More stories

  • in

    Root biomass and cumulative yield increase with mowing height in Festuca pratensis irrespective of Epichloë symbiosis

    Jackson, R. B. et al. The Ecology of soil carbon: Pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445. https://doi.org/10.1146/annurev-ecolsys-112414-054234 (2017).Article 

    Google Scholar 
    Sanderman, J., Hengl, T. & Fiske, G. J. Soil carbon debt of 12,000 years of human land use. PNAS 114, 9575–9580 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Amelung, W. et al. Towards a global-scale soil climate mitigation strategy. Nat. Commun. 11, 5427. https://doi.org/10.1038/s41467-020-18887-7 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Hopkins, A. & Holz, B. Grassland for agriculture and nature conservation: Production, quality and multi-functionality. Agron 4, 3–20 (2006).
    Google Scholar 
    van Veen, J. A., Liljeroth, E., Lekkerkerk, L. J. A. & van de Geijn, S. C. Carbon fluxes in plant-soil systems at elevated atmospheric CO2 levels. Ecol. Appl. 1, 175–181. https://doi.org/10.2307/1941810 (1991).Article 

    Google Scholar 
    Jones, M. B. & Donnelly, A. Carbon sequestration in temperate grassland ecosystems and the influence of management, climate and elevated CO2. New Phytol. 164, 423–439. https://doi.org/10.1111/j.1469-8137.2004.01201.x (2004).Article 

    Google Scholar 
    Ward, S. E. et al. Legacy effects of grassland management on soil carbon to depth. Glob. Change Biol. 22, 2929–2938. https://doi.org/10.1111/gcb.13246 (2016).Article 
    ADS 

    Google Scholar 
    Hungate, B. A. et al. The fate of carbon in grasslands under carbon dioxide enrichment. Nature 388, 576–579. https://doi.org/10.1038/41550 (1997).Article 
    ADS 
    CAS 

    Google Scholar 
    Six, J., Conant, R. T., Paul, E. A. & Paustian, K. Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils. Plant Soil 241, 155–176. https://doi.org/10.1023/A:1016125726789 (2002).Article 
    CAS 

    Google Scholar 
    Chang, J. et al. Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands. Nat. Commun. 12, 118. https://doi.org/10.1038/s41467-020-20406-7 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    IPCC. 2001. Climate change 2001: The scientific basis contribution of working group 1 to the third assessment report of the intergovernmental panel on climate change In (eds Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., Van Der Linden, P. J., Dai, X., Maskell, K. & Johnson, C. A.) (Cambridge University Press).Gwin, L. Scaling-up sustainable livestock production: Innovation and challenges for grass-fed beef in the U.S. J. Sustain. Agric. 33, 189–209. https://doi.org/10.1080/10440040802660095 (2009).Article 

    Google Scholar 
    Iqbal, J., Siegrist, J. A., Nelson, J. A. & McCulley, R. L. Fungal endophyte infection increases carbon sequestration potential of southeastern USA tall fescue stands. Soil Biol. Biochem. 44, 81–92. https://doi.org/10.1016/j.soilbio.2011.09.010 (2012).Article 
    CAS 

    Google Scholar 
    Robinson, R. A. & Sutherland, W. J. Post-war changes in arable farming and biodiversity in Great Britain. J. Appl. Ecol. 39, 157–176. https://doi.org/10.1046/j.1365-2664.2002.00695.x (2002).Article 

    Google Scholar 
    Law, Q. D., Bigelow, C. A. & Patton, A. J. Selecting turfgrasses and mowing practices that reduce mowing requirements. Crop Sci. 56, 3318–3327. https://doi.org/10.2135/cropsci2015.09.0595 (2016).Article 

    Google Scholar 
    White, L. M. Carbohydrate reserves of grasses: A review. Rangel Ecol. Manag. 26(1), 13–18 (1973).Article 
    CAS 

    Google Scholar 
    Virkajarvi, P. Effects of defoliation height on regrowth of timothy and meadow fescue in the generative and vegetative phases of growth. Agric. Food Sci. 12, 177–193 (2003).Article 

    Google Scholar 
    Reicher, Z., Patton, A. J., Bigelow, C. A. & Voigt, T. Mowing, Thatching, Aerifying, and Rolling Turf (Turf Grass Sci. Purdue Univ, 2006).
    Google Scholar 
    Kaatz, P. Cutting management for cool-season forage grasses. Michigan State University Extension, https://www.canr.msu.edu/news/cutting_management_for_cool_season_forage_grasses (2011).Briske, D. D. Strategies of plant survival in grazed systems: A functional interpretation. Ecol. Manag. Graz. Syst. 37–67 (1996).Crider, F. J. Root-growth stoppage resulting from defoliation of grass (No. 156759). United States Department of Agriculture, Economic Research Service (1995).Lal, R., Negassa, W. & Lorenz, K. Carbon sequestration in soil. Curr. Opin. Environ. Sustain. 15, 79–86. https://doi.org/10.1016/j.cosust.2015.09.002 (2015).Article 

    Google Scholar 
    Coughenour, M. B., McNaughton, S. J. & Wallace, L. L. Modelling primary production of perennial graminoids – uniting physiological processes and morphometric traits. Ecol. Modell. 23, 101–134. https://doi.org/10.1016/0304-3800(84)90121-2 (1984).Article 
    CAS 

    Google Scholar 
    Whipps, J. M. & Lynch, J. M. Energy losses by the plant in rhizodeposition. Plant products and the new technology / edited by K.W. Fuller and J.R. Gallon (1985).Johansson, G. Release of organic C from growing roots of meadow fescue (Festuca pratensis L.). Soil Biol. Biochem. 24, 427–433. https://doi.org/10.1016/0038-0717(92)90205-C (1992).Article 

    Google Scholar 
    Woodburn, A. T. Glyphosate: Production, pricing and use worldwide. Pest Manag. Sci. 56, 309–312. https://doi.org/10.1002/(SICI)1526-4998(200004)56:4%3c309::AID-PS143%3e3.0.CO;2-C (2000).Article 
    CAS 

    Google Scholar 
    Duke, S. O. & Powles, S. B. Glyphosate: A once-in-a-century herbicide. Pest Manag. Sci. 64, 319–325. https://doi.org/10.1002/ps.1518 (2008).Article 
    CAS 

    Google Scholar 
    Helander, M., Saloniemi, I. & Saikkonen, K. Glyphosate in northern ecosystems. Trends Plant Sci. 17, 569–574. https://doi.org/10.1016/j.tplants.2012.05.008 (2012).Article 
    CAS 

    Google Scholar 
    Benbrook, C. M. Trends in glyphosate herbicide use in the United States and globally. Environ. Sci. Eur. 28, 3. https://doi.org/10.1186/s12302-016-0070-0 (2016).Article 
    CAS 

    Google Scholar 
    Helander, M. et al. Glyphosate decreases mycorrhizal colonization and affects plant-soil feedback. Sci. Total Environ. 642, 285–291. https://doi.org/10.1016/j.scitotenv.2018.05.377 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Helander, M., Pauna, A., Saikkonen, K. & Saloniemi, I. Glyphosate residues in soil affect crop plant germination and growth. Sci. Rep. 9, 19653. https://doi.org/10.1038/s41598-019-56195-3 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Zaller, J. G. & Brühl, C. A. Editorial: Non-target effects of pesticides on organisms inhabiting agroecosystems. Front Environ. Sci. 7, 75. https://doi.org/10.3389/fenvs.2019.00075 (2019).Article 

    Google Scholar 
    Muola, A. et al. Risk in the circular food economy: Glyphosate-based herbicide residues in manure fertilizers decrease crop yield. Sci. Total Environ. 750, 141422. https://doi.org/10.1016/j.scitotenv.2020.141422 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Fuchs, B., Saikkonen, K. & Helander, M. Glyphosate-modulated biosynthesis driving plant defense and species interactions. Trends Plant Sci. 26, 312–323. https://doi.org/10.1016/j.tplants.2020.11.004 (2021).Article 
    CAS 

    Google Scholar 
    Fuchs, B. et al. A Glyphosate-based herbicide in soil differentially affects hormonal homeostasis and performance of non-target crop plants. Front Plant Sci. 12, 787958 (2022).Article 

    Google Scholar 
    Borggaard, O. K. & Gimsing, A. L. Fate of glyphosate in soil and the possibility of leaching to ground and surface waters: A review. Pest Manag. Sci. 64, 441–456. https://doi.org/10.1002/ps.1512 (2008).Article 
    CAS 

    Google Scholar 
    Rueppel, M. L., Brightwell, B. B., Schaefer, J. & Marvel, J. T. Metabolism and degradation of glyphosate in soil and water. J. Agric. Food Chem. 25, 517–528. https://doi.org/10.1021/jf60211a018 (1977).Article 
    CAS 

    Google Scholar 
    Carlisle, S. M. & Trevors, J. T. Glyphosate in the environment. Wat Air Soil Poll 39, 409–420 (1988).Article 
    ADS 
    CAS 

    Google Scholar 
    Torstensson, N. T. L., Lundgren, L. N. & Stenström, J. Influence of climatic and edaphic factors on persistence of glyphosate and 2,4-D in forest soils. Ecotoxicol. Environ. Saf. 18, 230–239. https://doi.org/10.1016/0147-6513(89)90084-5 (1989).Article 
    CAS 

    Google Scholar 
    Stenrød, M., Eklo, O. M., Charnay, M.-P. & Benoit, P. Effect of freezing and thawing on microbial activity and glyphosate degradation in two Norwegian soils. Pest Manag. Sci. 61, 887–898. https://doi.org/10.1002/ps.1107 (2005).Article 
    CAS 

    Google Scholar 
    Antier, C. et al. Glyphosate use in the European agricultural sector and a framework for its further monitoring. Sustainability 12, 5682. https://doi.org/10.3390/su12145682 (2020).Article 
    CAS 

    Google Scholar 
    Jones, R. J. Effect of an associate grass, cutting interval, and cutting height on yield and botanical composition of Siratro pastures in a sub-tropical environment. Aust. J. Exp. Agric. 14, 334–342. https://doi.org/10.1071/ea9740334 (1974).Article 

    Google Scholar 
    Volenec, J. J. & Nelson, C. J. Responses of Tall Fescue leaf meristems to N fertilization and harvest frequency. Crop Sci. 23(4), 720–724. https://doi.org/10.2135/cropsci1983.0011183X002300040028x (1983).Article 

    Google Scholar 
    Saikkonen, K. et al. Fungal endophytes help prevent weed invasions. Agric. Ecosyst. Environ. 165, 1–5. https://doi.org/10.1016/j.agee.2012.12.002 (2013).Article 

    Google Scholar 
    Scavo, A. & Mauromicale, G. Integrated weed management in herbaceous field crops. Agronomy 10, 466. https://doi.org/10.3390/agronomy10040466 (2020).Article 

    Google Scholar 
    Clay, K. & Holah, J. Fungal endophyte symbiosis and plant diversity in successional fields. Science 285, 1742–1744. https://doi.org/10.1126/science.285.5434.1742 (1999).Article 
    CAS 

    Google Scholar 
    Gundel, P. E., Pérez, L. I., Helander, M. & Saikkonen, K. Symbiotically modified organisms: Nontoxic fungal endophytes in grasses. Trends Plant Sci. 18, 420–427. https://doi.org/10.1016/j.tplants.2013.03.003 (2013).Article 
    CAS 

    Google Scholar 
    Kauppinen, M., Saikkonen, K., Helander, M., Pirttilä, A. M. & Wäli, P. R. Epichloë grass endophytes in sustainable agriculture. Nat. Plants 2, 15224 (2016).Article 

