K., Oldfield, T., Noble, D., Paterson, S., Roy, S., Semelin, J., Tinsley‐Marshall, P., Trevelyan, R., Wauchope, H., Wicander, S., & Sutherland, W. J. (2023). Introducing a common taxonomy to support learning from failure in conservation. Conservation Biology, 37(1), 1–15.

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.

Grenz, J., & Armstrong, C. G. (2023). Pop-up restoration in colonial contexts: applying an indigenous food systems lens to ecological restoration. Frontiers in Sustainable Food Systems, 7(September), 1–12.

James, R., Fisher, J. R. B., Carlos-Grotjahn, C., Boylan, M. S., Dembereldash, B., Demissie, M. Z., Diaz De Villegas, C., Gibbs, B., Konia, R., Lyons, K., Possingham, H., Robinson, C. J., Tang, T., & Butt, N. (2023). Gender bias and inequity holds women back in their conservation careers. Frontiers in Environmental Science, 10(January), 1–16. or

Jewell, K., Peterson, M. N., Martin, M., Stevenson, K. T., Terando, A., & Teseneer, R. (2023). Conservation decision makers worry about relevancy and funding but not climate change. Wildlife Society Bulletin, November 2022, 1–14.

Knauer, J., Cuntz, M., Smith, B., Canadell, J. G., Medlyn, B. E., Bennett, A. C., Caldararu, S., & Haverd, V. (2023). Higher global gross primary productivity under future climate with more advanced representations of photosynthesis. Science Advances, 9(46), 24–28.

NatureServe. (2023). Biodiversity in Focus: United States Edition.

Prichard, S. J., Hessburg, P. F., Hagmann, R. K., Povak, N. A., Dobrowski, S. Z., Hurteau, M. D., Kane, V. R., Keane, R. E., Kobziar, L. N., Kolden, C. A., North, M., Parks, S. A., Safford, H. D., Stevens, J. T., Yocom, L. L., Churchill, D. J., Gray, R. W., Huffman, D. W., Lake, F. K., & Khatri‐Chhetri, P. (2021). Adapting western North American forests to climate change and wildfires: 10 common questions. Ecological Applications, 31(8).

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.

Toomey, A. H. (2023). Why facts don’t change minds: Insights from cognitive science for the improved communication of conservation research. Biological Conservation, 278(December 2022), 109886.

Walmsley, B. (2023). Satellite images show the widespread impact of mining on tropical rivers. Nature, 620(7975), 729–730.


December 2023 science summary

Anglerfish pumpkin / jack-o'-lantern


On Friday Nov 17, the earth was 2C warmer than historic pre-industrial average (1850-1900), and 1.17C over the 1991-2020 average. This does not mean the earth has warmed 2C yet! That would require a sustained set of temperatures high above average. But still not great news.

This month I have three science articles but am also sharing some informal thoughts about how scientists might want to consider using (and not using) generative artificial intelligence tools.

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

Knauer et al. 2023 has good news - better modeling estimates forests could sequester more carbon than we thought. But it's likely very small good news. Their best case is 20% more "gross primary productivity" (GPP, energy captured by photosynthesis), BUT a) that's using an extremely unlikely 'worst case' cliamte scenario which is actually hard to achieve, (RCP8.5) and b) only a fraction of GPP ends up sequestered as carbon (see Cabon et al. 2022 for more). Since forests offset roughly 25% of annual human emissions, the results likely mean <1% of annual emissions could be offset. I'll take it, but we still need to reduce gross emissions as fast as possible.

Chiaravalloti et al. 2023 assessed how well cattle ranches in the Brazilian Pantanal (among the world's largest wetlands) aligned w/ Elinor Ostrom's principles (for sustainable use of natural resources, see Table 2 for a nice summary). They interviewed 49 cattle ranchers, other people working in the beef supply chain, conservationists, and policy makers. Flooding, very low stocking density, lack of transportation, and the fire regime all make ranching in the Pantanal unusual. They found the Pantanal ranches do well on the first 3 principles: clearly defined boundaries, appropriate rules for resource use reflecting local conditions, and collective decision making. But there is a lack of the other principles: limited monitoring, no graduated sanctions, lack of accessible conflict resolution, lack of recognition for self-governance on sustainability, and nested enterprises that coordinate governance and monitoring and the rest. They call for a series of specific recommendations to address these deficits, including celebrating early examples of things that are working well.

