More stories

  • in

    Minimal climate change impacts on the geographic distribution of Nepeta glomerulosa, medicinal species endemic to southwestern and central Asia

    Mahmoodi, S. et al. The current and future potential geographical distribution of Nepeta crispa Willd., an endemic, rare and threatened aromatic plant of Iran: Implications for ecological conservation and restoration. Ecol. Indic. 137, 108752 (2022).
    Google Scholar 
    Behroozian, M., Ejtehadi, H., Peterson, A. T., Memariani, F. & Mesdaghi, M. Climate change influences on the potential distribution of Dianthus polylepis Bien. ex Boiss.(Caryophyllaceae), an endemic species in the Irano-Turanian region. PLoS ONE 15, e0237527 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Khanal, S. et al. Potential impact of climate change on the distribution and conservation status of Pterocarpus marsupium, a Near Threatened South Asian medicinal tree species. Ecol. Inform. 70, 101722 (2022).
    Google Scholar 
    Dyderski, M. K., Paź, S., Frelich, L. E. & Jagodziński, A. M. How much does climate change threaten European forest tree species distributions?. Glob. Change Biol. 24, 1150–1163 (2018).ADS 

    Google Scholar 
    Sanjerehei, M. M. & Rundel, P. W. The impact of climate change on habitat suitability for Artemisia sieberi and Artemisia aucheri (Asteraceae)—A modeling approach. Pol. J. Ecol. 65, 97–109 (2017).
    Google Scholar 
    Erfanian, M. B., Sagharyan, M., Memariani, F. & Ejtehadi, H. Predicting range shifts of three endangered endemic plants of the Khorassan-Kopet Dagh floristic province under global change. Sci. Rep. 11, 1–13 (2021).
    Google Scholar 
    Zhang, J. M. et al. Effects of climate change on the distribution of wild Akebia trifoliata. Ecol. Evol. 12, e8714 (2022).PubMed 
    PubMed Central 

    Google Scholar 
    Li, J., Fan, G. & He, Y. Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Sci. Total Environ. 698, 134141 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Yang, X.-Q., Kushwaha, S., Saran, S., Xu, J. & Roy, P. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol. Eng. 51, 83–87 (2013).CAS 

    Google Scholar 
    Greiser, C., Hylander, K., Meineri, E., Luoto, M. & Ehrlén, J. Climate limitation at the cold edge: Contrasting perspectives from species distribution modelling and a transplant experiment. Ecography 43, 637–647 (2020).
    Google Scholar 
    Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).PubMed 

    Google Scholar 
    Thuiller, W. et al. Predicting global change impacts on plant species’ distributions: Future challenges. Plant Ecol. Evol. Syst. 9, 137–152 (2008).
    Google Scholar 
    Menke, S., Holway, D., Fisher, R. & Jetz, W. Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder. Glob. Ecol. Biogeogr. 18, 50–63 (2009).
    Google Scholar 
    Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).PubMed 

    Google Scholar 
    Celenk, S., Dirmenci, T., Malyer, H. & Bicakci, A. A palynological study of the genus Nepeta L.(Lamiaceae). Plant Syst. Evol. 276, 105–123 (2008).
    Google Scholar 
    Zargari, A. Medicinal Plants Vol. 2 (University of Tehran Pub, 1990).
    Google Scholar 
    Javidnia, K., Miri, R., Rezazadeh, S. R., Soltani, M. & Khosravi, A. R. Essential oil composition of two subspecies of Nepeta glomerulosa Boiss. from Iran. Nat. Prod. Commun. 3, 1934578X0800300530 (2008).
    Google Scholar 
    Jamzad, Z. Flora of Iran, no 76, Lamiaceae. Res. Inst. For. Rangel. Tehran 76, 542–544 (2012).
    Google Scholar 
    Talebi, S. M., Nohooji, M. G., Yarmohammadi, M., Azizi, N. & Matsyura, A. Trichomes morphology and density analysis in some Nepeta species of Iran. Mediterr. Bot. 39, 51–62 (2018).
    Google Scholar 
    Amirmohammadi, F., Azizi, M., Nemati, S. H., Memariani, F. & Murphy, R. Nutlet micro‐morphology of selected species of Nepeta (Lamiaceae) in Iran. Nord. J. Bot. (2019).Jamzad, Z., Chase, M. W., Ingrouille, M., Simmonds, M. S. & Jalili, A. Phylogenetic relationships in Nepeta L.(Lamiaceae) and related genera based on ITS sequence data. Taxon 52, 21–32 (2003).
    Google Scholar 
    Emami, S. A., Yazdian, R., Arab, A., Sadeghi, M. & Tayarani-Najaran, Z. Anti-melanogenic activity of different extracts from aerial parts of Nepeta glomeruloasin on murine melanoma B16F10 cells. Iran. J. Pharm. Sci. 13, 61–74 (2017).
    Google Scholar 
    Narimani, R., Moghaddam, M., Ghasemi Pirbalouti, A. & Mojarab, S. Essential oil composition of seven populations belonging to two Nepeta species from Northwestern Iran. Int. J. Food Prop. 20, 2272–2279 (2017).CAS 

    Google Scholar 
    Hosseini, A., Forouzanfar, F. & Rakhshandeh, H. Hypnotic effect of Nepeta glomerulosa on pentobarbital-induced sleep in mice. Jundishapur J. Nat. Pharm. Prod. https://doi.org/10.17795/jjnpp-25063 (2016).Article 

    Google Scholar 
    Layeghhaghighi, M., Hassanpour Asil, M., Abbaszadeh, B., Sefidkon, F. & Matinizadeh, M. Investigation of altitude on morphological traits and essential oil composition of Nepeta pogonosperma Jamzad and Assadi from Alamut region. J. Med. Plants Prod. 6, 35–40 (2017).
    Google Scholar 
    Sefidkon, F. Essential oil of Nepeta glomerulosa Boiss. from Iran. J. Essent. Oil Res. 13, 422–423 (2001).CAS 

    Google Scholar 
    Djamali, M. et al. Application of the global bioclimatic classification to Iran: Implications for understanding the modern vegetation and biogeography. Ecol. Mediterr. 37, 91–114 (2011).
    Google Scholar 
    Djamali, M., Brewer, S., Breckle, S. W. & Jackson, S. T. Climatic determinism in phytogeographic regionalization: a test from the Irano-Turanian region, SW and Central Asia. Flora Morphol. Distrib. Funct. Ecol. Plants 207, 237–249 (2012).
    Google Scholar 
    Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
    Google Scholar 
    Escobar, L. E., Lira-Noriega, A., Medina-Vogel, G. & Peterson, A. T. Potential for spread of the white-nose fungus (Pseudogymnoascus destructans) in the Americas: Use of Maxent and NicheA to assure strict model transference. Geospat. Health 9, 221–229 (2014).PubMed 

    Google Scholar 
    Valencia-Rodríguez, D., Jiménez-Segura, L., Rogéliz, C. A. & Parra, J. L. Ecological niche modeling as an effective tool to predict the distribution of freshwater organisms: The case of the Sabaleta Brycon henni (Eigenmann, 1913). PLoS ONE 16, e0247876 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Merow, C., Smith, M. J. & Silander, J. A. Jr. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
    Google Scholar 
    Peterson, A. T., Cobos, M. E. & Jiménez-García, D. Major challenges for correlational ecological niche model projections to future climate conditions. Ann. N. Y. Acad. Sci. 1429, 66–77 (2018).ADS 
    PubMed 

    Google Scholar 
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
    Google Scholar 
    Raghavan, R. K., Peterson, A. T., Cobos, M. E., Ganta, R. & Foley, D. Current and future distribution of the lone star tick, Amblyomma americanum (L.)(Acari: Ixodidae) in North America. PLoS ONE 14, e0209082 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muscarella, R. et al. ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).
    Google Scholar 
    Ramírez Villegas, J. & Jarvis, A. Downscaling global circulation model outputs: The delta method decision and policy analysis Working Paper No. 1 (2010).Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6, 337–348 (2016).PubMed 

    Google Scholar 
    Austin, M. Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecol. Model. 200, 1–19 (2007).
    Google Scholar 
    Rahmanian, S., Pouyan, S., Karami, S. & Pourghasemi, H. R. In Computers in Earth and Environmental Sciences 245–254 (Elsevier, 2022).Rahmanian, S., Pourghasemi, H. R., Pouyan, S. & Karami, S. Habitat potential modelling and mapping of Teucrium polium using machine learning techniques. Environ. Monit. Assess. 193, 1–21 (2021).
    Google Scholar 
    Domroes, M., Kaviani, M. & Schaefer, D. An analysis of regional and intra-annual precipitation variability over Iran using multivariate statistical methods. Theor. Appl. Climatol. 61, 151–159 (1998).ADS 

    Google Scholar 
    Prevéy, J. et al. Greater temperature sensitivity of plant phenology at colder sites: Implications for convergence across northern latitudes. Glob. Change Biol. 23, 2660–2671 (2017).ADS 

    Google Scholar 
    Rousta, I. et al. Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan. Remote Sens. 12, 2433 (2020).ADS 

    Google Scholar 
    Wang, Y. et al. Contrasting effects of temperature and precipitation on vegetation greenness along elevation gradients of the Tibetan Plateau. Remote Sens. 12, 2751 (2020).ADS 

    Google Scholar 
    Zhang, Y. et al. Vegetation change and its relationship with climate factors and elevation on the Tibetan plateau. Int. J. Environ. Res. Public Health 16, 4709 (2019).PubMed Central 

    Google Scholar 
    Vanneste, T. et al. Impact of climate change on alpine vegetation of mountain summits in Norway. Ecol. Res. 32, 579–593 (2017).
    Google Scholar 
    Rodriguez, C., Navarro, T. & El-Keblawy, A. Covariation in diaspore mass and dispersal patterns in three Mediterranean coastal dunes in southern Spain. Turk. J. Bot. 41, 161–170 (2017).
    Google Scholar 
    Zona, S. Fruit and seed dispersal of Salvia L.(Lamiaceae): A review of the evidence. Bot. Rev. 83, 195–212 (2017).
    Google Scholar 
    Ryding, O. Myxocarpy in the Nepetoideae (Lamiaceae) with notes on myxodiaspory in general. Syst. Geogr. Plants 71, 503–514 (2001).
    Google Scholar 
    Tanaka, K., Ogata, K., Mukai, H., Yamawo, A. & Tokuda, M. Adaptive advantage of myrmecochory in the ant-dispersed herb Lamium amplexicaule (Lamiaceae): Predation avoidance through the deterrence of post-dispersal seed predators. PLoS ONE 10, e0133677 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Ferreira, P. M. et al. Long-term ecological research in southern Brazil grasslands: Effects of grazing exclusion and deferred grazing on plant and arthropod communities. PLoS ONE 15, e0227706 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    The diversification of species in crop rotation increases the profitability of grain production systems