    Google Scholar 
    Clay, K. Fungal endophytes of grasses. Annu. Rev. Ecol. Syst. 21, 275–297 (1990).Article 

    Google Scholar 
    Saikkonen, K., Young, C. A., Helander, M. & Schardl, C. L. Endophytic Epichloë species and their grass hosts: From evolution to applications. Plant Mol. Biol. 90, 665–675. https://doi.org/10.1007/s11103-015-0399-6 (2016).Article 
    CAS 

    Google Scholar 
    Ahlholm, J. U., Helander, M., Lehtimäki, S., Wäli, P. & Saikkonen, K. Vertically transmitted fungal endophytes: Different responses of host-parasite systems to environmental conditions. Oikos 99, 173–183. https://doi.org/10.1034/j.1600-0706.2002.990118.x (2002).Article 

    Google Scholar 
    Easton, H. S. & Fletcher, L. R. in Proc. 6th International Symposium Fungal Endophytes of Grasses (eds Popay, A. J. & Thom, E. R.) 11–18 (New Zealand Grassland Association, 2007).Saari, S., Lehtonen, P., Helander, M. & Saikkonen, K. High variation in frequency of infection by endophytes in cultivars of meadow fescue in Finland. Grass Forage Sci. 64, 169–176. https://doi.org/10.1111/j.1365-2494.2009.00680.x (2009).Article 

    Google Scholar 
    König, J., Fuchs, B., Krischke, M., Mueller, M. J. & Krauss, J. Hide and seek: Infection rates and alkaloid concentrations of Epichloë festucae var. lolii in Lolium perenne along a land-use gradient in Germany. Grass Forage Sci. 73, 510–516. https://doi.org/10.1111/gfs.12330 (2018).Article 
    CAS 

    Google Scholar 
    Krauss, J. et al. Epichloë endophyte infection rates and alkaloid content in commercially available grass seed mixtures in Europe. Microorganisms 8, 498. https://doi.org/10.3390/microorganisms8040498 (2020).Article 
    CAS 

    Google Scholar 
    Brink, G. E., Casler, M. D. & Martin, N. P. Meadow Fescue, Tall Fescue, and Orchardgrass response to defoliation management. Agronomy J 102, 667–674. https://doi.org/10.2134/agronj2009.0376 (2010).Article 

    Google Scholar 
    Conant, R. T., Cerri, C. E. P., Osborne, B. B. & Paustian, K. Grassland management impacts on soil carbon stocks: A new synthesis. Ecol. Appl. 27, 662–668. https://doi.org/10.1002/eap.1473 (2017).Article 

    Google Scholar 
    Trlica, M. J. Distribution and utilization of carbohydrate reserves in range plants. In (ed Sosebee, R. E.) 73–96 (Rangeland Plant Physiology, 1977).Faeth, S. H. & Sullivan, T. J. Mutualistic asexual endophytes in a native grass are usually parasitic. Am. Nat. 161, 310–325. https://doi.org/10.1086/345937 (2003).Article 

    Google Scholar 
    Saikkonen, K., Saari, S. & Helander, M. Defensive mutualism between plants and endophytic fungi?. Fungal Divers. 41, 101–113. https://doi.org/10.1007/s13225-010-0023-7 (2010).Article 

    Google Scholar 
    Clay, K. & Schardl, C. Evolutionary origins and ecological consequences of endophyte symbiosis with grasses. Am. Nat. 160, 99–127. https://doi.org/10.1086/342161 (2002).Article 

    Google Scholar 
    Rozpądek, P. et al. The fungal endophyte Epichloë typhina improves photosynthesis efficiency of its host orchard grass (Dactylis glomerata). Planta 242, 1025–1035. https://doi.org/10.1007/s00425-015-2337-x (2015).Article 
    CAS 

    Google Scholar 
    Xia, C. et al. An Epichloë endophyte improves photosynthetic ability and dry matter production of its host Achnatherum inebrians infected by Blumeria graminis under various soil water conditions. Fungal Ecol. 22, 26–34. https://doi.org/10.1016/j.funeco.2016.04.002 (2016).Article 

    Google Scholar 
    Malinowski, D., Leuchtmann, A., Schmidt, D. & Nosberger, J. Symbiosis with Neotyphodium uncinatum endophyte may increase the competitive ability of meadow fescue. Agron. J. 89, 833–839 (1997).Article 

    Google Scholar 
    Schardl, C. L., Leuchtmann, A. & Spiering, M. J. Symbioses of grasses with seedborne fungal endophytes. Ann. Rev. Plant Biol. 55, 315–340. https://doi.org/10.1146/annurev.arplant.55.031903.141735 (2004).Article 
    CAS 

    Google Scholar 
    Chen, Z. et al. Fungal endophyte improves survival of Lolium perenne in low fertility soils by increasing root growth, metabolic activity and absorption of nutrients. Plant Soil 452, 185–206. https://doi.org/10.1007/s11104-020-04556-7 (2020).Article 
    CAS 

    Google Scholar 
    Franz, J. E., Mao, M.K. and Sikorski, J.A. (1997). Uptake, transport and metabolism of glyphosate in plants, in Glyphosate: A unique global herbicide, ed by Franz JE, ACS Monograph No 189, American Chemical Society, Washington, DC, pp 143–181.Pline, W. A., Wilcut, J. W., Edmisten, K. L. & Wells, R. Physiological and morphological response of glyphosate-resistant and non-glyphosate-resistant cotton seedlings to root-absorbed glyphosate. Pestic. Biochem. Phys. 73, 48–58. https://doi.org/10.1016/S0048-3575(02)00014-7 (2002).Article 
    CAS 

    Google Scholar 
    Johansson, G. Carbon distribution in grass (Festuca pratensis L.) during regrowth after cutting—utilization of stored and newly assimilated carbon. Plant Soil 151, 11–20. https://doi.org/10.1007/BF00010781 (1993).Article 
    ADS 
    CAS 

    Google Scholar 
    Ergon, Å. et al. How can forage production in Nordic and Mediterranean Europe adapt to the challenges and opportunities arising from climate change?. Euro J. Agron. 92, 97–106. https://doi.org/10.1016/j.eja.2017.09.016 (2018).Article 

    Google Scholar 
    Niemelainen, O. et al. Increase in perennial forage yields driven by climate change, at Apukka Research Station, Rovaniemi, 1980–2017. Agric. Food Sci. 29, 139–153 (2020).Article 

    Google Scholar 
    Anwar, M. R., Liu, D. L., Macadam, I. & Kelly, G. Adapting agriculture to climate change: A review. Theor. Appl. Climatol. 113, 225–245. https://doi.org/10.1007/s00704-012-0780-1 (2013).Article 
    ADS 

    Google Scholar 
    Farmit. Nurmea yli kymppitonni hehtaarilta. Farmit.net. (accessed 28 June 2022); https://www.farmit.net/nurmikasvit-lypsylehma/2016/05/24/nurmea-yli-kymppitonni-hehtaarilta (2016).Peltonen, S., Aalto, K., Hennola, I. & Anttila, S. (Eds.). Peltojen kunnostus. (Tieto Tuottamaan; No. 145), (ProAgria Keskusten Liiton julkaisuja; No. 1163). ProAgria maaseutukeskusten liitto (2019).Laihonen, M., Saikkonen, K., Helander, M. & Tammaru, T. Insect oviposition preference between Epichloë-symbiotic and Epichloë-free grasses does not necessarily reflect larval performance. Ecol. Evol. 10, 7242–7249. https://doi.org/10.1002/ece3.6450 (2020).Article 