Gomes et al. 2023 is a case study in the Brazilian Pantanal assessing 14 cattle ranches (cow-calf operations) using the Fazenda Pantaneira Sustentável (FPS) tool. The tool assesses 1) financial performance (costs of management, inputs, labor, etc., along w/ gross income), 2) productive performance (which favors native grass forage availability and producing calves), and 3) environmental performance (landscape diversity conservation index, which favors diverse vegetation types that have been maintained on the ranch), and combined them into a composite score. Table 2 has the results and highlights how much variation there is across ranches. Table 3 has an easier to read narrative summary of how the ranches are performing. They recommend using the relative high performing ranches as baselines for what performance level the others should aspire to.

I am in no way an expert on AI. I am a person who has played with a variety of tools, and is sometimes asked for my opinion. The thoughts here are my own, and don't reflect the views of my employer or anyone who actually knows what they're taking about. They are general guidance skewed by what I've tried, and the tools evolve fast so could already be wrong. Two things to keep in mind throughout - 1) when you ask AI for answers they often confidently provide wrong answers, and 2) don't put sensitive / non-public information in there as some of these tools have the right to reuse or share what you put in. Watch out for those!

With that caveat, I wanted to share some suggestions for how to use generative AI (including large language models [LLMs] like ChatGPT and Bard, plus image generating tools like DALL-E). Other kinds of AI are not included. I split use cases into three categories:

GREEN: relatively safe uses/ DO:

  • Reword emails / blogs / reports, including for length or tone or clarity. LLMs perform very well at producing text which is clear and understandable to a general audience, with few to no grammatical errors. It's also surprisingly good at adjusting emotional tone, e.g., doctors are using ChatGPT to write more empathetic emails to patients. Again, be wary of putting in sensitive info. A final edit and review for factual accuracy is essential. 
  • Use for screening a set of science paper PDFs (it produces a spreadsheet with summaries, methods, etc. which a researcher can use to decide where to start reading). Use “detailed summary” as the shorter summary leaves important stuff out (the link does to a detailed review I wrote of Elicit). This used to be free but they charge you now.
  • Help finding other kinds of information hard to find with traditional search engines. For example, a search for strategic planning frameworks for nonprofits (roughly similar to the conservation standards) was mostly unproductive, but similar queries to Bard produced an excellent science paper with a comparison between 5 frameworks.
    • Note that this only means looking for sources you will actually read, NOT asking it to pull out facts. It's a great way to find references you may otherwise miss.
  • Help finding hotels that meet criteria you can’t easily filter on in other travel sites (like quiet, dining options, offering special rates, etc.). Again - read reviews and the hotel website to verify the info, in some cases I was offered hotels that did not meet my criteria, but in other cases it helped me.
YELLOW: offers value but also some risk/ CAUTIOUSLY DO:
  • Look for key facts buried in long reports / science papers (either w/ ChatPDF or online LLMs) - then verify those facts are real / correct / actually in the source (ChatPDF will often tell you the correct page number for a factual assertion if asked). It is typically faster than reading long documents or searching through them (e.g.,  I used for the suites of IPCC AR6 reports). The 'cautiously' part is that I can't stress enough that these tools often make things up and provide fictional sources!
  • Summarize science papers or other long reports in plain language. Again, check any key points for veracity. Here's a long review comparing how ChatGPT summaries of papers compare to my own, and another one evaluating Elicit's short and detailed summaries of papers I co-authored.
  • Write sample code you either don't know how to write or that would take a long time. It may perform poorly and/or be hard to debug, but may be ‘good enough’ in cases where time is limited. On the other hand, in some cases it could introduce security and/or performance risks. This is best when you know how to code (and understand code you read) and are looking for sample code to start with.
  • Use to identify potential issues / problems with science papers. While existing functions around critiques of papers and methodological limitations or conflicts of interest do not work very well, in some cases they do work and have potential if refined. I'll say it again: verify anything it tells you.
  • List common arguments for or against a given topic. This can provide helpful context but should not be treated as definitive
  • Produce an initial outline for something like a paper or a report – suggesting possible topics and how to organize them as a way to stimulate thought and get started. My teacher friends also said it can be great for suggesting things to include in a syllabus.
RED: use cases to be avoided/ DO NOT:
  • Ask for facts and trust the results w/o carefully checking references (LLMs regularly fabricate false info and provide fictional references [hallucitations] for it)
  • Assume content provided (code, images, text) can be used w/o copyright issues. Often it cannot, and using a bit of LLM-generated content can screw w the copyright of the bigger report it goes into.
  • Assume LLMs will include caveats or methodological limitations when reporting results from reports (they generally do not)
  • Put sensitive, nonpublic, or other confidential text, data, or code into LLMs
  • Assume you know how the tools work. They change so fast you probably don't. Treat it as a black box which sometimes spits out candy, but sometimes you get those Harry Potter jelly beans that taste like vomit or earwax.