    ProductivityWith regard to productivity, in the summer harvest of the 2016–2017 crop year, in which all grain production systems had soybean in common, there were significant differences among crop rotations with species diversification and the double-cropped corn–soybean rotation; performance was better in AS-II, AS-III, AS-IV, AS-V and AS-VI and worst in AS-I. There was no significant difference in productivity among the crop rotations with species diversification (Table 2).Table 2 Productivity (kg ha−1) of the crop rotation systems for the 2014–2015 to 2019–2020 crop years in Londrina, state of Paraná, Brazil.Full size tableFor the summer harvest of the 2019–2020 crop year, in which all the grain production systems again had soybean in common, significant differences were also observed among the production systems. AS-I and AS-V had the lowest productivities, differing from AS-IV and AS-VI, which had the highest productivities. Conversely, the productivities of AS-II and AS-III did not differ significantly from those of the other evaluated systems (Table 2).In the cycle that ended in crop year 2019–2020, compared to the cycle that ended in crop year 2016–2017, there was a reduction in soybean productivity in all the analyzed grain production systems (Table 2). There was also a decrease in the productivity of corn grown in the summer in the 2015–2016 and 2018–2020 crop years. This decrease in productivity observed between the production cycles may be associated with climatic conditions because from 2014–2015 to 2016–2017, there was a good rainfall distribution and few water deficit peaks, while from 2017–2018 to 2019–2020, the water deficit peaks were more constant, especially in 2018–2019 and 2019–2020 (Fig. 1). Notably, there was a greater influence of the El Niño phenomenon on the first production cycle (2014–2017) and of the La Niña phenomenon on the second (2017–2020)28. In southern Brazil, these phenomena correspond to periods of weaker droughts under El Niño conditions and a higher frequency of severe and moderate droughts under La Niña conditions29. The occurrence of a water deficit may limit plant growth and development, particularly during the flowering and grain filling stages. Systems that employ crop rotation with species diversification are less susceptible to production losses due to water deficits30. The results of this study show that crop rotation systems with species diversification, by providing a longer soil cover time for soil protection, either with live plants or from the input of surface straw, together with the respective increase in the soil water storage capacity, can mitigate productivity losses resulting from periods of drought (Fig. 1, Table 2).Another finding is that soybean has higher productivity when grown in systems with greater species diversification, as was the case for AS-IV and AS-VI (Table 2). In general, grain production systems that employ crop rotation with species diversification produce more than those that are not diversified31,32, especially in atypical growing seasons affected by climatic factors limiting crop development33.AS-I and AS-V showed the lowest soybean productivity at the end of the second crop rotation cycle, in the 2019–2020 crop year (Table 2). AS-I had the lowest soybean productivity at the end of the two crop cycles, i.e., in 2016–2017 and 2019–2020, a result that is directly related to corn–soybean double cropping. In the southern region of Brazil, for example, soybean productivity in crop rotation systems with species diversification is 6.2% higher than that in double-crop systems22. In this sense, the results of this study indicate that production systems with little species diversification have lower soybean productivity than those that employ crop rotation with species diversification.At the end of the second crop rotation cycle, in 2019–2020, AS-II and AS-III also showed good soybean productivity, i.e., 3864 kg ha−1 and 3848 kg ha−1, respectively. AS-III had one of the highest grain yields in the summer crops, which may be associated with the use of cover crops in the previous winter. The use of cover crops in the winter growing seasons results in a number of benefits from permanent soil cover because cover crops can improve chemical, physical and biological soil attributes, favoring the accumulation of biomass and organic carbon in the soil34 and prevent soil erosion35. In addition, cover crops control pests, diseases and weeds36 and contribute to weed37 and nematode38 control.Regarding crop dry matter, AS-III, AS-IV, AS-V and AS-VI (Table 3) deposited the most dry matter in the system; the crop dry matter in these systems was greater than that in AS-I and showed no significant difference in relation to that in AS-II. The lower production of dry matter in AS-I is explained by the lack of corn cultivation in the summer. Corn grown in the summer was the crop that most contributed to the accumulation of dry matter in AS-III, AS-IV and AS-VI, compensating for the low averages obtained with beans in AS-V and AS-VI and with safflower in AS-IV. The higher dry matter inputs in AS-IV and AS-VI are because these are the only systems in which corn was grown in the summer for two consecutive years. The average dry matter contributed by corn grown in the summer is 9.9 Mg ha−1, while that from off-season corn and soybeans is 6.5 Mg ha−1 and 4.35 Mg ha−1, respectively.Table 3 Dry matter (Mg ha−1) of the grain production systems for the 2014–2015 to 2019–2020 crop years in Londrina, state of Paraná, Brazil.Full size tableStudies carried out in the Cerrado, Mato Grosso, showed that the minimum amount of plant dry matter deposited by crop rotation systems needed to obtain a balance of C in the soil in the region is between 11.7 and 13.3 Mg ha−139. Therefore, we can deduce that AS-III, AS-IV, AS-V and AS-VI would enter equilibrium; that is, over time, there will be neither accumulation of nor loss of C from the soil. For AS-I and AS-II, we can conclude that over time, C stocks in the soil will be reduced, causing a loss of soil fertility and, consequently, productivity, as shown in Table 2, where the yield of AS-I was lower than that of the most diversified treatments.The results show that crop diversification in grain production systems with the cultivation of commercial or cover crops in the winter benefited soybean and corn production in the summer. In similar studies, species diversification is reported to have increased summer crop productivity over time; specifically, in the U.S. and Canada, corn productivity increased by an average of 28.1%40, and in Canada, corn yield increased by 9.9% and soybean productivity increased by 11.8%41.Economic analysisThe highest mean annual revenue was found for AS-VI, while the lowest was found for AS-III. Regarding the mean annual cost, AS-VI demanded the greatest investment, while AS-III showed the lowest production cost. The highest mean annual profit was also observed for AS-VI, highlighting that the revenue more than offset the costs. As expected, the lowest mean annual profit was found for AS-I, that is, the corn–soybean double-crop system (Fig. 2).Figure 2(a) Mean annual revenue, (b) mean annual cost and (c) mean annual profit of grain production systems with varied levels of species diversity in Londrina, state of Paraná, Brazil.Full size imageThe higher profitability observed for AS-VI indicates that the practice of crop rotation with species diversification in grain production systems increased the grain productivity and economic gains. In this system, the productivity of the commercial crops was positively impacted, and the crops showed excellent yields compared to those in the production systems with lower species diversification. In addition, the winter crops played a key role in the composition of the revenues, especially wheat and bean. As previously noted, the highest mean annual costs of inputs (US$ 685), agricultural operations (US$ 353) and other costs (US$ 177) were found for this system. Within the inputs, the highest cost was for fertilizers (K2O, P2O5, and N), accounting for approximately 22% of the total cost (US$ 280). The higher cost may be related to higher energy demands because in a grain production system, a greater energy volume represents a greater use of inputs42. However, although the cost was the highest, the system was found to be more capable of converting investments into higher productivity and, consequently, into higher revenue and profit. Other studies conducted in Brazil also found economic benefits in crop rotation systems with species diversification, for example, in areas with a predominance of Caiuá sandstone, a region with low-fertility soils, in which the highest profitability was obtained in diversified systems that adopted the highest number of commercial crops, both in the winter and summer growing seasons21. Similarly, in another study in southern Brazil, higher productivities were obtained for more diversified crop rotation systems23. In a long-term study involving soybean, corn, wheat and tropical forage grasses in southern Brazil, higher profits were also found for more diversified production systems22.AS-II had the second highest mean annual profit; this system is characterized by the cultivation of cereals in the winter. The results show that this grain production system is promising, as the use of winter cereal crops had a positive effect on the productivity of the summer crops, leading to increased revenue and profit from the sale of soybean and corn (Supplementary Table S2). With regard to costs, the items that generated the highest expenses in AS-II were inputs, accounting for an average of 54% of the total cost, followed by agricultural operations, which represented an average of 31% of the total, and other costs, accounting for an average of 15% of the total cost (Supplementary Table S2). Studies conducted in other locations also recommend crop rotation systems with the use of cereals, as in the semiarid Northern Great Plains, Canada, where higher productivity and greater profit were found with these cultivation systems compared to a system without species diversification43.AS-V had the third highest mean annual profit. This system is composed of six different crops, and its profitability results were also relevant. Regarding the revenues obtained in the winter growing seasons, beans stood out, accounting for 21% of the total (Supplementary Table S2). One of the problems with AS-V was the cultivation of buckwheat, which, in addition to having a low market price and generating little revenue, also had a high production cost, negatively impacting the entire production system. Thus, if buckwheat had not been cultivated, AS-V could have achieved higher profitability than that observed. With regard to the costs for AS-V, the cost of inputs represented an average of 53% of the total cost, followed by agricultural operations (on average, 31% of the total cost) and other costs (on average, 15% of the total). The cultivation of legumes such as beans in the winter is beneficial for grain production systems because it can favor increased production and, consequently, the profit obtained with subsequent crops44.AS-III had the fourth highest mean annual profit. Although this system did not have the best profitability, it should not be disregarded. This system is focused on the production of straw in the winter and on the revenue generated by the summer crops. However, although cover crops do not generate income for the producer, they indirectly promote gains in subsequent crops. With the maintenance of soil cover, productivity gains and increased revenue are expected in production systems in the medium and long terms21. Cover crops, in general, control pests, diseases and weeds and improve soil conditions36 because they prevent soil compaction and improve soil water infiltration and retention, density, and hydraulic conductivity45. AS-III also had the lowest mean annual production cost; the cost with inputs was on average 35% lower than that observed in the other systems. The lower costs are because the cover crops were not harvested because their benefits are obtained from the biomass generated; thus, the cost is lower than that for systems for which the purpose is to sell grains. One of the great benefits of adopting this system is that the cultivation of cover crops in the winter can reduce the cost of the crop that follows because the amount of inputs involved in the production of the next crop can decrease, as can fuel expenses46. In addition, the lower demand for pesticides makes the system more economical and sustainable and less risky. The quantification and analysis of the items composing the costs of each system are extremely important for producers’ decision-making. However, this analysis requires extreme caution because higher production costs do not necessarily mean lower yields, and similarly, lower costs do not necessarily mean higher profits20,21.AS-IV had the second lowest mean annual profit. This system included winter agroenergy crops. With the exception of canola, the other agroenergy crops grown in this production system showed low profitability. Despite having one of the lowest production costs, the low revenue obtained with agroenergy crops compromised the profitability of AS-IV. Even with the sale of crambe, safflower and canola, the revenues were not sufficient to cover the production costs. Although this system did not show one of the best results, studies with bioenergy crops are being conducted in various regions of the world, and these crops may become an option for southern Brazil, as in the case of Italy, where plants of the family Brassicaceae are being introduced in rotation with cereals as a source of income diversification47.The lowest mean annual profit was observed for AS-I. The low profit is related to the high production costs. Despite having the second highest mean annual revenue, the high production cost compromised the profitability of the system. This result is associated with the lower grain productivity observed in this production system and the fact that it specialized in few crops and focused only on commodities, which are subject to changes in their sale price due to seasonality and market uncertainties, or with the increased susceptibility of this system to problems caused by climatic variations. The crops grown in this system are traded in the international market, and in this case, the producers are only “price takers,”, i.e., they are not able to influence the price of the products48. The prices of commodities may vary; thus, producers may obtain higher or lower revenue due to market fluctuations or volatility. In turn, market fluctuations or volatility are caused by, among other factors, production or external factors, such as exchange rate variations or increased food consumption49,50. AS-I had the highest mean annual pesticide costs, approximately 21% of the total cost (US$ 254). In addition to economic factors, the double-crop system has also generated problems such as the proliferation of pests, diseases and weeds because, in contrast to crop rotation, it does not interrupt the life cycles of pests and diseases51. To control the proliferation of pests, diseases and weeds, the increased use of inputs and an increase in the number of agricultural operations are required52, with a consequent increase in production costs20. This increase in production costs can be observed for winter corn crops, which were more expensive than summer soybean crops. In this system, the mean cost to produce soybean in the summer was US$ 567 per ha, and that to produce corn in the winter was US$ 648. Compared to the other systems studied, the average investment required for the winter growing season was US$ 448 and that for the growing season was US$ 640; that is, the winter crops required 30% less investment than the summer crops (Supplementary Table S3).When considering the real selling price of grains, the highest accumulated profit was observed in AS-VI (Fig. 3); however, in a scenario in which the price of soybeans fluctuates (Fig. 3a) both upward and downward, sensitivity analysis revealed different behaviors. If there was a 44% increase in the selling price of soybeans, the ranking order of the systems would change, making AS-I more profitable. AS-I is the most sensitive to soybean price variations, since in this system, the crop is mainly responsible for generating income and is cultivated in all summers. Thus, the opposite results are also expected. A negative variation in the selling price of soybeans will make AS-I the system with the highest accumulated loss. Price changes can significantly increase or decrease the profitability of producers. Thus, the choice of crops and the number of times a crop appears in each agricultural system determines the profitability of the system as the sale price of the crops varies.Figure 3Price sensitivity analysis (accumulated profit of 6 crop years on the y-axis) of six agricultural systems in Londrina, state of Paraná, Brazil. (a) Soybean; (b) corn; (c) wheat; and (d) bean.Full size imageCorn showed some changes in the order of classification of the systems (Fig. 3b). If the corn sale prices were increased by up to 50%, AS-VI would continue to be the system with the highest accumulated profit. In this scenario, AS-I, composed solely of the corn crop in winter, would cease to be the system with the lowest accumulated profit, occupying the position of AS-III. Different from what happened with the soybean crop, the fluctuations in the corn sale price had less impact on AS-I in terms of accumulated profit. This was because the corn produced in this system accounted for a smaller share of profits and, in some cases, even resulted in losses.Regarding the wheat crop (Fig. 3c), changes in the sale price led to little change in the accumulated profit. Wheat was grown only in AS-II and AS-VI, and in a scenario that considered only the variation in the price of this grain, if its selling price was reduced by up to 47%, AS-VI would continue to be the system with the highest accumulated profit. Changes in the selling price of the bean crop (Fig. 3d) had greater impacts. A 50% increase in the sale price of beans led to a 47% increase in profit in AS-VI.In addition to variations in sale prices, another possible scenario is that crops are stored and sold at later dates. This is possible, as cooperatives are able to provide producers with storage and future sale of grains, extending the time for decision-making. Thus, producers can market products at an optimal time, e.g., when sale prices are better than those on the day of harvest. In this scenario, if corn and soybeans were stored and sold at peak prices recorded each quarter, over the 12 months following the harvest date, the evaluated agricultural systems would show even greater profits. Figure 4 shows the evolution of real prices in tons (USD) of corn and soybeans from July 2014 to March 2021.Figure 4Evolution of corn and soybean prices from July 2014 to March 2020. Data were obtained from the Department of Rural Economy of the Paraná State Secretariat of Agriculture and Supply (DERAL-SEAB). The monetary values are corrected for inflation according to the Brazilian Extended National Consumer Price Index (IPCA) to December 2021.Full size imageIf the sale of soybean and corn was carried out at times of price peaks, the accumulated profit of the systems would vary (Table 4). AS-I, composed exclusively of corn and soybean crops, would become the highest profit system (US$ 3,683). AS-VI, although no longer the highest profit system, would still be one of the systems with the best economic results (US$ 3479). In this scenario, AS-IV would occupy the last position, with the lowest accumulated profit (US$ 2732).Table 4 Profit (USD ha−1) of the grain production systems for the 2014–2015 to 2019–2020 crop years, considering quarterly price peaks in Londrina, state of Paraná, Brazil. .Full size tableIn this scenario, driven by the devaluation of the real against the dollar, the increase in domestic consumption and exports influenced the supply of grains in the market, and agricultural commodities such as soybeans and corn reached high sale values. Thus, it is evident that the market is able to condition the farmer’s profitability, which can influence the results of the analysis, both positively and negatively, according to the daily variations in grain commercialization prices53.From the results, it is evident that species diversification in crop rotation has enabled an increase in both grain productivity and economic gains. It is not enough to simply adopt no-till practices without species diversification in grain production systems31,32; it is necessary for the systems to be aligned with the no-tillage system and conservation agriculture principles. The main reasons for investing in crop diversification are as follows: production of roots and straw to cover the soil surface; improved soil structure and sustained soil biology; nutrient cycling; breaking the cycles of pests, diseases, and weeds; productivity gains; and increased profitability. Thus, the challenge lies in the diffusion of production systems aligned with the principles of the no-tillage system and conservation agriculture, that is, to diversify without failing to produce and obtain gains from grain production. Information on the benefits of grain production systems that employ crop rotation with species diversification, tested and with demonstrated economicity, such as those presented in this study, can therefore be decisive for producers’ decision-making and the adoption of practices aligned with sustainability in agriculture. More