    Google Scholar  More

  • in

    Revenue loss due to whale entanglement mitigation and fishery closures

    Whale entanglements in fishing gear threaten whale populations, seafood production and long-term sustainability of commercial fisheries. While multiple mitigation strategies to reduce entanglements exist, there has been minimal consideration of the economic impact of these strategies. Here, we estimated retrospective losses to ex-vessel revenues for one of California’s most lucrative fisheries. Overall, we found fishery closures decreased ex-vessel revenue, with results showing some uncertainty due to large model prediction error. Regional differences in losses revealed interesting trends in the capacity for the fishery to recoup costs. For example, in the NMA, relatively small losses at the fishery level were predicted ($0.3 million in total) for the 2019 season despite an early closure to the season due to whale entanglement risk.NMA fishers collectively were able to meet predicted revenue for the season despite a shortening of the fishing 2019 season. In the 2020 season however, the NMA did not experience disturbances due to whale entanglements but larger ex-vessel losses (of $3.9 million) were predicted. This suggests that other disturbances such as a delay to the season due to crab meat quality, lost fishing opportunity related to the COVID-19 pandemic, or other unknown factors, had an influence on ex-vessel revenue during the 2020 season. While most of the 2020 season landings in the NMA occurred before COVID-19 arrived in the US, there is evidence that prices in latter part of the season may have been depressed due to loss of export markets for live crab47.In the CMA however, despite landing the majority of crab available during the 2019 season (see Fig. 2c), losses of $9.4 million were experienced across the fishery. While total fishery catch was not greatly reduced, closure to the fishery in the spring may be responsible for revenue losses through other mechanisms (e.g. price). In the 2020 season, whale entanglement risk substantially shortened the fishing season in the CMA, through a delay at the beginning of the season and an early closure in the spring. Estimated losses were largest ($14.4 million) during this season. It is likely that the COVID-19 pandemic was also responsible for some of this estimated loss in the CMA in the 2020 season47. Our model did not control for impacts of the COVID-19 pandemic. However, price trends suggest that that price of Dungeness Crab in California was not affected until mid-March 2020, at which point the fishery had caught 92% of the seasons catch (see Supplementary File S2). Prices then returned to normal levels in mid-May. If we apply extrapolated prices between mid-March and mid-May by replacing observed prices with linearly increasing prices by week, revenues would have been $753,754 higher in total across the fishery. This rough estimate suggests we can attribute 4.1% of overall estimated revenue losses during the 2020 season to COVID-19 impacts, with the caveat that we do not know what prices would have been in the absence of the pandemic. A counterfactual approach has been used to disentangle multiple stressors to infer causal impacts of management interventions elsewhere48, however as these closures, and the COVID pandemic, potentially impacted all fishers in the California Dungeness crab fishery, there are no control groups available for comparison and therefore this approach would not be appropriate.Closures and other disturbances appear to have been less impactful in the NMA and high price for Dungeness crab may have contributed to the ability of vessels operating in the NMA to withstand disturbances (Supplementary Fig. S2). Prices were particularly high during the summer portion of the season in 2020 during which time the CMA was closed to Dungeness crab fishing (Supplementary Fig. S2). The NMA did not experience closures due to whale entanglement during 2020 and was predicted to have lower than average pre-season abundance (lower catch potential) during 2020 (see Fig. 2.b), while the CMA was predicted to have high catch potential for 2020 (Fig. 2.c), therefore differences in management measures implemented, and seasons’ catch potential, also contributed to differences in losses estimated.The CMA also experienced high prices, including decadal high prices for crab during the November–December of the 2019 fishing season (Supplementary Fig. S2). However, losses observed overall across the two seasons suggest the fishery, unlike the NMA, did not get much overall benefit from the high price in 2019 or the high pre-season abundance of crab (i.e. catch potential) estimated for the 2020 season in the CMA. A number of factors may have contributed to a poor season in the CMA including catchability or biology of Dungeness crab as well as external factors such as the COVID-19 pandemic behavioral choice factors, for example deciding not to fish45. Temporally shifting or reducing the opportunity for participation through closed periods due to whale entanglement risk may have exacerbated other impacts on revenues in the CMA which were not as impactful on revenues in the NMA.The high variability in estimated economic impacts per vessel reported here demonstrates that closures did not affect all vessels equally, similarly to impacts observed following a climate related harmful algal bloom in the 2016 season which were variable by vessel size and between communities45. The estimated losses we present at the fishery level in the NMA and CMA may therefore be underestimated, or overestimated, for particular groups of vessels within those management areas. This reflects the diverse nature of the Dungeness Crab fishery in behaviour and fishing strategy and highlights the importance of capturing impacts at finer scales than the fishery level alone.Limitations to the estimation of closure impactsA limitation of the hurdle model is that there are other latent factors influencing fishery participation and revenues that our model does not incorporate, particularly those determining fisher behavior such as fuel price, shipyard backlogs and market demand. A behavioral choice model, for example one that incorporates location or fishing alternative choice given a closure50,51,52 would be a potential method to better understand how spatial management strategies affect fisher behavior and is recommended as a future analysis to assess trade-offs involving socio-economic risk. Our results, reporting losses from Dungeness crab fishing revenue only, also do not account for the ability of some fishers to mitigate revenue losses by participating in other fisheries. Dungeness crab fishing is highly connected within west coast fishery participation networks44,45. Thus, it is important to note that our results for the 2019 and 2020 seasons present only losses from Dungeness crab fishing and may overestimate total annual revenue losses by some vessels that are able to mitigate impacts with participation in other fisheries.The model, predicting out-of-sample, over-estimated revenues in recent years suggesting that our predictions of revenues may also be over-predicted. An improved estimation at the vessel level, given some over-estimation of vessels that did not fish, could be investigated through a selection model approach rather than a two-part model approach54. However, two-part models are most appropriate for estimation of conditional (actual) outcomes as was intended here rather than unconditional (potential) outcomes and they do not require separate drivers for the selection and estimation model, which we did not have available54. When the impacts of policy interventions are difficult to disentangle from other impacts, approaches such as a counterfactual synthetic control48 approach could be used to separate the impacts of the policy alone. In this context, however, it is useful to report the cumulative impact of disturbances given that these disturbances (e.g., delays due to crab quality, harmful algal blooms) happen frequently and therefore the closures will rarely happen in isolation.Whilst there are limitations to our approach, revenue predictions presented here offer more insight compared to predicting revenues based only on a 5-year average of total fishery revenues (Supplementary Table S3) as is commonly conducted to calculate disaster assistance requirement, as our analysis includes an estimation of crab abundance as well as historical vessel level data in its estimation. Accounting for the influence of crab abundance is critical in this fishery given abundance is highly variable and the majority of fishable biomass is taken each year. Estimation of revenue at the individual vessel level allows for consideration of fishery heterogeneity (e.g., by vessel size). Revenues calculated on a 5-year average would suggest total California Commercial Dungeness crab fishery revenues would have been $10.62 million higher than observed in 2019 and $12.73 million higher than observed in 2020 (Supplementary Table S3). Thus, revenues estimated on the 5-year average suggest that losses would have been $0.97 million higher than our model prediction across the fishery for 2019 and $5.56 million lower than our model prediction for 2020. Our predictions suggest that delays and closures due to whale entanglement mitigation and other disturbances in to the 2019 and 2020 seasons were similar to the impact of closures due to the HAB in the 2016 season, which were estimated at $13.6 million in losses from Dungeness Crab revenues across the fishery38.Economic cost of mitigationMany strategies that prevent fishery interactions with marine mammals exist, including gear reductions or modifications, depth limitations and dynamic or seasonal time-area closures13,14,22,23,24,25,26,55. Whilst the fishery does implement pro-active gear modification measures set out in the best practices guide34, only two management intervention options were enacted in the 2019 and 2020 seasons to mitigate against entanglements of marine life with Dungeness crab gear; delays to the start of the crab season in the winter and early closures in spring due to overlap with whale distribution in fishing grounds. These delays and closures can have differential impacts on the fishery as the fishing season is not heterogeneously prosperous. An example is that closures during the holiday season (Nov–Dec) when Dungeness crab is traditionally consumed can cause substantial lost revenue opportunity for fishers at a time when price and demand are highest35,49. The fishery operates as a derby in which the majority of revenues are made in the first month of the fishery being open. The strong seasonal dynamics of the Dungeness crab fishery, largely driven by rapid depletion of legal sized crab, mean that the timing of management actions can have important impacts on fishing revenues. Across the fishery, based on observed vessel level revenues during the 2011–2018 baseline period, vessels earned an average of 62.33% (SD 24.04) of annual ex-vessel revenue during the first month of the season (15th Nov–15th Dec for the CMA/1st Dec–31st Dec for the NMA). After April 1st, vessels on average earn 10.54% (SD 18.98) of annual ex-vessel revenue. This average, based only on vessels that historically have actively participate past April 1st, (283 vessels in the NMA, 346 vessels in the CMA) rises to 20.36% (SD 13.37) of ex-vessel revenue. Thus, while the majority of the overall fisheries revenue is taken at the start of the season, an April 1st closure could still have a substantial impact on the revenues of active fishing vessels in the spring. Determination of economic risk for the fishery, at a minimum, should consider timing of closures in addition to total revenue losses, in order to quantify losses that will be felt at the individual vessel level. We suggest further research to investigate how closures affect different groups of fishers through stakeholder participation.Socio-economic impacts from whale mitigation measures could permeate into communities further than our analysis (based on ex-vessel revenue only) conveys35,36,37,49, and further investigation into these community level impacts is necessary to understand and sustain an equitable fishery supply chain even where there is no absolute revenue loss. Some of the communities influenced by whale entanglement mitigation in California rely heavily on ocean resources for employment, through fishing occupations but also through hospitality and tourism. Managing this issue in a way that minimizes the burden on resource dependent communities is strongly in line with the objectives set out in the UN Sustainable Development Goals (SDG’s), especially SDG 14 (life below water) but also related goals such as human well-being, reducing inequality and reducing the impacts of climate change56.Management ImplicationsBalancing socio-economic impacts against whale entanglement risk is challenging given the legally protected status of whale populations. However, potential economic losses reported here should motivate the development of mitigation measures (through cooperative innovation between industry, researchers and managers) that allow fishery production to be optimized whilst ensuring successful whale protection. At present, entire management areas, which constitute large regions of the coast, are closed in response to whale entanglement risk in California. Investigating how to minimize the spatiotemporal footprint of closures, such as by defining high risk zones dynamically based on fine-scale information of whale density and fishing effort, could provide an alternative mitigation structure. This could better consider the economic and conservation trade-offs while still being sensitive to changing environmental conditions. The introduction of dynamic zone closures, often broadly referred to as dynamic ocean management, has been demonstrated to reduce risk whilst minimizing lost fishing opportunities12,26,57,58, especially when environmental variability is high or species have a dynamic distribution59. Moreover, analysis of policy instruments to reduce whale entanglements with the American lobster fishery on the US Northeast coast found that economic costs of risk reduction could be 20% lower when mitigation decisions considered fishing opportunity costs alongside non-monetary benefits (biological risk), compared to non-monetary benefits alone12. This is promising for the implementation of such strategies in the California Current System.The caveat of this strategy is that dynamic zone closures require spatially and temporally explicit information on whale density and fishing effort which can be costly to attain. The use of ropeless gear has also been suggested as an alternative whale entanglement mitigation measure that requires further research and development before being initiated as an alternative regulatory tool60. The costs of monitoring or technical advancements however may outweigh the financial and societal cost of fishery closures. Revenue losses for Dungeness crab estimated here for the 2019 and 2020 seasons are on par with losses experienced during the HAB period. During the delays to the 2016 fishing season an estimated $26.1 million was lost from ex-vessel revenues from all species that crab fishers target, including $13.6 million from Dungeness crab alone38, requiring $25 million in government aid. Whale mitigation under the RAMP regulation will potentially delay or close the fishery year after year with uncertain economic impact that cannot be sustainably resolved with government aid. Development of tools to mitigate against economic loss while achieving whale protection will be necessary to come to a sustainable solution. This can only be achieved by first including economic loss in risk assessments. Doing so may also provide balance to partnerships between fishery managers and fishers.Regulators are obligated to protect Humpback whales, blue whales and Leatherback turtles using the best available science33. In this fishery, current triggers to open and close are based on a range of factors, but thus ultimately depend on the number of whales present within a management region33. Regulators have a number of alternative regulatory options available to them, which include depth restrictions, gear restrictions or modifications and fleet advisories, if they can offer the same level of whale protection33. Yet, the RAMP process lacks the socio-economic information needed to consider the socio-economic risk of regulatory actions, and that of the alternatives, to the fishing community. Results presented here highlight that the economic effects and that risk to fishing communities should be considered when designing whale entanglement mitigation programs33. Having this economic information will facilitate the ability of managers, as set out in the RAMP regulation (subsection d4)33, to consider the socio-economic impact if deciding between management measures that equivalently reduce entanglement risk.We have used two fishing seasons as an example of the economic impacts of these new whale entanglement regulations which will be implemented each year going forward. Synthesis of ex-vessel revenues is not a complete picture of the socio-economic impacts of regulations, but it provides a starting point for protecting both whales and fishing communities. While reported whale entanglements remain higher than pre-2014 totals, reported whale entanglements in California have declined markedly in the years following the 2014–2016 large marine heatwave (Fig. 1b). This is a success for this fishery and attributed to increased awareness, development of best practices for fishing gear and the mitigation program to protect whales. We now need to be successful at protecting and mitigating the socio-economic impacts to fishery participants and the fishing communities they support. More

  • in

    Different roles of concurring climate and regional land-use changes in past 40 years’ insect trends