Again, please use the above as ideas you can try out and verify for yourself. Don't trust my judgment on AIs any more than you would trust their assessment of their limitations.


Cabon, A., Kannenberg, S. A., Arain, A., Babst, F., Baldocchi, D., Belmecheri, S., Delpierre, N., Guerrieri, R., Maxwell, J. T., McKenzie, S., Meinzer, F. C., Moore, D. J. P., Pappas, C., Rocha, A. V., Szejner, P., Ueyama, M., Ulrich, D., Vincke, C., Voelker, S. L., … Anderegg, W. R. L. (2022). Cross-biome synthesis of source versus sink limits to tree growth. Science, 376(6594), 758–761.

Chiaravalloti, R. M., Tomas, W. M., Akre, T., Morato, R. G., Camilo, A. R., Giordano, A. J., & Leimgruber, P. (2023). Achieving conservation through cattle ranching: The case of the Brazilian Pantanal. Conservation Science and Practice, September.

Gomes, E. G., Santos, S. A., Paula, E. S. de, Nogueira, M. A., Oliveira, M. D. de, Salis, S. M., Soriano, B. M. A., & Tomas, W. M. (2023). Multidimensional performance assessment of a sample of beef cattle ranches in the Pantanal from a data envelopment analysis perspective. Ciência Rural, 53(12), 1–12.

Knauer, J., Cuntz, M., Smith, B., Canadell, J. G., Medlyn, B. E., Bennett, A. C., Caldararu, S., & Haverd, V. (2023). Higher global gross primary productivity under future climate with more advanced representations of photosynthesis. Science Advances, 9(46), 24–28.

p.s. This anglerfish jack-o-lantern was carved by my wife and me; we got the pumpkin with a really long stem and wanted a theme that would make good use of it

September 2023 science summary

Seal at Starlux mini golf


I had high hopes to do more reading this month but international travel and getting sick got in the way. So here are just two articles for some light summer reading.

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Provencher et al. 2023 models the potential carbon gains (and costs) to restore degraded rangelands (remotely sensed) in UT and NV (and some of OR, ID, and CA). The restoration sometimes involves herbicide to kill invasives and always involves: seeding w/ native perennial plants, excluding grazing for only 3 years from pixels that were seeded (grazing resumes after 3 years), and ending fire suppression. They found that invasive annual species like cheatgrass are more common than other analyses have found (Fig 8). See Table 3 for the key results: sequestration rates were very low in two sites (compared to less arid ecosystems) and modest in a third. Overall they ranged from 0.022 - 0.730 t CO2e / ha / yr (0.6-20 g C / m2 / yr). The best case scenario is in UT where ~$66 / ha delivers ~0.73 t CO2e/yr (+-50%), or ~$90 / t CO2e / yr (comparable to reforestation). Conversely the other ranches would be >$3,000 / t CO2e / yr. But selecting sites likely to be favorable to carbon accumulation could help make the case for ecological restoration (with empirical data needed if one wanted to sell carbon credits). And there is a LOT of degraded rangeland globally, so there's room to scale. To make carbon trading feasible in the Intermountain West, making this kind of seeding cheaper and more successful is important. 

I couldn't resist reading Clark et al. 2023 right away despite my sad backlog. I once had a native plant garden guy tell me "at best non-native plants offer no value to pollinators and other wildlife, and most are harmful." Obviously false as an absolute! But how do they compare? Clark looked at 10 species in a Connecticut forest and found some invasive species (like honeysuckle) had more bugs (mass and protein) than the average for natives, but others (Japanese barberry) had fewer bugs. But birds seemed to forage both equally. It's a tiny study and I wish they hadn't pooled all native species, but I do like a study that counters "it depends!" to a truism in conservation.


Clark, R. E. (2023). Are native plants always better for wildlife than invasives ? Insights from a community-level bird- exclusion experiment.

Provencher, L., Byer, S., Frid, L., Senthivasan, S., Badik, K. J., & Szabo, K. (2023). Carbon Sequestration in Degraded Intermountain West Rangelands, United States. Rangeland Ecology & Management, 90, 22–34.

p.s. This is a photo of a fountain at a mini golf course in Wildwood, NJ