  • in

    Quantifying thermal cues that initiate mass emigrations in juvenile white sharks

    Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333(6045), 1024–1026. https://doi.org/10.1126/SCIENCE.1206432 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Newton, I. Migration within the annual cycle: Species, sex and age differences. J. Ornithol. 152, 169–185. https://doi.org/10.1007/S10336-011-0689-Y/TABLES/1 (2011).Article 

    Google Scholar 
    Dodson, S., Abrahms, B., Bograd, S. J., Fiechter, J. & Hazen, E. L. Disentangling the biotic and abiotic drivers of emergent migratory behavior using individual-based models. Ecol. Model. 432, 109225. https://doi.org/10.1016/J.ECOLMODEL.2020.109225 (2020).Article 

    Google Scholar 
    Lehikoinen, A. et al. Sex-specific timing of autumn migration in birds: the role of sexual size dimorphism, migration distance and differences in breeding investment. Ornis Fennica 94, 53–65 (2017).
    Google Scholar 
    Stewart, B. S. Ontogeny of differential migration and sexual segregation in northern elephant seals. J. Mammol. 78(4), 1101–1116 (1997).Somveille, M., Rodrigues, A. S. L. & Manica, A. Why do birds migrate? A macroecological perspective. Glob. Ecol. Biogeogr. 24(6), 664–674. https://doi.org/10.1111/geb.12298 (2015).Article 

    Google Scholar 
    Corkeron, P. J. & Connor, R. C. Why do baleen whales migrate?. Mar. Mamm. Sci. 15(4), 1228–1245. https://doi.org/10.1111/J.1748-7692.1999.TB00887.X (1999).Article 

    Google Scholar 
    Mourier, J., Mills, S. C. & Planes, S. Population structure, spatial distribution and life-history traits of blacktip reef sharks Carcharhinus melanopterus. J. Fish Biol. 82(3), 979–993. https://doi.org/10.1111/JFB.12039 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Avgar, T., Mosser, A., Brown, G. S. & Fryxell, J. M. Environmental and individual drivers of animal movement patterns across a wide geographical gradient. J. Anim. Ecol. 82, 96–106. https://doi.org/10.1111/j.1365-2656.2012.02035.x (2013).Article 
    PubMed 

    Google Scholar 
    Crawshaw, L. I. Physiological and behavioral reactions of fishes to temperature change. J. Fish. Res. Board Can. 34(5), 730–734. https://doi.org/10.1139/f77-113 (1977).Article 

    Google Scholar 
    Heithaus, M., Dill, L., Marshall, G. J. & Buhleier, B. Habitat use and foraging behavior of tiger sharks (Galeocerdo cuvier) in a seagrass ecosystem. Mar. Biol. 140, 337–348. https://doi.org/10.1007/s00227-001-0711-7 (2002).Article 

    Google Scholar 
    Magnuson, J. J., Crowder, L. B. & Medvick, P. A. Temperature as an ecological resource. Integr. Comp. Biol. 19(1), 331–343. https://doi.org/10.1093/icb/19.1.331 (1979).Article 

    Google Scholar 
    Matern, S. A., Cech, J. J. & Hopkins, T. E. Diel movements of bat rays, Myliobatis californica, in Tomales Bay, California: Evidence for behavioral thermoregulation?. Environ. Biol. Fishes 58(2), 173–182. https://doi.org/10.1023/A:1007625212099 (2000).Article 

    Google Scholar 
    Speed, C. W., Meekan, M. G., Field, I. C., McMahon, C. R. & Bradshaw, C. J. A. Heat-seeking sharks: Support for behavioural thermoregulation in reef sharks. Mar. Ecol. Prog. Ser. 463, 231–244. https://doi.org/10.3354/meps09864 (2012).Article 
    ADS 