    All statistical analyses were performed through R version 4.1.050. Besides the explicitly mentioned packages, the R packages cowplot51, data.table52, dplyr53, ggplot254, itsadug55, purrr56, raster57, sf58, sfheaders59, tibble60 and tidyr61 were key for data handling, data analysis, and plotting. Posterior distributions were summarised through means and credible intervals (CIs). CIs are the highest density intervals, calculated through the package bayestestR62. To summarise multiple posterior distributions, 5000 Monte Carlo simulations were used.Study regionThe study included data from the whole of Switzerland. As an observation unit for records, we chose 1 × 1 km squares (henceforth squares). Switzerland covers 41,285 km2, spanning a large gradient in elevation, climate and land use. It can be divided into five coarse biogeographic regions based on floristic and faunistic distributions and on institutional borders of municipalities63 (Fig. 1b). The Jura is a mountainous but agricultural landscape in the northwest (~4200 km2, 300–1600 m asl; annual mean temperature: ~9.4 °C, annual precipitation: ~1100 mm (gridded climate data here and in the following from MeteoSwiss (https://www.meteoswiss.admin.ch), average 1980–2020, at sites ~500 m asl.)). The Jura is separated from the Alps by the Plateau, which is the lowland region spanning from the southwest to the northeast (~11,300 km2, 250–1400 m asl, mostly below 1000 m asl; ~9.5 °C, ~1100 mm). It is the most densely populated region with most intensive agricultural use. For the Alps, three regions can be distinguished. The Northern Alps (~10,700 km2, 350–4000 m asl; ~9.2 °C, ~1400 mm), which entail the area from the lower Prealps, which are rather densely populated and largely used agriculturally, up to the northern alpine mountain range. The Central Alps (~11,300 km2, 450–4600 m asl; ~9.5 °C, ~800 mm) comprise of the highest mountain ranges in Switzerland and the inner alpine valleys characterised by more continental climate (i.e., lower precipitation). Intensive agricultural land use is concentrated in the lower elevations and agriculture in higher elevations is mostly restricted to grassland areas used for summer grazing. The Southern Alps (~3800 km2, 200–3800 m asl; ~10.4 °C, 1700 mm) range from the southern alpine mountain range down to the lowest elevations of Switzerland and are clearly distinguished from the other regions climatically, as they are influenced by Mediterranean climate, resulting in, e.g., milder winters. Besides differences between biogeographic regions, climate, land use and changes therein vary greatly between different elevations (Supplementary Fig. S9). To account for these differences, we split the regions in two elevation classes at the level of squares. All squares with a mean elevation of less than 1000 m asl were assigned to the low elevation, whereas squares above 1000 m asl were assigned to the high elevation (no squares in the Plateau fell in the high elevation). This resulted in nine bioclimatic zones (Fig. 1b), for which separate species trends were estimated in the subsequent analyses. The threshold of 1000 m asl enabled a meaningful distinction based on the studied drivers (climate and land-use change) and was also determined by the availability of records data (high coverage in all nine bioclimatic zones).Species detection dataWe extracted records of butterflies (refers here to Papilionoidea as well as Zygaenidae moths), grasshoppers (refers here to all Orthoptera) and dragonflies (refers here to all Odonata) from the database curated by info fauna (The Swiss Faunistic Records Centre; metadata available from the GBIF database at https://doi.org/10.15468/atyl1j, https://doi.org/10.15468/bcthst, https://doi.org/10.15468/fcxtjg). This database unites faunistic records made in Switzerland from various sources including both records by private persons and from projects such as research projects, Red-List inventories or checks of revitalisation measures. Only records with a sufficient precision, both temporally (day of recording) and spatially (place of recording known to the precision of 1 km2 or less), were used for analyses. Besides temporal and spatial information, information on the observer and the project (if any) was obtained for each record. All records made by a person/project on a day in a square were attributed to one visit, which was later used as replication unit to model the observation process (see below).We included records from the focal time range 1980–2020. Additionally, we included records from 1970–1979 for butterflies in occupancy-detection models to increase the robustness of mean occupancy estimates. We excluded the mean occupancy estimates for these additional years from further analyses to cover the same period for all groups. Prior to analyses, following the approach in ref. 26, we excluded observations of non-adult stages and observations from squares that only were visited in 1 year of the studied period, because these would not contain any information on change between years64. This resulted in 18,018 squares (15,248 for butterflies, 9870 for grasshoppers, 5188 for dragonflies) and 1,448,134 records (879,207 butterflies, 272,863 grasshoppers, 296,064 dragonflies) that we included in the analyses (Supplementary Fig. S2). The three datasets for the different groups were treated separately for occupancy-detection modelling, following the same procedures for all three groups. To determine detections and non-detections for each species and visit, which could then be used for occupancy-detection modelling, we only included visits that (a) did not originate from a project, which had a restricted taxonomic focus not including the focal species, (b) were not below the 5% quantile or above the 95% quantile of the day of the year at which the focal species has been recorded26 and (c) were from a bioclimatic zone, from which the focal species was recorded at least once.Occupancy-detection modelsWe used occupancy-detection models65,66 to estimate annual mean occupancy of squares for the whole of Switzerland and for the nine bioclimatic zones for each species (i.e., mean number of squares occupied by a species), mostly following the approach in ref. 26. We fitted a separate model for each species, based on different datasets for the three groups. We included only species that were recorded in any square in at least 25% of all analysed years. Occupancy-detection models are hierarchical models in which two interconnected processes are modelled jointly, one of which describes occurrence probability (ecological process; used to infer mean occupancy), whereas the other describes detection probability (observation process)65. The two processes are modelled through logistic regression models. The occupancy model estimates occurrence probability for all square and year combinations, whereas the observation model estimates the probability that a species has been detected by an observer during a visit. More formally, each square i in the year t has the latent occupancy status zi,t, which may be either 1 (present) or 0 (absent). zi,t depends on the occurrence probability ψi,t as follows$${z}_{i,t}sim {{{mbox{Bern}}}}left({psi }_{i,t}right)$$
    (1)
    The occupancy status is linked to the detection/non-detection data yi,t,j at square i in year t at visit j as$${y}_{i,t,, j}{{|}}{z}_{i,t}sim {mathrm {Bern}}({z}_{i,t}{p}_{i,t,j})$$
    (2)
    where pi,t,j is the detection probability.The regression model for occurrence probability (occupancy model) looked as follows$${{mbox{logit}}}({psi }_{i,t})={mu }_{o}+{beta }_{o1}{{{{{rm{elevatio}}}}}}{{{{{{rm{n}}}}}}}_{i}+{beta }_{o2}{{{{{rm{elevatio}}}}}}{{{{{{rm{n}}}}}}}_{i}^{2}+{alpha }_{o1,i}+{alpha }_{o2,i}+{gamma }_{r(i),t}$$
    (3)
    with μo being the global intercept, elevationi being the scaled elevation above sea level and αo1,i, αo2,i and γr(i),t being the random effects for fine biogeographic region (12 levels, Supplementary Fig. S10; these were again defined based on floristic and faunistic distributions and followed institutional borders63), square and year. The random effects for fine biogeographic region and square were modelled as follows:$${alpha }_{o1}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{o1}right)$$
    (4)
    and$${alpha }_{o2}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{o2}right)$$
    (5)
    The random effect of the year was implemented with separate random walks per zone following ref. 67, which allowed the effect to vary between the nine bioclimatic zones, while accounting for dependencies among consecutive years. Conceptually, in random walks, the effect of 1 year is dependent on the previous year’s effect, resulting in trajectories with less sudden changes between consecutive years. This was implemented as follows:$${gamma }_{r,t}sim left{begin{array}{c}{{{{{rm{Normal}}}}}}left(0,{1.5}^{2}right){{{{rm{for}}}}},t=1\ {{{{{rm{Normal}}}}}}left({gamma }_{r,t-1},{sigma }_{gamma r}^{2}right){{{{rm{for}}}}},t , > ,1end{array}right.$$
    (6)
    with$${sigma }_{gamma r}sim {{mbox{Cauchy}}}left(0,1right)$$
    (7)
    The regression model for detection probability (observation model) looked as follows$${{{{rm{logit}}}}}({p}_{i,t,j}) =, {mu }_{d}+{beta }_{d1}{{{{{rm{yda}}}}}}{{{{{{rm{y}}}}}}}_{j}+{beta }_{d2}{{{{{rm{yda}}}}}}{{{{{{rm{y}}}}}}}_{j}^{2}+{beta }_{d3}{{{{{rm{shortlis}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d4}{{{{{rm{longlis}}}}}}{{{{{{rm{t}}}}}}}_{j} \ quad+ {beta }_{d5}{{{{{rm{exper}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d6}{{{{{rm{projec}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d7}{{{{{rm{targeted}}}}}}_{{{{{rm{projec}}}}}}{{{{{{rm{t}}}}}}}_{j} \ quad+ {beta }_{d8}{{{{{rm{redlis}}}}}}{{{{{{rm{t}}}}}}}_{j}+{alpha }_{d1,t}$$
    (8)
    where μd is the global intercept, ydayj is the scaled day of the year of visit j, shortlistj and longlistj are dummies of a three-level factor denoting the number of species recorded during the visit (1; 2–3; >3), and expertj, projectj, targeted_projectj and redlistj are dummies of a five-level factor denoting the source of the data. The source might either be a common naturalist observation (reference level), an observation by an expert naturalist, an observation made during a not further specified project, an observation made in a project targeted at the focal species or an observation made during a Red-List inventory. An expert naturalist was defined as an observer that contributed a significant number of records, which was defined as the upper 2.5% quantile of all observers arranged by their total number of records, and that made at least one visit with an exceptionally long species list, which was defined as a visit in the upper 2.5% quantile of all visits arranged by the number of records. The proportions of records originating from these different sources changed across years, but change was not unidirectional and differed among the investigated groups (Supplementary Fig. S11), such that accounting for data source in the model should suffice to yield reliable estimates of occupancy trends. αd1,t is a random effect for year, which was modelled as$${alpha }_{d1}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{d1}right)$$
    (9)
    The occupancy and observation models were fitted jointly in Stan through the interface CmdStanR68. Four Markov chain Monte Carlo chains with 2000 iterations each, including 1000 warm-up iterations, were used. Priors of the model parameters are specified in Supplementary Table S5. For the prior distribution of global intercepts, a standard deviation of 1.5 was chosen to not overweight the extreme values on the probability scale. To ensure that chains mixed well, Rhat statistics for annual mean occupancy estimates were calculated through the package rstan69. For Switzerland-wide annual estimates (n = 18,140), 98.0% of values met the standard threshold of 1.1 (99.9% of values More

  • in

    Francisella tularensis PCR detection in Cape hares (Lepus capensis) and wild rabbits (Oryctolagus cuniculus) in Algeria

    Tularemia affects animal welfare, human health, and the environment and is thus better approached from a one-health perspective27. Several studies in the Northern hemisphere28, and more recently in Australia15,16, have provided a vital research track in the epidemiology of this disease. In contrast, studies in Africa are too limited and scarce. The aim of this study was to investigate the presence of tularemia in wild leporids collected in Northern Algeria. These animals are highly susceptible to F. tularensis infection and considered sentinel hosts for surveillance of tularemia. The strategy we used to detect F. tularensis in leporids mainly used molecular, histological and immunohistochemical analyzes of tissues taken from animals found dead or hunted. To the best of our knowledge, detection of F. tularensis by PCR or culture has not been previously reported in wild leporidae in Algeria or other African countries.Animal tissue samples were tested using three qPCR assays of variable sensitivity and specificity. The Type B-qPCR test targets a specific junction between ISFtu2 and a flanking 3′ region, which is considered specific for F. tularensis subsp. holarctica26, the only tularemia agent found in Europe and Asia. The Tul4-qPCR assay targets a simple copy gene encoding a surface protein, which can be found in the genome of all F. tularensis subspecies causing tularemia and that of the aquatic bacterium F. novicida. Because F. novicida has never been isolated from lagomorphs or other animal species, and very rarely from human29, a positive Tul4 qPCR for the studied tissue samples likely indicated the presence of F. tularensis DNA. The ISFtu2 qPCR is considered highly sensitive because multiple copies of this insertion sequence are found in the F. tularensis genome. However, it lacks specificity because ISFtu2 is also found in many other Francisella species25.Two animals were considered “probable” tularemia cases because some of their samples were positive for the three qPCR tests. Ten animals were considered “possible” tularemia cases because their samples were positive for the ISFtu2 and Tul4 qPCRs but not the Type B qPCR. Finally 19 leporids were “uncertain” cases because only samples positive for the ISFtu2 qPCR were found. For the remaining 43 animals, all the tested samples were negative for the three qPCRs. Overall, we detected F. tularensis DNA-positive samples in 12/74 (16.21%) leporids, which strongly suggest that tularemia is present in the lagomorph population of the study area. The positive Type B qPCR tests in two animals suggested that F. tularensis subsp. holarctica could be the involved subspecies. We did not confirm these data by isolating F. tularensis from the studied leporids. However, the isolation of this pathogen from human or animal samples is tedious and has a low sensitivity13. Moreover, most of our samples were not appropriate for F. tularensis culture because of their long-term preservation in ethanol 70° or 10% formalin. Further study using fresh (non-fixed) tissue samples from dead leporids collected in the same study area is needed to definitively confirm the presence of tularemia in these animals and characterize the F. tularensis subspecies and genotypes involved.Although PCR is usually more sensitive than culture for detecting F. tularensis, it also has some limitations. Firstly, the DNA extraction from organs preserved in ethanol for several months was difficult although easier for spleen than for liver samples. Some tissue samples could be lysed only after overnight incubation with proteinase K. Secondly, tissue samples contained PCR inhibitors as demonstrated by better DNA amplification from some samples after their dilution in PCR grade water. To reduce the effect of PCR inhibitors, organ samples with negative qPCR were retested using Bovine Serum Albumin (BSA) and the Real-time PCR system TaqMan (Applied Biosystems, Munich, Germany)30. Finally, DNA regions to be amplified were optimized to obtain high sensitivity and specificity of qPCR tests.IHC detection of F. tularensis in formalin-fixed tissue can be helpful for tularemia diagnosis31,32. For one possible tularemia case, F. tularensis could be detected on immunohistochemical (IHC) examination of a liver sample using a specific anti-F. tularensis antibody. The intensity and localization of positive staining were comparable to those previously recorded for other animals32,33. IHC did not provide interpretable findings for four other tested specimens. Such negative results might be explained by an inhomogeneous distribution of infectious foci in the involved organs as well as a low bacterial inoculum in infected tissues. This has been previously demonstrated in tularemia granulomatous lesions in cell types like epithelial cells of the kidney, testis, and epididymis, hepatocytes, and bronchiolar epithelial cells31. Besides, IHC is a delicate technology whose results are highly dependent on the quality and fixation time of the organ tissues34. IHC analysis of dead animal tissues remains challenging, especially in case of tissue necrosis34.In our limited case series we found a F. tularensis infection prevalence in leporids of 2.7% (2/74) for probable tularemia cases and 16.2% (12/74) when considering both probable and posible cases. We cannot make a guess about the prevalence of tularemia because our series is not representative of the general lagomorph population in the study area. In Germany, F. tularensis DNA was detected in 1.1% of European Brown hares and 2.4% of wild rabbits collected between 2009 and 201435. Higher infection rates were reported in the same country, including 11.8% (100/848 animals) in hares collcted in the North Rhine-Westphalia region36 and 30% (55/179) in brown hares collected between 2010 and 2016 in Baden-Wuerttemberg37. In Hungary, the prevalence of tularemia in hares was evaluated at 4.9–5.3%38. In Portugal, prevalences of 4.3% and 6.3% were reported in brown hares and wild rabbits, respectively39. However, the comparison of the reported tularemia prevalences in leporids is irrelevant because studies involved different animal species and geographic areas, and used different methods for F. tularensis detection.Two possibilities could explain the lack of detection of tularemia in Algeria before this study. The first hypothesis is that this disease was not searched for in previous years, while it could have been present in this country for decades. The second hypothesis is that tularemia was recently imported in Algeria. Migratory birds may have been involved in the long-distance spread of F. tularensis40. These hosts can be infested by ectoparasites such as ticks which are the primary vectors of tularemia41,42. They can also spread the bacteria in the hydro-telluric environment through their secretions and feces18,43,44. An alternative possibility is that F. tularensis-infected animals (especially game animals) have been imported in Algeria from endemic countries. Whatever the mode of introduction of tularemia in Algeria, the dissemination of this disease over time might have been facilitated by the ability of F. tularensis to infect multiple hosts and its better survival in a cool environment45, which characterizes Northern Algeria climate. The emergence or re-emergence of tularemia in other countries has been related to climate change, human-mediated movement of infected animals, and wartime resulting in a significant rise of F. tularensis infections in the rodent populations39,46.In our study, infected animals were collected throughout 4 years, although more frequently in autumn. Probable and possible tularemia cases were mainly collected during the hunting season (i.e., September, October, November, and December). Animals could not be collected in February because of heavy rains and in May and June because it corresponds to female leporids’ lactation period. In most endemic countries, tularemia cases are typically more frequent in late spring, the summer months, and early autumn37,47,48,49,50. Occasionally, fatal tularemia cases in hares have been predominantly reported during the cold season11,51. The climatic conditions can affect tularemia outbreaks in animals, depending on the reservoir involved and the predominant modes of infection52.We detected tularemia more frequently in female than in male hares, and the reverse was true for wild rabbits. The prevalence of tularemia in male or female lagomorphs varies between studies. In Sweden, Morener et al.50 reported a tularemia case series only involving male hares. In the same country, Borg et al.50 observed an overrepresentation of females in the epizootic of 1967. They suggested that, compared to males, females had a higher risk of exposure to infected mosquitoes or were more vulnerable to tularemia because they were pregnant or had just given birth to a litter50. Tularemia was found in a few juveline leporids, which might be explained by a shorter exposure time to F. tularensis, a higher death rates due to higher susceptibility to F. tularensis infection or easier predation by their natural enemies, or more frequent hunting of adults compared to the juveniles53.Tularemia is usually more frequently detected in leporids found dead than in hunted animals. As an example, a German study reported a higher prevalence of tularemia in hares found dead (2.9%) than in hunted ones (0.7%)35. In our study, most qPCR-positive animals were hunted. Our study might not be representative of the prevalence of tularemia in either population because most collected animals had been hunted.The incubation period and clinical presentation of tularemia in leporids vary according to the species considered. Tularemia is typically an acute disease in mountain hares (Lepus timidus) in Scandinavia and has a chronic pattern in European brown hares (Lepus europaeus) in Central Europe50. The incubation time and clinical presentation of tularemia can be different in Cape hares (Lepus capensis). Wild rabbits are less sensitive to F. tularensis infection than hares31,39,54. An extended incubation period and chronic evolution of tularemia would facilitate the detection of F. tularensis in infected animals. In our study, a similar tularemia prevalence was found in the Cape hares and wild rabbits, which might reflect exposure to a same biotope area and environmental reservoirs of F. tularensis.The pathological lesions of tularemiia in leporids can vary according to the F. tularensis strain involved, the mode and route of infection, and the susceptibility and immune status of the host32,50. In the European brown hares, granulomas with central necrosis have been reported in the lungs and kidneys and occasionally in the liver, spleen, bone marrow, and lymph nodes50. In contrast, only acute necrosis in the liver, spleen, bone marrow, and lymph nodes have been found in Lepus timudus hares in Sweden50. The lesions in the Japanese hare (Lepus brachyurus angustidens) are comparable to those of Lepus timidus, except for cutaneous, lung, brain, and adrenal gland lesions32. In the European rabbit, Oryctolagus cuniculus, tularemia is not associated with identifiable macroscopic tissue lesions39,55. To our knowledge, no reports describing post-mortem lesions in Cape hares with tularemia are available. In this study, similar lesions were found in hares and wild rabbits except necrotic foci only observed in some wild rabbit organs (such as liver, lungs, kidney, ovary). Most animals had pathological lesions of pneumonia, gastritis and enteritis. Kidney lesions and adrenal glands enlargment were oberved. Necrotic lesions were occasionally found in the lungs, liver, spleen and ovary and hemorrhages in the lungs, liver, and intestines.Tularemia is an arthropod-born disease in most endemic areas14,22,28. In our study, 50% of positive leporids were infested by known tularemia vectors such as ticks (Ixodes ricinus56,57, Rhipicephalus sanguineus39), fleas (Spillopsylus cuniculi58), and lice of lagomorphs (Haemodipsus lepori and Haemodipsus setoni59,60). Ticks are the most significant arthropod vectors of tularemia61. Ticks are frequently involved in the transmission of tularemia in North America, including Dermacentor andersoni, D. variabilis, and Amblyomma americanum57,62,63. In Europe, tick-borne tularemia represents 13% to 26% of human cases57,64. The involved species include D. marginatus, D. reticulatus, I. ricinus, R. sanguineus, and Haemaphysalis concinna65,66. Further research on wild leporid sucking arthropods is needed to confirm the presence and clarify the ecology of F. tularensis in Algeria.Our study reports for the first time the detection of F. tularensis DNA in leporids from Northern Algeria. The markers most in favor of tularemia in the animals studied are the positivity of qPCR tests, in particular, the “type B” qPCR test which amplifies a specific DNA sequence of F. tularensis subsp. holarctica, and a positive immunohistological examination in one animal. Further investigation is needed to confirm our results by the isolation of this pathogen from animal samples and determine the F. tularensis subspecies and genotypes involved. This would allow the characterization of the F. tularensis subspecies and genotypes present in Algeria. Furthermore, our findings push us in future studies to seek tularemia in the Algerian human population. To achieve this, interdisciplinary or trans-disciplinary collaborative efforts underpinned by the One Health concept will be necessary. More

  • in

    Siberian carbon sink reduced by forest disturbances

    Keenan, R. J. et al. Dynamics of global forest area: results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manage. 352, 9–20 (2015).Article 

    Google Scholar 
    Arneth, A. et al. in Special Report on Climate Change and Land (eds Shukla, P. R. et al.) Ch. 1 (IPCC, 2019).Piao, S. et al. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, GB3018 (2007).Article 

    Google Scholar 
    Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).Article 

    Google Scholar 
    Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 

    Google Scholar 
    Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).Article 

    Google Scholar 
    Chen, J. M. et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10, 4259 (2019).Article 

    Google Scholar 
    Myneni, R. B. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).Article 

    Google Scholar 
    Filipchuk, A. et al. Russian forests: a new approach to the assessment of carbon stocks and sequestration capacity. Environ. Dev. 26, 68–75 (2018).Article 

    Google Scholar 
    Goodale, C. L. et al. Forest carbon sinks in the Northern Hemisphere. Ecol. Appl. 12, 891–899 (2002).Article 

    Google Scholar 
    Tchebakova, N. M. et al. Energy and mass exchange and the productivity of main Siberian ecosystems (from eddy covariance measurements). 2. Carbon exchange and productivity. Biol. Bull. 42, 579–588 (2015).Article 

    Google Scholar 
    Vaganov, E. A. et al. Forests and swamps of Siberia in the global carbon cycle. Contemp. Probl. Ecol. 1, 168–182 (2008).Article 

    Google Scholar 
    Schepaschenko, D. et al. Russian forest sequesters substantially more carbon than previously reported. Sci. Rep. 11, 12825 (2021).Article 

    Google Scholar 
    Shvidenko, A. & Schepaschenko, D. Climate change and wildfires in Russia. Contemp. Probl. Ecol. 6, 683–692 (2013).Article 

    Google Scholar 
    Bradshaw, C. J. A. & Warkentin, I. G. Global estimates of boreal forest carbon stocks and flux. Glob. Planet. Change 128, 24–30 (2015).Article 

    Google Scholar 
    Curtis, P. G. et al. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).Article 

    Google Scholar 
    Sukhinin, A. I. et al. AVHRR-based mapping of fires in Russia: new products for fire management and carbon cycle studies. Remote Sens. Environ. 93, 546–564 (2004).Article 

    Google Scholar 
    Soja, A. J. et al. Climate-induced boreal forest change: predictions versus current observations. Glob. Planet. Change 56, 274–296 (2007).Article 

    Google Scholar 
    Dolman, A. J. et al. An estimate of the terrestrial carbon budget of Russia using inventory-based, eddy covariance and inversion methods. Biogeosciences 9, 5323–5340 (2012).Article 

    Google Scholar 
    Schaphoff, S. et al. Tamm review: Observed and projected climate change impacts on Russia’s forests and its carbon balance. For. Ecol. Manage. 361, 432–444 (2016).Article 

    Google Scholar 
    de Jong, R. et al. Trend changes in global greening and browning: contribution of short-term trends to longer-term change. Glob. Change Biol. 18, 642–655 (2012).Article 

    Google Scholar 
    Buermann, W. et al. Recent shift in Eurasian boreal forest greening response may be associated with warmer and drier summers. Geophys. Res. Lett. 41, 1995–2002 (2014).Article 

    Google Scholar 
    Rödig, E. et al. Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory. Glob. Ecol. Biogeogr. 26, 1292–1302 (2017).Article 

    Google Scholar 
    Quegan, S. et al. Estimating the carbon balance of central Siberia using a landscape-ecosystem approach, atmospheric inversion and dynamic global vegetation models. Glob. Change Biol. 17, 351–365 (2011).Article 