    Google Scholar 
    Dewar, H., Domeier, M. & Nasby-Lucas, N. Insights into young of the year white shark, Carcharodon carcharias, behavior in the Southern California Bight. Environ. Biol. Fishes https://doi.org/10.1023/B:EBFI.0000029343.54027.6a.pdf (2004).Article 

    Google Scholar 
    Hertz, P. E., Huey, R. & Stevenson, R. D. Evaluating temperature regulation by field-active ectotherms. Am. Nat. 142, 796–818 (1993).Article 
    CAS 
    PubMed 

    Google Scholar 
    Heupel, M. R., Simpfendorfer, C. A. & Hueter, R. E. Estimation of shark home ranges using passive monitoring techniques. Environ. Biol. Fishes 71(2), 135–142. https://doi.org/10.1023/b:ebfi.0000045710.18997.f7 (2004).Article 

    Google Scholar 
    Topping, D. T., Lowe, C. G. & Caselle, J. E. Site fidelity and seasonal movement patterns of adult California sheephead Semicossyphus pulcher (Labridae): An acoustic monitoring study. Mar. Ecol. Progr. Ser. 326, 257–267 (2006).Weng, K. C. et al. Movements, behavior and habitat preferences of juvenile white sharks Carcharodon carcharias in the eastern Pacific. Mar. Ecol. Prog. Ser. 338, 211–224. https://doi.org/10.3354/meps338211 (2007).Article 
    ADS 

    Google Scholar 
    Lyons, K. et al. The degree and result of gillnet fishery interactions with juvenile white sharks in southern California assessed by fishery-independent and -dependent methods. Fish. Res. 147, 370–380. https://doi.org/10.1016/J.FISHRES.2013.07.009 (2013).Article 
    ADS 

    Google Scholar 
    Papastamatiou, Y. P. et al. Drivers of daily routines in an ectothermic marine predator: Hunt warm, rest warmer?. PLoS ONE. https://doi.org/10.1371/journal.pone.0127807 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Adolph, S. C. Influence of behavioral thermoregulation on microhabitat use by two sceloporus lizards. Ecology 71(1), 315–327. https://doi.org/10.2307/1940271 (1990).Article 

    Google Scholar 
    Heithaus, M. R. The biology of tiger sharks, Galeocerdo cuvier, in Shark Bay, Western Australia: sex ratio, size distribution, diet, and seasonal changes in catch rates. Environ. Biol. Fishes 61, 25–36 (2001).Article 

    Google Scholar 
    Vaudo, J. J. & Lowe, C. G. Movement patterns of the round stingray Urobatis halleri(Cooper) near a thermal outfall. J. Fish Biol. 68(6), 1756–1766. https://doi.org/10.1111/j.0022-1112.2006.01054.x (2006).Article 

    Google Scholar 
    Vaudo, J. J. & Heithaus, M. R. Microhabitat selection by marine mesoconsumers in a thermally heterogeneous habitat: Behavioral thermoregulation or avoiding predation risk?. PLoS ONE. 8(4), e61907. https://doi.org/10.1371/journal.pone.0061907 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Weng, K. C. et al. Migration and habitat of white sharks (Carcharodon carcharias) in the eastern Pacific Ocean. Mar. Biol. 152(4), 877–894. https://doi.org/10.1007/s00227-007-0739-4 (2007).Article 

    Google Scholar 
    White, C. F. et al. Quantifying habitat selection and variability in habitat suitability for juvenile white sharks. PLoS ONE 14(5), e0214642. https://doi.org/10.1371/journal.pone.0214642 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Curtis, T. H. et al. First insights into the movements of young-of-the-year white sharks (Carcharodon carcharias) in the western North Atlantic Ocean. Sci. Rep. 8(1), 1–8. https://doi.org/10.1038/s41598-018-29180-5 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Bruce, B. D., Harasti, D., Lee, K., Gallen, C. & Bradford, R. Broad-scale movements of juvenile white sharks Carcharodon carcharias in eastern Australia from acoustic and satellite telemetry. Mar. Ecol. Prog. Ser. 619, 1–15. https://doi.org/10.3354/MEPS12969 (2019).Article 
    ADS 

    Google Scholar 
    Carey, F. G. et al. Temperature and activities of a white shark Carcharodon carcharias. Copeia 2, 254–260. https://doi.org/10.2307/1444603 (1982).Article 

    Google Scholar 
    Klimley, A. P., Beavers, S. C., Curtis, T. H. & Jorgensen, S. J. Movements and swimming behavior of three species of sharks in La Jolla Canyon, California. Environ. Biol. Fish. 63, 117–135. https://doi.org/10.1023/A:1014200301213.pdf (2002).Article 

    Google Scholar 
    Towner, A. V., Underhill, L. G., Jewell, O. J. D. & Smale, M. J. Environmental Influences on the abundance and sexual composition of white sharks Carcharodon carcharias in Gansbaai, South Africa. PLoS ONE. 8(8), e71197. https://doi.org/10.1371/journal.pone.0071197 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Anderson, J. M. et al. High-resolution acoustic telemetry reveals swim speeds and inferred field metabolic rates in juvenile white sharks (Carcharodon carcharias). PLoS ONE 17(6), e0268914. https://doi.org/10.1371/JOURNAL.PONE.0268914 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Anderson, J. M. et al. Interannual nearshore habitat use of young of the year white sharks off Southern California. Front. Mar. Sci. 8, 238. https://doi.org/10.3389/fmars.2021.645142 (2021).Article 

    Google Scholar 
    Domeier, M. L. & Nasby-Lucas, N. Two-year migration of adult female white sharks (Carcharodon carcharias) reveals widely separated nursery areas and conservation concerns. Anim. Biotelemet. 1(1), 1–10. https://doi.org/10.1186/2050-3385-1-2/FIGURES/3 (2013).Article 

    Google Scholar 
    Oñate-González, E. C. et al. Importance of Bahia Sebastian Vizcaino as a nursery area for white sharks (Carcharodon carcharias) in the Northeastern Pacific: A fishery dependent analysis. Fish. Res. 188, 125–137. https://doi.org/10.1016/J.FISHRES.2016.12.014 (2017).Article 

    Google Scholar 
    Lowe, C. G. et al. Historic fishery interactions with white sharks in the Southern California Bight. Glob. Perspect. Biol. Life Hist. White Shark 14, 169–190 (2012).
    Google Scholar 
    Anderson, J. M. et al. Non-random Co-occurrence of Juvenile White Sharks (Carcharodon carcharias) at Seasonal Aggregation Sites in Southern California. Front. Mar. Sci. 8, 1–14. https://doi.org/10.3389/fmars.2021.688505 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Benson, J. F. et al. Juvenile survival, competing risks, and spatial variation in mortality risk of a marine apex predator. J. Appl. Ecol. 55, 2888–2897. https://doi.org/10.1111/1365-2664.13158 (2018).Article 

    Google Scholar 
    RStudio Team. RStudio: Integrated Development for R. (RStudio, PBC, 2020) http://www.rstudio.com/.Derrick, T., & Thomas, J. Time Series Analysis: The Cross-Correlation Function. Innovative Analyses of Human Movement, Chapter 7. https://lib.dr.iastate.edu/kin_pubs/46 (2004).Killick, R., Fearnhead, P. & Eckley, I. A. Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107, 1590–1598. https://doi.org/10.1080/01621459.2012.737745 (2012).Article 
    MathSciNet 
    CAS 
    MATH 

    Google Scholar 
    Bakun, A. Coastal Upwelling Indices, West Coast of North America. US Department of Commerce. NOAA Technical Report, NMFS SSRF-671 (1973).Di Lorenzo, E. Seasonal dynamics of the surface circulation in the Southern California Current System. Deep-Sea Res. Part II 50(14–16), 2371–2388. https://doi.org/10.1016/S0967-0645(03)00125-5 (2003).Article 
    ADS 

    Google Scholar 
    Lynn, R. J. & Simpson, J. J. The California Current System: The seasonal variability of its physical characteristics. J. Geophys. Res. 92(C12), 12947. https://doi.org/10.1029/jc092ic12p12947 (1987).Article 
    ADS 

    Google Scholar 
    Sinnett, G. & Feddersen, F. The surf zone heat budget: The effect of wave heating. Geophys. Res. Lett. 41(20), 7217–7226. https://doi.org/10.1002/2014GL061398 (2014).Article 
    ADS 

    Google Scholar 
    Wei, X., Li, K.-Y., Kilpatrick, T., Wang, M. & Xie, S.-P. Large-scale conditions for the record-setting Southern California marine heatwave of August 2018. Geophys. Res. Lett. 48(7), e2020GL091803 (2021).Article 
    ADS 

    Google Scholar 
    Freedman, R. M., Brown, J. A., Caldow, C. & Caselle, J. E. Marine protected areas do not prevent marine heatwave-induced fish community structure changes in a temperate transition zone. Sci. Rep. 10(1), 1–8. https://doi.org/10.1038/s41598-020-77885-3 (2020).Article 
    CAS 

    Google Scholar 
    Heupel, M. R., Simpfendorfer, C. A. & Hueter, R. E. Running before the storm: blacktip sharks respond to falling barometric pressure associated with Tropical Storm Gabrielle. J. Fish Biol. 63(5), 1357–1363. https://doi.org/10.1046/J.1095-8649.2003.00250.X (2003).Article 

    Google Scholar 
    Guttridge, T. L. et al. Deep danger: Intra-specific predation risk influences habitat use and aggregation formation of juvenile lemon sharks Negaprion brevirostris. Mar. Ecol. Progr. Ser. 445, 279–291 (2012).Article 
    ADS 

    Google Scholar 
    Grainger, R. et al. Diet composition and nutritional niche breadth variability in juvenile white sharks (Carcharodon carcharias). Front. Mar. Sci. 7, 422 (2020).Article 

    Google Scholar 
    Hussey, N. E., Christiansen, H. M. & Dudley, S. F. J. Size-based analysis of diet and trophic position of the white shark, carcharodon carcharias, in South African waters. Glob. Perspect. Biol. Life Hist. White Shark 3, 27–49. https://doi.org/10.1201/b11532-5 (2012).Article 

    Google Scholar 
    Kim, S. L., Tinker, M. T., Estes, J. A. & Koch, P. L. Ontogenetic and among-individual variation in foraging strategies of northeast Pacific white sharks based on stable isotope analysis. PLoS ONE 7(9), e45068. https://doi.org/10.1371/JOURNAL.PONE.0045068 (2012).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tinker, M. T. et al. Dramatic increase in sea otter mortality from white sharks in California. Mar. Mamm. Sci. 32(1), 309–326. https://doi.org/10.1111/mms.12261 (2015).Article 