    Google Scholar 
    Gurney, K. R. et al. Interannual variations in continental-scale net carbon exchange and sensitivity to observing networks estimated from atmospheric CO2 inversions for the period 1980 to 2005. Glob. Biogeochem. Cycles 22, GB3025 (2008).Article 

    Google Scholar 
    Stephens, B. B. et al. Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2. Science 316, 1732–1735 (2007).Article 

    Google Scholar 
    Leskinen, P. et al. Russian Forests and Climate Change: What Science Can Tell Us 11 (EFI, 2020); https://doi.org/10.36333/wsctu11Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).Article 

    Google Scholar 
    Stow, D. A. et al. Remote sensing of vegetation and land-cover change in Arctic tundra ecosystems. Remote Sens. Environ. 89, 281–308 (2004).Article 

    Google Scholar 
    Karlsen, S. R. et al. A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high Arctic plant productivity. Environ. Res. Lett. 13, 025011 (2018).Article 

    Google Scholar 
    Ding, Z. et al. Nearly half of global vegetated area experienced inconsistent vegetation growth in terms of greenness, cover, and productivity. Earths Future 8, e2020EF001618 (2020).Article 

    Google Scholar 
    Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).Article 

    Google Scholar 
    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 

    Google Scholar 
    Giglio, L. et al. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).Article 

    Google Scholar 
    Blunden, J. & Arndt, D. S. State of the climate in 2015. Bull. Am. Meteorol. Soc. 97, Si–S275 (2016).Article 

    Google Scholar 
    Bastos, A. et al. Was the extreme Northern Hemisphere greening in 2015 predictable? Environ. Res. Lett. 12, 044016 (2017).Article 

    Google Scholar 
    Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).Article 

    Google Scholar 
    Kukavskaya, E. A. et al. Biomass dynamics of central Siberian Scots pine forests following surface fires of varying severity. Int. J. Wildland Fire 23, 872–886 (2014).Article 

    Google Scholar 
    Gauthier, S. et al. Boreal forest health and global change. Science 349, 819 (2015).Article 

    Google Scholar 
    Harris, N. L. et al. Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573 (2012).Article 

    Google Scholar 
    Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).Article 

    Google Scholar 
    Rogers, B. M. et al. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nat. Geosci. 8, 228–234 (2015).Article 

    Google Scholar 
    Shvetsov, E. G. et al. Assessment of post-fire vegetation recovery in southern Siberia using remote sensing observations. Environ. Res. Lett. 14, 055001 (2019).Article 

    Google Scholar 
    Wang, J. A. et al. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nat. Clim. Change 11, 435–441 (2021).Article 

    Google Scholar 
    Xu, L. et al. Changes in global terrestrial live biomass over the 21st century. Sci. Adv. 7, eabe9829 (2021).Article 

    Google Scholar 
    Shuman, J. K. et al. Forest forecasting with vegetation models across Russia. Can. J. For. Res. 45, 175–184 (2014).Article 

    Google Scholar 
    Flannigan, M. et al. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob. Change Biol. 15, 549–560 (2009).Article 

    Google Scholar 
    Yuan, W. et al. Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome. Nat. Commun. 5, 4270 (2014).Article 

    Google Scholar 
    Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change 11, 234–240 (2021).Article 

    Google Scholar 
    Larjavaara, M. et al. Post-fire carbon and nitrogen accumulation and succession in Central Siberia. Sci. Rep. 7, 12776 (2017).Article 

    Google Scholar 
    Berner, L. T. et al. Cajander larch (Larix cajanderi) biomass distribution, fire regime and post-fire recovery in northeastern Siberia. Biogeosciences 9, 3943–3959 (2012).Article 

    Google Scholar 
    Myneni, R. et al. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid v.006 (LAADS DAAC, 2015).Houghton, R. A. et al. Mapping Russian forest biomass with data from satellites and forest inventories. Environ. Res. Lett. 2, 045032 (2007).Article 

    Google Scholar 
    DiMiceli, C. et al. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000–2014, Collection 5 Percent Tree Cover v.6 (University of Maryland, 2017).Simard, M. et al. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. 116, G04021 (2011).
    Google Scholar 
    Broxton, P. et al. A global land cover climatology using MODIS data. J. Appl. Meteorol. Climatol. 53, 1593–1605 (2014).Article 

    Google Scholar 
    Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).Article 

    Google Scholar 
    Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data. 13, 3927–3950 (2021).Article 

    Google Scholar 
    Carreiras, J. M. B. et al. Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions. Remote Sens. Environ. 196, 154–162 (2017).Article 

    Google Scholar 
    Penman, J. et al. Good Practice Guidance for Land Use, Land-Use Change and Forestry (IGES, 2013).Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).Article 

    Google Scholar 
    Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).Article 

    Google Scholar 
    Fernandez-Moran, R. et al. SMOS-IC: an alternative SMOS soil moisture and vegetation optical depth product. Remote Sens. 9, 457 (2017).Article 

    Google Scholar 
    Wigneron, J.-P. et al. SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives. Remote Sens. Environ. 254, 112238 (2021).Article 

    Google Scholar 
    Mitchard, E. T. A. et al. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Glob. Ecol. Biogeogr. 23, 935–946 (2014).Article 

    Google Scholar 
    Mitchard, E. T. A. et al. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps. Carbon Balance Manage. 8, 10 (2013).Article 

    Google Scholar 
    Harmon, M. E. et al. Release of coarse woody detritus-related carbon: a synthesis across forest biomes. Carbon Balance Manage. 15, 1 (2020).Article 

    Google Scholar 
    Bartalev, S. A. & Stytsenko, F. V. Assessment of forest-stand destruction by fires based on remote-sensing data on the seasonal distribution of burned areas. Contemp. Probl. Ecol. 14, 711–716 (2021).Article 

    Google Scholar 
    van Wees, D. et al. The role of fire in global forest loss dynamics. Glob. Change Biol. 27, 2377–2391 (2021).Article 

    Google Scholar 
    Vicente‐Serrano, S. M. et al. A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J. Clim. 23, 1696–1718 (2010).Article 

    Google Scholar 
    Schepaschenko, D. et al. A new hybrid land cover dataset for Russia: a methodology for integrating statistics, remote sensing and in situ information. J. Land Use Sci. 6, 245–259 (2011).Article 

    Google Scholar 
    Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations. Earth Syst. Sci. Data. 9, 791–808 (2017).Article 

    Google Scholar 
    Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).Article 

    Google Scholar 
    De Grandpré, L. et al. Long-term post-fire changes in the northeastern boreal forest of Quebec. J. Veg. Sci. 11, 791–800 (2000).Article 

    Google Scholar  More

  • in

    Natural plant diet impacts phenotypic expression of pyrethroid resistance in Anopheles mosquitoes

    Bhatt, S. et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 526, 207–211 (2015).Article 
    ADS 

    Google Scholar 
    WHO. Test procedures for insecticide resistance monitoring in malaria vector mosquitoes (2016).WHO. Guidelines for malaria vector control (2019).Gnankiné, O. et al. Insecticide resistance in Bemisia tabaci Gennadius (Homoptera: Aleyrodidae) and Anopheles gambiae Giles (Diptera: Culicidae) could compromise the sustainability of malaria vector control strategies in West Africa. Acta Trop. 128, 7–17 (2013).Article 

    Google Scholar 
    Ranson, H. & Lissenden, N. Insecticide resistance in African Anopheles mosquitoes: A worsening situation that needs urgent action to maintain malaria control. Trends Parasitol. 32, 187–196 (2016).Article 

    Google Scholar 
    Reid, M. C. & McKenzie, F. E. The contribution of agricultural insecticide use to increasing insecticide resistance in African malaria vectors. Malar. J. 15, 1–8 (2016).Article 

    Google Scholar 
    Huijben, S. & Paaijmans, K. P. Putting evolution in elimination: Winning our ongoing battle with evolving malaria mosquitoes and parasites. Spec. Issue Rev. Synth. https://doi.org/10.1111/eva.12530 (2017).Article 

    Google Scholar 
    WHO. Global technical strategy for malaria 2016–2030, 2021 update (2021).Ranson, H. et al. Identification of a point mutation in the voltage-gated sodium channel gene of Kenyan Anopheles gambiae associated with resistance to DDT and pyrethroids. Insect Mol. Biol. 9, 491–497 (2000).Article 

    Google Scholar 
    Weill, M. et al. The unique mutation in ace-1 giving high insecticide resistance is easily detectable in mosquito vectors. Insect Mol. Biol. 13, 1–7 (2004).Article 
    ADS 

    Google Scholar 
    Ranson, H. et al. Pyrethroid resistance in African anopheline mosquitoes: What are the implications for malaria control?. Trends Parasitol. 27, 91–98 (2011).Article 

    Google Scholar 
    Hemingway, J., Hawkes, N. J., McCarroll, L. & Ranson, H. The molecular basis of insecticide resistance in mosquitoes. Insect Biochem. Mol. Biol. 34, 653–665 (2004).Article 

    Google Scholar 
    Martinez-Torres, D. et al. Molecular characterization of pyrethroid knockdown resistance (kdr) in the major malaria vector Anopheles gambiae s.s.. Insect Mol. Biol. 7, 179–184 (1998).Article 

    Google Scholar 
    Jones, C. et al. Footprints of positive selection associated with a mutation (N1575Y) in the voltage-gated sodium channel of Anopheles gambiae. Proc. Natl. Acad. Sci. U. S. A. 109, 6614–6619 (2012).Article 
    ADS 

    Google Scholar 
    Hunt, R. H., Brooke, B. D., Pillay, C., Koekemoer, L. L. & Coetzee, M. Laboratory selection for and characteristics of pyrethroid resistance in the malaria vector Anopheles funestus. Med. Vet. Entomol. 19, 271–275 (2005).Article 

    Google Scholar 
    Glunt, K. D., Thomas, M. B. & Read, A. F. The effects of age, exposure history and malaria infection on the susceptibility of Anopheles mosquitoes to low concentrations of pyrethroid. PLoS One 6, e24968 (2011).
    Article 
    ADS 

    Google Scholar 
    Rajatileka, S., Burhani, J. & Ranson, H. Mosquito age and susceptibility to insecticides. Trans. R. Soc. Trop. Med. Hyg. 105, 247–253 (2011).Article 

    Google Scholar 
    Chouaibou, M. S. et al. Increase in susceptibility to insecticides with aging of wild Anopheles gambiae mosquitoes from Côte d’Ivoire. BMC Infect. Dis. 12, 1–7 (2012).Article 

    Google Scholar 
    Jones, C. M. et al. Aging partially restores the efficacy of malaria vector control in insecticide-resistant populations of Anopheles gambiae s.l. from Burkina Faso. Malar. J. 11, 1–11 (2012).Article 

    Google Scholar 
    Kulma, K., Saddler, A. & Koella, J. C. Effects of age and larval nutrition on phenotypic expression of insecticide-resistance in Anopheles Mosquitoes. PLoS ONE 8, 8–11 (2013).Article 

    Google Scholar 
    Aïzoun, N., Aïkpon, R., Azondekon, R., Asidi, A. & Akogbéto, M. Comparative susceptibility to permethrin of two Anopheles gambiae s.l. populations from Southern Benin, regarding mosquito sex, physiological status and mosquito age. Asian Pac. J. Trop. Biomed. 4, 312–317 (2014).Article 

    Google Scholar 
    Collins, E. et al. The relationship between insecticide resistance, mosquito age and malaria prevalence in Anopheles gambiae s.l. from Guinea. Sci. Rep. 9, 1–12 (2019).Article 

    Google Scholar 
    Oliver, S. & Brooke, B. The effect of larval nutritional deprivation on the life history and DDT resistance phenotype in laboratory strains of the malaria vector Anopheles arabiensis. Malar. J. 12, 1–9 (2013).Article 

    Google Scholar 
    Owusu, H. F., Chitnis, N. & Müller, P. Insecticide susceptibility of Anopheles mosquitoes changes in response to variations in the larval environment. Sci. Rep. 7, 1–9 (2017).Article 

    Google Scholar 
    Sovegnon, P. M., Fanou, M. J., Akoton, R. & Djihinto, O. Y. Effects of larval diet on the life-history traits and phenotypic expression of pyrethroid resistance in the major malaria vector Anopheles gambiae s.s. Preprint at bioRxiv http://doi.org/https://doi.org/10.1101/2022.01.11.475801 (2022).Halliday, W. R. & Feyereisen, R. Why does DDT toxicity change after a blood meal in adult female Culex pipiens?. Pestic. Biochem. Physiol. 28, 172–181 (1987).Article 

    Google Scholar 
    Oliver, S. V., Lyons, C. L. & Brooke, B. D. The effect of blood feeding on insecticide resistance intensity and adult longevity in the major malaria vector Anopheles funestus (Diptera: Culicidae). Sci. Rep. 12, 1–9 (2022).Article 

    Google Scholar 
    Farenhorst, M. et al. Fungal infection counters insecticide resistance in African malaria mosquitoes. Proc. Natl. Acad. Sci. U. S. A. 106, 17443–17447 (2009).Article 
    ADS 

    Google Scholar 
    Koella, J. C., Saddler, A. & Karacs, T. P. S. Blocking the evolution of insecticide-resistant malaria vectors with a microsporidian. Evol. Appl. 5, 283–292 (2012).Article 

    Google Scholar 
    Alout, H. et al. Interplay between Plasmodium infection and resistance to insecticides in vector mosquitoes. J. Infect. Dis. 210, 1464–1470 (2014).Article 

    Google Scholar 
    Glunt, K. D., Oliver, S. V., Hunt, R. H. & Paaijmans, K. P. The impact of temperature on insecticide toxicity against the malaria vectors Anopheles arabiensis and Anopheles funestus. Malar. J. 17, 1–8 (2018).Article 

    Google Scholar 
    Oliver, S. & Brooke, B. The effect of commercial herbicide exposure on the life history and insecticide resistance phenotypes of the major malaria vector Anopheles arabiensis (Diptera: culicidae). Acta Trop. 188, 152–160 (2018).Article 

    Google Scholar 
    Oliver, S. & Brooke, B. The effect of metal pollution on the life history and insecticide resistance phenotype of the major malaria vector Anopheles arabiensis (Diptera: Culicidae). PLoS ONE 13, 1–17 (2018).Article 

    Google Scholar 
    Foster, W. A. Mosquito sugar feeding and reproductive energetics. Annu. Rev. Entomol. 40, 443–474 (1995).Article 

    Google Scholar 
    Nyasembe, V. O., Tchouassi, D. P., Pirk, C. W. W., Sole, C. L. & Torto, B. Host plant forensics and olfactory-based detection in Afro-tropical mosquito disease vectors. PLoS Negl. Trop. Dis. 12, 1–21 (2018).Article 

    Google Scholar 
    Barredo, E. & DeGennaro, M. Not just from blood: Mosquito nutrient acquisition from nectar sources. Trends Parasitol. 36, 473–484 (2020).Article 

    Google Scholar 
    Stone, C. M. & Foster, W. A. Plant-sugar feeding and vectorial capacity. In Ecology of Parasite-Vector Interactions (eds Takken, W. & Koenraadt, C.) 35–79 (Wageningen Academic, 2013). https://doi.org/10.3920/978-90-8686-744-8_3.Chapter 

    Google Scholar 
    Hien, D. F. D. S. et al. Plant-mediated effects on mosquito capacity to transmit human malaria. PLoS Pathog. 12, e1005773 (2016).Article 

    Google Scholar 
    Stone, C., Witt, A., Walsh, G., Foster, W. & Murphy, S. Would the control of invasive alien plants reduce malaria transmission? A review. Parasites Vectors 11, 1–18 (2018).Article 

    Google Scholar 
    Ebrahimi, B. et al. Alteration of plant species assemblages can decrease the transmission potential of malaria mosquitoes. J. Appl. Ecol. 55, 841–851 (2018).Article 

    Google Scholar 
    Manda, H. et al. Discriminative feeding behaviour of Anopheles gambiae s.s. on endemic plants in western Kenya. Med. Vet. Entomol. 21, 103–111 (2007).Article 

    Google Scholar 
    Nyasembe, V. O. et al. Plasmodium falciparum infection increases Anopheles gambiae attraction to nectar sources and sugar uptake. Curr. Biol. 24, 217–221 (2014).Article 

    Google Scholar 
    Després, L., David, J. P. & Gallet, C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 22, 298–307 (2007).Article 

    Google Scholar 
    Nkya, T. E., Akhouayri, I., Kisinza, W. & David, J. P. Impact of environment on mosquito response to pyrethroid insecticides: Facts, evidences and prospects. Insect Biochem. Mol. Biol. 43, 407–416 (2013).Article 

    Google Scholar 
    Li, X., Schuler, M. A. & Berenbaum, M. R. Molecular mechanisms of metabolic resistance to synthetic and natural xenobiotics. Annu. Rev. Entomol. 52, 231–253 (2007).Article 

    Google Scholar 
    Bationo, C. S. et al. Spatio-temporal analysis and prediction of malaria cases using remote sensing meteorological data in Diébougou health district, Burkina Faso, 2016–2017. Sci. Rep. 11, 1–12 (2021).Article 

    Google Scholar 
    Namountougou, M. et al. First report of the L1014S kdr mutation in wild populations of Anopheles gambiae M and S molecular forms in Burkina Faso (West Africa). Acta Trop. 125, 123–127 (2013).Article 

    Google Scholar 
    Service, M. W. A critical review of procedures for sampling populations of adult mosquitoes. Bull. Entomol. Res. 67, 343–382 (1977).Article 

    Google Scholar 
    Thiombiano, A. et al. Catalogue des plantes vasculaires du Burkina Faso. In Boissiera Vol. 65 (ed Cyrille Chatelain) (Conservatoire et Jardin botaniques, 2012).Morlais, I., Ponçon, N., Simard, F., Cohuet, A. & Fontenille, D. Intraspecific nucleotide variation in Anopheles gambiae: New insights into the biology of malaria vectors. Am. J. Trop. Med. Hyg. 71, 795–802 (2004).Article 

    Google Scholar 
    Santolamazza, F. et al. Insertion polymorphisms of SINE200 retrotransposons within speciation islands of Anopheles gambiae molecular forms. Malar. J. 7, 163 (2008).Article 

    Google Scholar 
    R Core Team. A language and environment for statistical computing (2021).Crawley, M. J. The R Book (Wiley, 2007).Book 
    MATH 

    Google Scholar 
    Lenth, R. V. emmeans: Estimated marginal means, aka least-squares means (2021).Hien, A. et al. Evidence supporting deployment of next generation insecticide treated nets in Burkina Faso: Bioassays with either chlorfenapyr or piperonyl butoxide increase mortality of pyrethroid-resistant Anopheles gambiae. Malar. J. 20, 1–13 (2021).Article 

    Google Scholar 
    Nicolson, S. W., Nepi, M. & Pacini, E. Nectaries and Nectar (Springer, Dordrecht, 2007).Book 

    Google Scholar 
    Abdu-Allah, G. et al. Dietary antioxidants impact DDT resistance in Drosophila melanogaster. PLoS ONE 15, 1–12 (2020).Article 

    Google Scholar 
    Gnankiné, O. & Bassolé, I. L. H. N. Essential oils as an alternative to pyrethroids’ resistance against Anopheles species complex giles (Diptera: Culicidae). Molecules 22, 1321 (2017).Article 

    Google Scholar 
    Gendrin, M. & Christophides, G. K. The Anopheles mosquito microbiota and their impact on pathogen transmission. In Anopheles Mosquitoes—New Insights into Malar. Vectors (ed. Manguin, S.) (IntechOpen, 2013).Saab, S. A. et al. The environment and species affect gut bacteria composition in laboratory co-cultured Anopheles gambiae and Aedes albopictus mosquitoes. Sci. Rep. 10, 1–13 (2020).Article 

    Google Scholar 
    Dada, N., Sheth, M., Liebman, K., Pinto, J. & Lenhart, A. Whole metagenome sequencing reveals links between mosquito microbiota and insecticide resistance in malaria vectors. Sci. Rep. 8, 1–13 (2018).Article 

    Google Scholar 
    Barnard, K., Jeanrenaud, A. C. S. N., Brooke, B. D. & Oliver, S. V. The contribution of gut bacteria to insecticide resistance and the life histories of the major malaria vector Anopheles arabiensis (Diptera: Culicidae). Sci. Rep. 9, 1–11 (2019).Article 

    Google Scholar 
    Omoke, D. et al. Western Kenyan Anopheles gambiae showing intense permethrin resistance harbour distinct microbiota. Malar. J. 20, 1–14 (2021).Article 

    Google Scholar 
    Pelloquin, B. et al. Overabundance of Asaia and Serratia Bacteria is associated with deltamethrin insecticide susceptibility in Anopheles coluzzii from Agboville, Côte d’Ivoire. Microbiol. Spectr. 9, e00157-21 (2021).Article 

    Google Scholar 
    WHO. Test procedures for insecticide resistance monitoring in malaria vector mosquitoes (2013).Owusu, H. F., Jančáryová, D., Malone, D. & Müller, P. Comparability between insecticide resistance bioassays for mosquito vectors: Time to review current methodology?. Parasites Vectors 8, 1–11 (2015).Article 

    Google Scholar  More

  • in

    Crown feature effect evaluation on wind load for evergreen species based on laser scanning and wind tunnel experiments

    Wang, Z. & Yuan, J. Typhoon numerical simulation and visualization for disaster risk assessment. Bull. Surv. Mapp. 108–110, 132 (2015).ADS 

    Google Scholar 
    Tang, J., Xu, L., Li, Y., He, Y. & Cui, S. Assessing the damage caused by typhoon on urban green space ecosystme service based on UAV remote sensing. J. Nat. Disasters 27, 153–161 (2018).
    Google Scholar 
    Tian, Y., Zhou, W., Qian, Y., Zheng, Z. & Pan, X. The influence of Typhoon Mangkhut on urban green space and biomass in Shenzhen, China. Acta Ecol. Sin. 40, 2589–2598 (2020).
    Google Scholar 
    Lin, G. Major anti-wind and alkali-resisting landscape plants of south China’s seaside region. For. Invertory Plan. 29, 78–81 (2004).
    Google Scholar 
    Lin, T. C., Hogan, J. A. & Chang, C. T. Tropical cyclone ecology: a scale-link perspective. Trends Ecol. Evol. 35, 594–604 (2020).Article 

    Google Scholar 
    Lin, H. et al. Risk assessments of trees and urban spaces under attacks of typhoons: a survey and statistical study in Guangzhou, China. IOP Conf. Ser. Earth Environ. Sci. 588, 032055 (2020).Article 

    Google Scholar 
    Loehle, C. Biomechanical constraints on tree architecture. Trees Struct. Funct. 30, 2061–2070 (2016).Article 

    Google Scholar 
    Zhu, J., Matsuzaki, T. & Sakioka, K. Wind speeds within a single crown of Japanese black pine (Pinus thunbergii Parl.). For. Ecol. Manag. 135, 19–31 (2000).Article 