    Google Scholar  More

  • in

    Defensive functions and potential ecological conflicts of floral stickiness

    Gorb, E. V. & Gorb, S. N. Anti-adhesive effects of plant wax coverage on insect attachment. J. Exp. Bot. 68, 5323–5337 (2017).CAS 
    PubMed 

    Google Scholar 
    Agrawal, A. A. & Konno, K. Latex: A model for understanding mechanisms, ecology, and evolution of plant defense against herbivory. Annu. Rev. Ecol. Evol. Syst. 40, 311–331 (2009).
    Google Scholar 
    Langenheim, J. H. Plant resins. Am. Sci. 78, 16–24 (1990).
    Google Scholar 
    Ben-Mahmoud, S. et al. Acylsugar amount and fatty acid profile differentially suppress oviposition by western flower thrips, Frankliniella occidentalis, on tomato and interspecific hybrid flowers. PLoS ONE 13, 1–20 (2018).
    Google Scholar 
    LoPresti, E. F., Pearse, I. S. & Charles, G. K. The siren song of a sticky plant: Columbines provision mutualist arthropods by attracting and killing passerby insects. Ecology 96, 2862–2869 (2015).CAS 
    PubMed 

    Google Scholar 
    Weinhold, A. & Baldwin, I. T. Trichome-derived O-acyl sugars are a first meal for caterpillars that tags them for predation. Proc. Natl. Acad. Sci. 108, 7855–7859 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Krimmel, B. A. & Wheeler, A. G. Host-plant stickiness disrupts novel ant–mealybug association. Arthropod. Plant. Interact. 9, 187–195 (2015).
    Google Scholar 
    Simmons, A. T., Gurr, G. M., McGrath, D., Martin, P. M. & Nicol, H. I. Entrapment of Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) on glandular trichomes of Lycopersicon species. Aust. J. Entomol. 43, 196–200 (2004).
    Google Scholar 
    Carter, C. D., Gianfagna, T. J. & Sacalis, J. N. Sesquiterpenes in glandular trichomes of a wild tomato species and toxicity to the colorado potato beetle. J. Agric. Food Chem. 37, 1425–1428 (1989).CAS 

    Google Scholar 
    Van Dam, N. M. & Hare, J. D. Biological activity of Datura wrightii glandular trichome exudate against Manduca sexta larvae. J. Chem. Ecol. 24, 1529–1549 (1998).
    Google Scholar 
    Kessler, A. & Heil, M. The multiple faces of indirect defences and their agents of natural selection. Funct. Ecol. 25, 348–357 (2011).
    Google Scholar 
    Karban, R., LoPresti, E., Pepi, A. & Grof-Tisza, P. Induction of the sticky plant defense syndrome in wild tobacco. Ecology 100, 1–9 (2019).
    Google Scholar 
    Krimmel, B. A. & Pearse, I. S. Sticky plant traps insects to enhance indirect defence. Ecol. Lett. 16, 219–224 (2013).CAS 
    PubMed 

    Google Scholar 
    Eisner, T. & Aneshansley, D. J. Adhesive strength of the insect-trapping glue of a plant (Befaria racemosa). Ann. Entomol. Soc. Am. 76, 295–298 (1983).
    Google Scholar 
    Spomer, G. G. Evidence of protocarnivorous capabilities in Geranium viscosissimum and Potentilla arguta and other sticky plants. Int. J. Plant Sci. 160, 98–101 (1999).
    Google Scholar 
    Darnowski, D. W., Carroll, D. M., Płachno, B., Kabanoff, E. & Cinnamon, E. Evidence of protocarnivory in triggerplants (Stylidium spp.; Stylidiaceae). Plant Biol. 8, 805–812 (2006).CAS 
    PubMed 

    Google Scholar 
    Givnish, T. J., Burkhardt, E. L., Happel, R. E. & Weintraub, J. D. Carnivory in the bromeliad Brocchinia reducta, with a cost/benefit model for the general restriction of carnivorous plants to sunny, moist nutrient-poor habitats. Am. Nat. 124, 479–497 (1984).
    Google Scholar 
    Jürgens, N. Psammophorous plants and other adaptations to desert ecosystems with high incidence of sandstorms. Feddes Repert. 107, 345–359 (1996).
    Google Scholar 
    Lopresti, E. F. & Karban, R. Chewing sandpaper: Grit, plant apparency, and plant defense in sand-entrapping plants. Ecology 97, 826–833 (2016).PubMed 

    Google Scholar 
    Krupnick, G. A. & Weis, A. E. The effect of floral herbivory on male and female reproductive success in Isomeris arborea. Ecology 80, 135–149 (1999).
    Google Scholar 
    McCall, A. C. Florivory affects pollinator visitation and female fitness in Nemophila menziesii. Oecologia 155, 729–737 (2008).ADS 
    PubMed 

    Google Scholar 
    Bandeili, B. & Müller, C. Folivory versus florivory-adaptiveness of flower feeding. Naturwissenschaften 97, 79–88 (2010).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Lai, D. et al. Lotus japonicus flowers are defended by a cyanogenic β-glucosidase with highly restricted expression to essential reproductive organs. Plant Mol. Biol. 89, 21–34 (2015).CAS 
    PubMed 

    Google Scholar 
    Kessler, A. & Halitschke, R. Testing the potential for conflicting selection on floral chemical traits by pollinators and herbivores: Predictions and case study. Funct. Ecol. 23, 901–912 (2009).
    Google Scholar 
    Kessler, D., Diezel, C., Clark, D. G., Colquhoun, T. A. & Baldwin, I. T. Petunia flowers solve the defence/apparency dilemma of pollinator attraction by deploying complex floral blends. Ecol. Lett. 16, 299–306 (2013).PubMed 

    Google Scholar 
    Li, J. et al. Defense of pyrethrum flowers: Repelling herbivores and recruiting carnivores by producing aphid alarm pheromone. New Phytol. 223, 1607–1620 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kennedy, G. G. Tomato, pests, parasitoids, and predators: tritrophic interactions involving the genus Lycopersicon. Annu. Rev. Entomol. 48, 51–72 (2003).CAS 
    PubMed 

    Google Scholar 
    McCarren, S., Coetzee, A. & Midgley, J. Corolla stickiness prevents nectar robbing in Erica. J. Plant Res. https://doi.org/10.1007/s10265-021-01299-z (2021).Article 
    PubMed 

    Google Scholar 
    Matulevich Peláez, J. A., Gil Archila, E. & Ospina Giraldo, L. F. Estudio fitoquímico de hojas, flores y frutos de Bejaria resinosa mutis ex linné filius (ericaceae) y evaluación de su actividad antiinflamatoria. Rev. Cuba. Plantas Med. 21, 332–345 (2016).
    Google Scholar 
    Kraemer, M. On the pollination of Bejaria resinosa Mutis ex Linne f. ( Ericaceae ), an ornithophilous Andean paramo shrub. Flora 196, 59–62 (2001).
    Google Scholar 
    Melampy, A. M. N. Flowering phenology, pollen flow and fruit production in the Andean Shrub Befaria resinosa. Oecologia 73, 293–300 (1987).ADS 
    CAS 
    PubMed 

    Google Scholar 
    LoPresti, E. F., Robinson, M. L., Krimmel, B. A. & Charles, G. K. The sticky fruit of manzanita: potential functions beyond epizoochory. Ecology 99, 2128–2130 (2018).PubMed 

    Google Scholar 
    Kessler, A. & Chautá, A. The ecological consequences of herbivore-induced plant responses on plant-pollinator interactions. Emerg. Topics Life Sci. 4, 33–43 (2020).
    Google Scholar 
    Lucas-Barbosa, D. Integrating studies on plant-pollinator and plant-herbivore interactions. Trends Plant Sci. 21, 125–133 (2016).CAS 
    PubMed 

    Google Scholar 
    Leckie, B. M. et al. Differential and synergistic functionality of acylsugars in suppressing oviposition by insect herbivores. PLoS ONE 11, 1–19 (2016).
    Google Scholar 
    Monteiro, R. F. & Macedo, M. V. First report on the diversity of insects trapped by a sticky exudate of the inflorescences of Vriesea bituminosa Wawra (Bromeliaceae: Tillandsioideae). Arthropod. Plant. Interact. 8, 519–523 (2014).
    Google Scholar 
    Chatzivasileiadis, E. A. & Sabelis, M. W. Toxicity of methyl ketones from tomato trichomes to Tetranychus urticae Koch. Exp. Appl. Acarol. 21, 473–484 (1997).CAS 

    Google Scholar 
    Avé, D. A., Gregory, P. & Tingey, W. M. Aphid repellent sesquiterpenes in glandular trichomes of Solanum berthaultii and S. tuberosum. Entomol. Exp. Appl. 44, 131–138 (1987).
    Google Scholar 
    LoPresti, E. Columbine pollination success not determined by a proteinaceous reward to hummingbird pollinators. J. Pollinat. Ecol. 20, 35–39 (2017).
    Google Scholar 
    Krimmel, B. A. & Pearse, I. S. Generalist and sticky plant specialist predators suppress herbivores on a sticky plant. Arthropod. Plant. Interact. 8, 403–410 (2014).
    Google Scholar 
    Adlassnig, W., Lendl, T., Peroutka, M. & Lang, I. Deadly glue- Adhesive traps of carnivorous plants. in Biological Adhesive Systems (eds. von Byren, J. & Grunwald, I.) 15–28 (2010).Ellison, A. M. & Gotelli, N. J. Evolutionary ecology of carnivorous plants. Trends Ecol. Evol. 16, 623–629 (2001).
    Google Scholar 
    Maloof, J. E. & Inouye, D. W. Are nectar robbers cheaters or mutualists?. Ecology 81, 2651–2661 (2000).
    Google Scholar 
    Asai, T., Hirayama, Y. & Fujimoto, Y. Epi-α-bisabolol 6-deoxy-β-d-gulopyranoside from the glandular trichome exudate of Brillantaisia owariensis. Phytochem. Lett. 5, 376–378 (2012).CAS 

    Google Scholar 
    Asai, T., Hara, N. & Fujimoto, Y. Fatty acid derivatives and dammarane triterpenes from the glandular trichome exudates of Ibicella lutea and Proboscidea louisiana. Phytochemistry 71, 877–894 (2010).CAS 
    PubMed 

    Google Scholar 
    Ohkawa, A., Sakai, T., Ohyama, K. & Fujimoto, Y. Malonylated glycerolipids from the glandular trichome exudate of Ceratotheca triloba. Chem. Biodivers. 9, 1611–1617 (2012).CAS 
    PubMed 

    Google Scholar 
    Omosa, L. K. et al. Antimicrobial flavonoids and diterpenoids from Dodonaea angustifolia. S. Afr. J. Bot. 91, 58–62 (2014).CAS 

    Google Scholar 
    Kessler, A. The information landscape of plant constitutive and induced secondary metabolite production. Curr. Opin. Insect Sci. 8, 47–53 (2015).PubMed 

    Google Scholar 
    Knudsen, J. T., Tollsten, L., Groth, I., Bergström, G. & Raguso, R. A. Trends in floral scent chemistry in pollination syndromes: Floral scent composition in hummingbird-pollinated taxa. Bot. J. Linn. Soc. 146, 191–199 (2004).
    Google Scholar 
    Pearse, I. S., Gee, W. S. & Beck, J. J. Headspace volatiles from 52 oak species advertise induction, species identity, and evolution, but not defense. J. Chem. Ecol. 39, 90–100 (2013).CAS 
    PubMed 

    Google Scholar 
    El-Sayed, A. M., Byers, J. A. & Suckling, D. M. Pollinator-prey conflicts in carnivorous plants: When flower and trap properties mean life or death. Sci. Rep. 6, 1–11 (2016).
    Google Scholar 
    Greenaway, W., May, J. & Whatley, F. R. Analysis of phenolics of bud exudate of Populus tristis by GC/MS. Zeitschrift fur Naturforsch.. Sect C J. Biosci. 47, 512–515 (1992).
    Google Scholar 
    Urzua, A. & Cuadra, P. Acylated flavonoid aglycones from Gnaphalium robustum. Phytochem. Divers. Redundancy Ecol. Interact. 29, 1342–1343 (1990).CAS 

    Google Scholar 
    Drewes, S. E., Mudau, K. E., Van Vuuren, S. F. & Viljoen, A. M. Antimicrobial monomeric and dimeric diterpenes from the leaves of Helichrysum tenax var tenax. Phytochemistry 67, 716–722 (2006).CAS 
    PubMed 

    Google Scholar 
    Midiwo, J. O. et al. Bioactive compounds from some Kenyan ethnomedicinal plants: Myrsinaceae, Polygonaceae and Psiadia punctulata. Phytochem. Rev. 1, 311–323 (2002).CAS 

    Google Scholar 
    Jiménez-Pomárico, A. et al. Chemical and morpho-functional aspects of the interaction between a Neotropical resin bug and a sticky plant. Rev. Biol. Trop. 67, 454–465 (2019).
    Google Scholar 
    Linhart, Y. B., Thompson, J. D., Url, S. & John, D. Terpene-based selective herbivory by Helix aspersa (Mollusca) on Thymus vulgaris (Labiatae). Oecologia 102, 126–132 (2012).
    Google Scholar 
    Kessler, A., Halitschke, R. & Poveda, K. Herbivory-mediated pollinator limitation: Negative impacts of induced volatiles on plant-pollinator interactions. Ecology 92, 1769–1780 (2011).PubMed 

    Google Scholar 
    Sletvold, N., Moritz, K. K. & Ågren, J. Additive effects of pollinators and herbivores result in both conflicting and reinforcing selection on floral traits. Ecology 96, 214–221 (2015).PubMed 

    Google Scholar 
    Ramos, S. E. & Schiestl, F. P. Rapid plant evolution driven by the interaction of pollination and herbivory. Science (80-). 364, 193–196 (2019).ADS 
    CAS 

    Google Scholar 
    Rojas-Nossa, S. V. Estrategias de extracción de néctar por pinchaflores (Aves: Diglossa y Diglossopis) y sus efectos sobre la polinización de plantas de los altos Andes. Ornitol. Colomb. 5, 21–39 (2007).
    Google Scholar 
    R Team Core. R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2021).Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
    Google Scholar 
    Diaz-Uriarte, R. Package ‘ varSelRF ’. Compr. R Arch. Netw. 1–23 (2015). More

  • in

    Health risk assessment and source apportionment of potentially toxic metal(loid)s in windowsill dust of a rapidly growing urban settlement, Iran

    PTM concentrationsIn Table 1, the descriptive statistics of PTMs in 50 dust samples from Qom city are described. The background values were used based on the concentrations of metals in the Upper Continental Crust. The mean concentration of As, Cd, Cu, Mo, Pb, Sb, and, Zn exceeded the background value. Also, Cd, Cu, Mo, Pb, Sb, and, Zn had a coefficient of variation (C.V.) greater than 50%, indicating a severe variability in PTMs concentrations in the atmospheric dust of the studied area2. Metals with C.V.  Pb  > Zn  > As  > Cd  > Cu  > Mo  > Cr  > Mn  > Ni = Co. Antimony (38.55) and Pb (35.13) had the highest average EF values, which means they were enriched very high in the windowsill dust. Also, they had a wide range of EF values in the 50 stations: from 4.0 to 227.0 for Sb, and from 8.3 to 140.8 for Pb which might reflect the existence of discrete multiple sources in the studied area. The degree of enrichment for Pb and Sb in the industrial sector was extreme and in the commercial sector was very high; also, the other sectors were significantly enriched. Zinc and As had a more homogenous enrichment in the area. In all the functional sectors, 95% and 84% of stations were significantly enriched by As and Zn, respectively. Copper, Cd, and Mo were moderately enriched in all functional sectors, but the greenspace sector had minimal enrichment by these elements. Some areas in the industrial sector had significant to very high enrichment of Cd. The EF value indicated Co, Cr, Mn, and Ni were minimally enriched in all the stations.Figure 2Box plot of the (a) enrichment factor (EF), and (b) geo accumulation index (Igeo) for the dust samples in the studied area.Full size imageThe highest average values of Igeo were obtained in the order of Pb  > Sb  > As  > Zn. PTMs included Co, Cr, Ni, Fe, and Mn were categorized as unpolluted and Cd, Cu, and Mo were in the category of unpolluted to moderately polluted. In the industrial zone, the windowsill dust was extremely polluted with Sb and Pb. The sequence of contamination intensity with Pb, Zn and Sb according to land use was: industrial  > commercial  > residential  > greenspace. The highest concentration of arsenic in the study area belongs to the industrial area.To evaluate the pollution level based on land use, PLI and mCd indices were utilized (Fig. 3). These cumulative indices showed that the dust in Qom city is considerably contaminated with PTMs. According to the PLI index, all the stations were categorized as polluted sites. The PLIzone values were in the order of industrial (3.77)  > commercial (2.05)  > residential (1.67)  > green space (1.38). This pattern was also repeated with the mCd index. The mCd for the industrial sector ranged from 6.98 (high contamination level) to 39.60 (ultra-high contamination level). In the commercial sector, fifty percent of dust samples were classified as having a high degree of contamination. All the greenspace stations were in the moderate pollution category. This shows the possible effect of tree density in diminishing the risk of dust pollution to the receptors.Figure 3Pollution level indexes (a) mCd and (b) PLI, based on four functional areas.Full size imageSpatial distribution of PTMsThe As, Cd, Cu, Sb, Pb, and Zn content in 100% of the dust samples exceeded the background value. Spatial distribution maps were generated for the hotspot PTMs (As, Sb, Pb, Cd, Cu, Mo and Zn) by applying the inverse distance weighted (IDW) interpolation method (ArcGIS 10.3). Figure 4 demonstrates that PTMs dispersions were slightly influenced by the prevailing wind direction (from the west), suggesting they came from the point- or area- sources. On the other hand, the K–S test showed that the overall distribution of PTMs was not normal in the studied region. This might signify the influence of industrial activities and the presence of multiple sources of dust.Figure 4Spatial distribution maps of seven PTMs in windowsill dusts of Qom, Iran. This map was constructed using ArcGIS version 10.3. (https://www.esri.com/en-us/arcgis/products/arcgisdesktop/overview).Full size imageThe highest pollution load of PTMs belonged to the industrial section. The level of pollution gradually decreased from Shokouhieh to Mahmoudabad industrial zones. The reason is related to more active industries, a closed environment, and more construction existing in Shokouhieh industrial town than in Mahmoudabad industrial town.There is a clear decreasing trend from the central part to southern (downtown area) and southwestern (suburb area) parts of the city. In fact, these parts are diffusely populated and the southwestern part is almost new with lots of barren lands. Copper, Mo, and Cd show high concentrations toward the central part of the city. Educational, cultural and commercial activities are mainly located in the central part of the city. Also, historical and religious districts in the city center are accompanied by a huge influx of tourists throughout the year. For this reason, the central part of the city has various public transportations such as bus stands and taxi stations, and is dominated by a high load of motorcycles.In the eastern part of the city, some hotspots can be observed (Fig. 4). This part includes an important transportation system (like highways and a complex interchange) where exhaust traffic emissions might be a probable source of As, Sb, Pb, and Zn. Unlike Pb and Zn, several peaks of As are scattered in the western part, suggesting an area source might exist in the region. It is noteworthy that the western area is densely populated with lots of residential buildings. Bisht et al. (2022)35 also observed hotspots of As in the residential area of Dehradun, India.PTM potential sourcesTo evaluate the relationship between PTMs in dust samples, the Spearman correlation and PCA were developed (Fig. 5) and more details are given in Table S7. Statistical analysis can help to identify the potential source of contamination in urban dust. The Spearman correlation was significant at p  residential ≈ greenspace. The five PTMs with the highest overall HI are ranked as follows: Pb  > As  > Cr  > Mn  > Sb (Fig. 7). The HI values in all the sections were lower than the permissible level (1.00), except for Pb. In the industrial section, Pb recorded the highest HI value for children (HI = 1.73) which exceeded the acceptable value. The HI values were 10 times higher for children than adults indicating they are more susceptible to PTMs in the dust.The dominant pathway for noncancerous risk was ingestion followed by dermal contact and inhalation. The trend is in line with previous research25,51,52. However, for Co and Mn, the descending order was different as follows ingestion  > inhalation  > dermal contact. The highest contribution of HQinh and HQderm to HI was measured for Co (34.0%) and Cd (29.0%), respectively.In this study, the carcinogenic risk from windowsill dust was estimated for the carcinogens including Cd, Co, Cr and Ni, Pb, and As through the possible routes (Fig. 8, Table S9). The contribution of PTMs to CR decreased in the order of Cr (3.24E−05)  > As (2.05E−05)  > Pb (2.52E−06)  > Co (6.91E−09)  > Ni (1.72E−09)  > Cd (2.58E−10). The average CR values for target PTMs through inhalation ranged from 7.9E−10 to 1.7E−07, which remained in the safety zone (CR  inhalation  > dermal (Fig. 8). While the contribution of Cr to carcinogenic risk was higher through inhalation than ingestion. The reports concluded that the primary exposure route of Cr is inhalation54. Considering the predominant forms of Cr in the environment, CrVI is more toxic than CrIII. Exposure to CrVI can cause immunological diseases, dental effects and carcinogenic effects (lung cancer, nose and nasal sinus cancer, suspected laryngeal and stomach cancers)54,55.The result of health risk from target PTMs in windowsills of Qom indicates significant chronic exposure to Pb can take place for children in the industrial zone. The ingestion route is the most probable pathway for children due to their hand–to–mouth behavior56. Lead can bio-accumulate in the body without any obvious symptoms of toxicity56. The total CR values for Pb, Cr and As in different land-use types were in the range of tolerable carcinogenic risk (1 × 10−4  More