    Google Scholar 
    Ver Planck, N. R. & MacFarlane, D. W. Branch mass allocation increases wind throw risk for Fagus grandifolia. For. Int. J. For. Res. 92, 490–499 (2019).
    Google Scholar 
    Dunham, R. A. & Cameron, A. D. Crown, stem and wood properties of wind-damaged and undamaged Sitka spruce. For. Ecol. Manag. 135, 73–81 (2000).Article 

    Google Scholar 
    Burcham, D. C., Autio, W. R., Modarres-Sadeghi, Y. & Kane, B. After pruning, wind-induced bending moments and vibration decrease more on reduced than raised Senegal mahogany (Khaya senegalensis). Urban For. Urban Green. 61, 127100 (2021).Article 

    Google Scholar 
    Gilman, E. F., Masters, F. & Grabosky, J. C. Pruning affects tree movement in hurricane force wind. Arboric. Urban For. 34, 20–28 (2008).Article 

    Google Scholar 
    Päätalo, M. L., Peltola, H. & Kellomäki, S. Modelling the risk of snow damage to forests under short-term snow loading. For. Ecol. Manag. 116, 51–70 (1999).Article 

    Google Scholar 
    North, E., Johnson, G., Murphy, R., Giblin, C. & Rendahl, A. Boulevard tree failures during wind loading events. Arboric. Urban For. 45, 259–269 (2019).
    Google Scholar 
    Angelou, N., Dellwik, E. & Mann, J. Wind load estimation on an open-grown European oak tree. For. Int. J. For. Res. 92, 381–392 (2019).
    Google Scholar 
    Kontogianni, A., Tsitsoni, T. & Goudelis, G. An index based on silvicultural knowledge for tree stability assessment and improved ecological function in urban ecosystems. Ecol. Eng. 37, 914–919 (2011).Article 

    Google Scholar 
    He, D. & Li, Z. Wind tunnel test on wind- induced responses of roadside trees. J. Nat. Disasters 28, 44–53 (2019).
    Google Scholar 
    Cao, J., Tamura, Y. & Yoshida, A. Wind tunnel study on aerodynamic characteristics of shrubby specimens of three tree species. Urban For. Urban Green. 11, 465–476 (2012).Article 

    Google Scholar 
    Zhang, W., Kang, L., Zhang, Q., Li, C. & Zou, X. Speed upwind and downwind of a single plant. J. Beijing Norm. Univ. Nat. Sci. 56, 573–581 (2020).
    Google Scholar 
    Cheng, H. et al. Wind tunnel study of airflow recovery on the lee side of single plants. Agric. For. Meteorol. 263, 362–372 (2018).Article 
    ADS 

    Google Scholar 
    Ma, S. et al. Experimental research of viscous flow around a Nitraria tangutorum boscage. Res. Soil Water Conserv. 13, 147–149 (2006).ADS 

    Google Scholar 
    Rahman, M. et al. Disentangling the role of competition, light interception, and functional traits in tree growth rate variation in South Asian tropical moist forests. For. Ecol. Manag. 483, 118908 (2021).Article 

    Google Scholar 
    Forrester, D. I., Benneter, A., Bouriaud, O. & Bauhus, J. Diversity and competition influence tree allometric relationships—Developing functions for mixed-species forests. J. Ecol. 105, 761–774 (2017).Article 

    Google Scholar 
    Coombes, A., Martin, J. & Slater, D. Defining the allometry of stem and crown diameter of urban trees. Urban For. Urban Green. 44, 1–15 (2019).Article 

    Google Scholar 
    Stoffel, M. Mechanical stability and growth performance of trees. (2009).Lento, M., Thijs, D., Jonas, A., Dominique, D. & Jan, C. Comparative study of flow field and drag coefficient of model and small natural trees in a wind tunnel. Urban For. Urban Green. 35, 230–239 (2018).Article 

    Google Scholar 
    West, G. B., Brown, J. H. & Enquist, B. J. A general model for the structure and allometry of plant vascular systems. Nature 400, 664–667 (1999).Article 
    ADS 
    CAS 

    Google Scholar 
    Enquist, B. J. Cope’s rule and the evolution of long-distance transport in vascular plants: Allometric scaling, biomass partitioning and optimization. Plant Cell Environ. 26, 151–161 (2003).Article 

    Google Scholar 
    Bentley, L. P. et al. An empirical assessment of tree branching networks and implications for plant allometric scaling models. Ecol. Lett. 16, 1069–1078 (2013).Article 

    Google Scholar 
    Eloy, C., Fournier, M., Lacointe, A. & Moulia, B. Wind loads and competition for light sculpt trees into self-similar structures. Nat. Commun. 8, 1–11 (2017).Article 
    CAS 

    Google Scholar 
    Lin, M. Y. & Khlystov, A. Investigation of ultrafine particle deposition to vegetation branches in a wind tunnel. Aerosol Sci. Technol. 46, 465–472 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Ji, W. & Zhao, B. A wind tunnel study on the effect of trees on PM2.5 distribution around buildings. J. Hazard. Mater. 346, 36–41 (2018).Article 
    CAS 

    Google Scholar 
    Hui, K. K. W. et al. Unveiling falling urban trees before and during Typhoon Higos (2020): Empirical case study of potential structural failure using tilt sensor. Forests 13, 359 (2022).Article 

    Google Scholar 
    Liao, S., Huang, M., Lou, W., Lin, W. & Xiao, Z. Numerical simulation of a urban wind field under the influence of typhoon “Mangkhut”. Acta Aerodyn. Sin. 39, 107–116 (2021).
    Google Scholar 
    Gardiner, B., Berry, P. & Moulia, B. Review: Wind impacts on plant growth, mechanics and damage. Plant Sci. 245, 94–118 (2016).Article 
    CAS 

    Google Scholar 
    Lüttge, U. & Buckeridge, M. Trees: structure and function and the challenges of urbanization. Trees Struct. Funct. https://doi.org/10.1007/s00468-020-01964-1 (2020).Article 

    Google Scholar 
    Hwang, H. M., Fiala, M. J., Park, D. & Wade, T. L. Review of pollutants in urban road dust and stormwater runoff: part 1. Heavy metals released from vehicles. Int. J. Urban Sci. 20, 334–360 (2016).Article 

    Google Scholar 
    Kuitert, W. The nature of urban Seoul: Potential vegetation derived from the soil map. Int. J. Urban Sci. 17, 95–108 (2013).Article 

    Google Scholar 
    Stubbs, C. J., Cook, D. D. & Niklas, K. J. A general review of the biomechanics of root anchorage. J. Exp. Bot. 70, 3439–3451 (2019).Article 
    CAS 

    Google Scholar 
    Sterck, F. J. & Bongers, F. Ontogenetic changes in size, allometry, and mechanical design of tropical rain forest trees. Am. J. Bot. 85, 266–272 (1998).Article 
    CAS 

    Google Scholar 
    Sim, V. & Jung, W. Wind fragility for urban street tree in Korea. J. Wetl. Res. 21, 298–304 (2019).
    Google Scholar 
    Ueda, M. & Shibata, E. Why do trees decline or dieback after a strong wind? Water status of Hinoki cypress standing after a typhoon. Tree Physiol. 24, 701–706 (2004).Article 

    Google Scholar 
    Jalkanen, A. & Mattila, U. Logistic regression models for wind and snow damage in northern Finland based on the National Forest Inventory data. For. Ecol. Manag. 135, 315–330 (2000).Article 

    Google Scholar 
    Hale, S. E., Gardiner, B. A., Wellpott, A., Nicoll, B. C. & Achim, A. Wind loading of trees: Influence of tree size and competition. Eur. J. For. Res. 131, 203–217 (2012).Article 

    Google Scholar 
    Olson, M. E., Aguirre-Hernández, R. & Rosell, J. A. Universal foliage-stem scaling across environments and species in dicot trees: Plasticity, biomechanics and Corner’s Rules. Ecol. Lett. 12, 210–219 (2009).Article 

    Google Scholar 
    Blanchard, E. et al. Contrasted allometries between stem diameter, crown area, and tree height in five tropical biogeographic areas. Trees Struct. Funct. 30, 1953–1968 (2016).Article 

    Google Scholar 
    King, D. A., Davies, S. J., Tan, S. & Noor, N. S. M. The role of wood density and stem support costs in the growth and mortality of tropical trees. J. Ecol. 94, 670–680 (2006).Article 

    Google Scholar 
    Petty, J. A. & Worrell, R. Stability of coniferous tree stems in relation to damage by snow. Forestry 54, 115–128 (1981).Article 

    Google Scholar 
    Gilman, E. F. & Lilly, S. Tree pruning (International Society of Arboriculture, 2002).
    Google Scholar 
    Zhang, J., Gou, Z., Zhang, F. & Shutter, L. A study of tree crown characteristics and their cooling effects in a subtropical city of Australia. Ecol. Eng. 158, 106027 (2020).Article 

    Google Scholar 
    Marchi, L. et al. State of the art on the use of trees as supports and anchors in forest operations. Forests 9, 467 (2018).Article 

    Google Scholar  More

  • in

    Re-examining extreme carbon isotope fractionation in the coccolithophore Ochrosphaera neapolitana

    Laboratory cultureOchrosphaera neapolitana (RCC1357) was precultured in K/2 medium without Tris buffer8 using artificial seawater (ASW) supplemented with NaHCO3 and HCl to yield an initial DIC of 2050 µM. In triplicate, 1-L bottles were filled with 150 mL of seawater medium with air in the bottle headspace and inoculated with a mid-log phase preculture at an initial cell concentration of 104 cells mL−1. Cultures were grown at 18 °C under a warm white LED light at 100 ± 20 µE on a 16h-light/8h-dark cycle. Bottles were orbitally shaken at 60 rpm to keep cells in suspension. Cell growth was monitored with a Multisizer 4e particle counter and sizer (Beckman Coulter). At ~1.4 × 105 cells mL−1, cells were diluted up to 300 mL to 2–3 × 104 cells mL−1 and harvested after 2 days of more exponential growth up to 7.9 ± 0.6 × 104 cells mL−1. More detailed culture results are listed in the Supplementary Note 1.Immediately after harvesting, pH was measured using a pH probe calibrated with Mettler Toledo NBS standards (it should be noted here that high ionic strength calibration standards would be optimal for pH measurement of liquids like seawater). There was a carbonate system shift during the batch culture and more details are shown in Supplementary Fig. S1. Cells in 50 mL were pelleted by centrifuging at ~1650 × g for 5 min. Seawater supernatant was analyzed for DIC and δ13CDIC by injecting 3.5 mL into an Apollo analyzer and injecting 1 mL into He-flushed glass vials containing H3PO4 for the Gas Bench.For seawater DIC, an Apollo SciTech DIC-C13 Analyzer coupled to a Picarro CO2 analyzer was calibrated with in-house NaHCO3 standards dissolved in deionized water at different known concentrations and δ13C values from −4.66 to −7.94‰. δ13CDIC in media were measured with a Gas Bench II with an autosampler (CTC Analytics AG, Switzerland) coupled to ConFlow IV Interface and a Delta V Plus mass spectrometer (Thermo Fischer Scientific). Pelleted cells were snap-frozen with N2 (l) and stored at −80 °C. For PIC analysis, pellet was resuspended in 1 mL methanol and vortexed. After centrifugation, the methanol phase with extracted organics was removed and the pellet containing the coccoliths was dried at 60 °C overnight. About 300 mg of dried coccolith powder were placed in air-tight glass vials, flushed with He and reacted with five drops of phosphoric acid at 70 °C. PIC δ13C and δ18O were measured by the same Gas Bench system. The system and abovementioned in-house standards were calibrated using international standards NBS 18 (δ13C = −5.01‰, δ18O = +23.00‰) and NBS 19 (δ13C = +1.95‰, δ18O = +2.2‰). The analytical error for DIC concentration and δ13C is More