  • in

    The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets

    Tsai, C.-W., Lai, C.-F., Chao, H.-C. & Vasilakos, A. V. Big data analytics: a survey. J. Big Data 2, 21 (2015).
    Google Scholar 
    Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).ADS 
    CAS 

    Google Scholar 
    Manzoni, C. et al. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief. Bioinform. 19, 286–302 (2018).CAS 

    Google Scholar 
    Lichtman, J. W., Pfister, H. & Shavit, N. The big data challenges of connectomics. Nat. Neurosci. 17, 1448–1454 (2014).CAS 

    Google Scholar 
    Altaf-Ul-Amin, M., Afendi, F. M., Kiboi, S. K. & Kanaya, S. Systems biology in the context of big data and networks. Biomed. Res. Int. 2014, 428570 (2014).
    Google Scholar 
    Xia, J., Wang, J. & Niu, S. Research challenges and opportunities for using big data in global change biology. Glob. Change Biol. 26, 6040–6061 (2020).ADS 

    Google Scholar 
    Hindell, M. A. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020).ADS 
    CAS 

    Google Scholar 
    Hussey, N. E. et al. Ecology. Aquatic animal telemetry: A panoramic window into the underwater world. Science 348, 1255642 (2015).
    Google Scholar 
    Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).
    Google Scholar 
    Sherub, S., Fiedler, W., Duriez, O. & Wikelski, M. Bio-logging, new technologies to study conservation physiology on the move: a case study on annual survival of Himalayan vultures. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 203, 531–542 (2017).
    Google Scholar 
    Nathan, R. et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780 (2022).CAS 

    Google Scholar 
    Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. J. Anim. Ecol. 89, 161–172 (2020).
    Google Scholar 
    Patterson, A., Gilchrist, H. G., Chivers, L., Hatch, S. & Elliott, K. A comparison of techniques for classifying behavior from accelerometers for two species of seabird. Ecol. Evol. 9, 3030–3045 (2019).
    Google Scholar 
    Masello, J. F. et al. How animals distribute themselves in space: energy landscapes of Antarctic avian predators. Mov. Ecol. 9, 24 (2021).
    Google Scholar 
    Shepard, E. L. C. et al. Energy landscapes shape animal movement ecology. Am. Nat. 182, 298–312 (2013).
    Google Scholar 
    Elliott, K. H., Le Vaillant, M., Kato, A., Speakman, J. R. & Ropert-Coudert, Y. Accelerometry predicts daily energy expenditure in a bird with high activity levels. Biol. Lett. 9, 20120919 (2013).
    Google Scholar 
    Nickel, B. A., Suraci, J. P., Nisi, A. C. & Wilmers, C. C. Energetics and fear of humans constrain the spatial ecology of pumas. Proc. Natl. Acad. Sci. USA 118, e2004592118 (2021).
    Eisaguirre, J. M., Booms, T. L., Barger, C. P., Lewis, S. B. & Breed, G. A. Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds. J. Anim. Ecol. 89, 2567–2583 (2020).
    Google Scholar 
    Wittemyer, G., Northrup, J. M. & Bastille-Rousseau, G. Behavioural valuation of landscapes using movement data. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180046 (2019).
    Google Scholar 
    Chimienti, M. et al. The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data. Ecol. Evol. 6, 727–741 (2016).
    Google Scholar 
    Hounslow, J. L. et al. Assessing the effects of sampling frequency on behavioural classification of accelerometer data. J. Exp. Mar. Bio. Ecol. 512, 22–30 (2019).
    Google Scholar 
    Glass, T. W., Breed, G. A., Robards, M. D., Williams, C. T. & Kielland, K. Accounting for unknown behaviors of free-living animals in accelerometer-based classification models: Demonstration on a wide-ranging mesopredator. Ecol. Inf. 60, 101152 (2020).
    Google Scholar 
    Wang, Y. et al. Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements. Mov. Ecol. 3, 2 (2015).
    Google Scholar 
    Chakravarty, P., Cozzi, G., Ozgul, A. & Aminian, K. A novel biomechanical approach for animal behaviour recognition using accelerometers. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13172 (2019).Article 

    Google Scholar 
    Clarke, T. M. et al. Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish. Mov. Ecol. 9, 26 (2021).
    Google Scholar 
    Zhang, J., O’Reilly, K. M., Perry, G. L. W., Taylor, G. A. & Dennis, T. E. Extending the functionality of behavioural change-point analysis with k-Means clustering: a case study with the little penguin (Eudyptula minor). PLoS ONE 10, e0122811 (2015).
    Google Scholar 
    Korpela, J. et al. Machine learning enables improved runtime and precision for bio-loggers on seabirds. Commun. Biol. 3, 633 (2020).
    Google Scholar 
    Jeantet, L. et al. Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology. R. Soc. Open Sci. 7, 200139 (2020).ADS 

    Google Scholar 
    Wang, G. Machine learning for inferring animal behavior from location and movement data. Ecol. Inf. 49, 69–76 (2019).
    Google Scholar 
    Dunford, C. E. et al. Surviving in steep terrain: a lab-to-field assessment of locomotor costs for wild mountain lions (Puma concolor). Mov. Ecol. 8, 34 (2020).
    Google Scholar 
    Jeanniard-du-Dot, T., Guinet, C., Arnould, J. P. Y., Speakman, J. R. & Trites, A. W. Accelerometers can measure total and activity-specific energy expenditures in free-ranging marine mammals only if linked to time-activity budgets. Funct. Ecol. 31, 377–386 (2017).
    Google Scholar 
    Hicks, O. et al. Acceleration predicts energy expenditure in a fat, flightless, diving bird. Sci. Rep. 10, 21493 (2020).ADS 
    CAS 

    Google Scholar 
    Dentinger, J. E. et al. A probabilistic framework for behavioral identification from animal-borne accelerometers. Ecol. Model. 464, 109818 (2022).
    Google Scholar 
    Chakravarty, P., Maalberg, M., Cozzi, G., Ozgul, A. & Aminian, K. Behavioural compass: animal behaviour recognition using magnetometers. Mov. Ecol. 7, 28 (2019).
    Google Scholar 
    Hammond, T. T., Palme, R. & Lacey, E. A. Ecological specialization, variability in activity patterns and response to environmental change. Biol. Lett. 14, 20180115 (2018).
    Google Scholar 
    Lynch, H. J. & LaRue, M. A. First global census of the Adélie Penguin. Auk 131, 457–466 (2014).
    Google Scholar 
    Riaz, J., Bestley, S., Wotherspoon, S., Freyer, J. & Emmerson, L. From trips to bouts to dives: temporal patterns in the diving behaviour of chick-rearing Adélie penguins East Antarctica. Mar. Ecol. Prog. Ser. 654, 177–194 (2020).ADS 

    Google Scholar 
    Cherel, Y. Isotopic niches of emperor and Adélie penguins in Adélie Land, Antarctica. Mar. Biol. 154, 813–821 (2008).
    Google Scholar 
    Little Penguin (Eudyptula minor) – BirdLife species factsheet. at Carroll, G., Harcourt, R., Pitcher, B. J., Slip, D. & Jonsen, I. Recent prey capture experience and dynamic habitat quality mediate short-term foraging site fidelity in a seabird. Proc. Biol. Sci. 285, 20180788 (2018).
    Google Scholar 
    Meyer, X. et al. Oceanic thermal structure mediates dive sequences in a foraging seabird. Ecol. Evol. 10, 6610–6622 (2020).
    Google Scholar 
    Cavallo, C. et al. Quantifying prey availability using the foraging plasticity of a marine predator, the little penguin. Funct. Ecol. https://doi.org/10.1111/1365-2435.13605 (2020).Article 

    Google Scholar 
    Ropert-Coudert, Y., Chiaradia, A. & Kato, A. An exceptionally deep dive by a Little Penguin Eudyptula minor. Mar. Ornithol 34, 71–74 (2006).
    Google Scholar 
    Ropert-Coudert, Y., Kato, A., Wilson, R. P. & Cannell, B. Foraging strategies and prey encounter rate of free-ranging Little Penguins. Mar. Biol. 149, 139–148 (2006).
    Google Scholar 
    Rodríguez, A., Chiaradia, A., Wasiak, P., Renwick, L. & Dann, P. Waddling on the dark side: ambient light affects attendance behavior of little penguins. J. Biol. Rhythms 31, 194–204 (2016).
    Google Scholar 
    Ropert-Coudert, Y. et al. Happy feet in a hostile world? the future of penguins depends on proactive management of current and expected threats. Front. Mar. Sci. 6, 248 (2019).
    Google Scholar 
    Shuert, C. R., Pomeroy, P. P. & Twiss, S. D. Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal. Anim. Biotelemetry 6, 14 (2018).
    Google Scholar 
    Dickinson, E. R. et al. Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids. Mov. Ecol. 9, 28 (2021).
    Google Scholar 
    Conway, A. M., Durbach, I. N., McInnes, A. & Harris, R. N. Frame-by-frame annotation of video recordings using deep neural networks. Ecosphere 12, e03384 (2021).
    Google Scholar 
    Ravindran, S. Five ways deep learning has transformed image analysis. Nature 609, 864–866 (2022).ADS 
    CAS 

    Google Scholar 
    Del Caño, M. et al. Fine-scale body and head movements allow to determine prey capture events in the Magellanic Penguin (Spheniscus magellanicus). Mar. Biol. 168, 84 (2021).
    Google Scholar 
    Johnson, J. M. & Khoshgoftaar, T. M. Survey on deep learning with class imbalance. J. Big Data 6, 27 (2019).
    Google Scholar 
    Hazen, E. L. et al. Marine top predators as climate and ecosystem sentinels. Front. Ecol. Environ. 17, 565–574 (2019).
    Google Scholar 
    Sánchez, S. et al. Within-colony spatial segregation leads to foraging behaviour variation in a seabird. Mar. Ecol. Prog. Ser. 606, 215–230 (2018).ADS 

    Google Scholar 
    Patrick, S. C., Martin, J. G. A., Ummenhofer, C. C., Corbeau, A. & Weimerskirch, H. Albatrosses respond adaptively to climate variability by changing variance in a foraging trait. Glob. Change Biol. https://doi.org/10.1111/gcb.15735 (2021).Article 

    Google Scholar 
    Bonar, M. et al. Geometry of the ideal free distribution: individual behavioural variation and annual reproductive success in aggregations of a social ungulate. Ecol. Lett. 23, 1360–1369 (2020).
    Google Scholar 
    Michelot, C., Kato, A., Raclot, T. & Ropert-Coudert, Y. Adélie penguins foraging consistency and site fidelity are conditioned by breeding status and environmental conditions. PLoS ONE 16, e0244298 (2021).CAS 

    Google Scholar 
    Mahoney, P. J. et al. Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties. Ecol. Appl. 28, 1715–1729 (2018).
    Google Scholar 
    Watanabe, Y. Y., Ito, K., Kokubun, N. & Takahashi, A. Foraging behavior links sea ice to breeding success in Antarctic penguins. Sci. Adv. 6, eaba4828 (2020).ADS 

    Google Scholar 
    Lescroël, A. et al. Working less to gain more: when breeding quality relates to foraging efficiency. Ecology 91, 2044–2055 (2010).
    Google Scholar 
    Zimmer, I., Ropert-Coudert, Y., Kato, A., Ancel, A. & Chiaradia, A. Does foraging performance change with age in female little penguins (Eudyptula minor)?. PLoS ONE 6, e16098 (2011).ADS 
    CAS 

    Google Scholar 
    Hertel, A. G., Royauté, R., Zedrosser, A. & Mueller, T. Biologging reveals individual variation in behavioural predictability in the wild. J. Anim. Ecol. 90, 723–737 (2021).
    Google Scholar 
    Dickinson, E. R., Stephens, P. A., Marks, N. J., Wilson, R. P. & Scantlebury, D. M. Best practice for collar deployment of tri-axial accelerometers on a terrestrial quadruped to provide accurate measurement of body acceleration. Anim. Biotelemetry 8, 9 (2020).
    Google Scholar 
    Garde, B. et al. Ecological inference using data from accelerometers needs careful protocols. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13804 (2022).Article 

    Google Scholar 
    Watanabe, Y. Y., Ito, M. & Takahashi, A. Testing optimal foraging theory in a penguin-krill system. Proc. Biol. Sci. 281, 20132376 (2014).
    Google Scholar 
    Grémillet, D. et al. Energetic fitness: Field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics. Funct. Ecol. 32, 1203–1213 (2018).
    Google Scholar 
    Chimienti, M. et al. Quantifying behavior and life-history events of an Arctic ungulate from year-long continuous accelerometer data. Ecosphere 12, e03565 (2021).
    Google Scholar 
    Sutton, G. J., Botha, J. A., Speakman, J. R. & Arnould, J. P. Y. Validating accelerometry-derived proxies of energy expenditure using the doubly-labelled water method in the smallest penguin species. Biol. Open 10, bio055475 (2021).
    Google Scholar 
    Pagano, A. M. & Williams, T. M. Estimating the energy expenditure of free-ranging polar bears using tri-axial accelerometers: A validation with doubly labeled water. Ecol. Evol. 9, 4210–4219 (2019).
    Google Scholar 
    Ballance, L. T., Ainley, D. G., Ballard, G. & Barton, K. An energetic correlate between colony size and foraging effort in seabirds, an example of the Adélie penguin Pygoscelis adeliae. J. Avian Biol. 40, 279–288 (2009).
    Google Scholar 
    Wilson, R. P. et al. Long-term attachment of transmitting and recording devices to penguins and other seabirds. Wildl. Soc. Bull. 25, 101–106 (1997).
    Google Scholar 
    Shepard, E. L. C. et al. Identification of animal movement patterns using tri-axial accelerometry. Endanger. Species Res. 10, 47–60 (2008).ADS 

    Google Scholar 
    Kato, A., Ropert-Coudert, Y., Grémillet, D. & Cannell, B. Locomotion and foraging strategy in foot-propelled and wing-propelled shallow-diving seabirds. Mar. Ecol. Prog. Ser. 308, 293–301 (2006).ADS 

    Google Scholar 
    Team, R. C. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://url.org/www.R-project.org/ (2021).
    Ainley, D. The Adélie Penguin: Bellwether of Climate Change (New York: Columbia University Press) (2006).Langrognet, F. et al. Rmixmod: Classification with Mixture Modelling. (2020).Bishop, C. M. Pattern Recognition and Machine Learning. Springer Science+Business Media, LLC, New
    York, NY. (2006).Amélineau, F. et al. Intra- and inter-individual changes in little penguin diving and isotopic composition over the breeding season. Mar. Biol. 168, 62 (2021).
    Google Scholar 
    Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
    Google Scholar 
    Kuhn, M. & Wickham, H. Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. https://www.tidymodels.org (2020).Wright, M. N. & Ziegler, A. Ranger : A fast implementation of random forests for high dimensional data in C++ andR. J. Stat. Softw. 77, 1–17 (2017).
    Google Scholar  More

  • in

    Senescence of the immune defences and reproductive trade-offs in females of the mealworm beetle, Tenebrio molitor

    Williams, G. C. Natural selection, the costs of reproduction, and a refinement of lack’s principle. Am. Nat. 100, 687–690 (1966).
    Google Scholar 
    Stearns, S. C. The evolution of life histories. (Oxford University Press, 1992).Kirkwood, T. B. L. Evolution of ageing. Nature 270, 301–304 (1977).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Partridge, L., Prowse, N. & Pignatelli, P. Another set of responses and correlated responses to selection on age at reproduction in Drosophila melanogaster. Proc. R. Soc. B. 266, 255–261 (1999).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Metcalfe, N. Growth versus lifespan: Perspectives from evolutionary ecology. Exp. Gerontol. 38, 935–940 (2003).PubMed 

    Google Scholar 
    Lee, W.-S., Monaghan, P. & Metcalfe, N. B. Experimental demonstration of the growth rate–lifespan trade-off. Proc. R. Soc. B. 280, 20122370 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Lemaître, J.-F. et al. Early-late life trade-offs and the evolution of ageing in the wild. Proc. R. Soc. B. 282, 20150209 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Jehan, C., Sabarly, C., Rigaud, T. & Moret, Y. Late-life reproduction in an insect: Terminal investment, reproductive restraint or senescence. J. Anim. Ecol. 90, 282–297 (2021).PubMed 

    Google Scholar 
    Pawelec, G. Age and immunity: What is “immunosenescence”?. Exp. Gerontol. 105, 4–9 (2018).CAS 
    PubMed 

    Google Scholar 
    Schwenke, R. A., Lazzaro, B. P. & Wolfner, M. F. Reproduction–immunity trade-offs in insects. Annu. Rev. Entomol. 61, 239–256 (2016).CAS 
    PubMed 

    Google Scholar 
    Maklakov, A. A. & Chapman, T. Evolution of ageing as a tangle of trade-offs: Energy versus function. Proc. R. Soc. B. 286, 20191604 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hamel, S. et al. Fitness costs of reproduction depend on life speed: empirical evidence from mammalian populations: Fitness costs of reproduction in mammals. Ecol. Lett. 13, 915–935 (2010).PubMed 

    Google Scholar 
    Graham, A. L., Allen, J. E. & Read, A. F. Evolutionary causes and consequences of immunopathology. Annu. Rev. Ecol. Evol. Syst. 36, 373–397 (2005).
    Google Scholar 
    Sorci, G. & Faivre, B. Inflammation and oxidative stress in vertebrate host–parasite systems. Phil. Trans. R. Soc. B. 364, 71–83 (2009).PubMed 

    Google Scholar 
    Ashley, N. T., Weil, Z. M. & Nelson, R. J. Inflammation: Mechanisms, costs, and natural variation. Annu. Rev. Ecol. Evol. Syst. 43, 385–406 (2012).
    Google Scholar 
    Babin, A., Moreau, J. & Moret, Y. Storage of carotenoids in crustaceans as an adaptation to modulate immunopathology and optimize immunological and life history strategies. BioEssays 41, 1800254 (2019).
    Google Scholar 
    Vasto, S. et al. Inflammatory networks in ageing, age-related diseases and longevity. Mech. Ageing Dev. 128, 83–91 (2007).CAS 
    PubMed 

    Google Scholar 
    Finch, C. E. & Crimmins, E. M. Inflammatory exposure and historical changes in human life-spans. Science 305, 1736–1739 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Licastro, F. et al. Innate immunity and inflammation in ageing: A key for understanding age-related diseases. Immun. Ageing 2, 8 (2005).PubMed 
    PubMed Central 

    Google Scholar 
    Pawelec, G., Goldeck, D. & Derhovanessian, E. Inflammation, ageing and chronic disease. Curr. Opin. Immunol. 29, 23–28 (2014).CAS 
    PubMed 

    Google Scholar 
    Pursall, E. R. & Rolff, J. Immune responses accelerate ageing: Proof-of-principle in an insect model. PLoS ONE 6, e19972 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Khan, I., Agashe, D. & Rolff, J. Early-life inflammation, immune response and ageing. Proc. R. Soc. B. 284, 20170125 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Vigneron, A., Jehan, C., Rigaud, T. & Moret, Y. Immune defenses of a beneficial pest: The mealworm beetle, Tenebrio molitor. Front. Physiol. 10, 138 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Jehan, C., Chogne, M., Rigaud, T. & Moret, Y. Sex-specific patterns of senescence in artificial insect populations varying in sex-ratio to manipulate reproductive effort. BMC Evol. Biol. 20, 18 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Jehan, C., Sabarly, C., Rigaud, T. & Moret, Y. Age-specific fecundity under pathogenic threat in an insect: Terminal investment versus reproductive restraint. J. Anim. Ecol. 91, 101–111 (2022).PubMed 

    Google Scholar 
    Chung, K.-H. & Moon, M.-J. Fine structure of the hemopoietic tissues in the mealworm beetle, Tenebrio molitor. Entomol. Res. 34, 131–138 (2004).
    Google Scholar 
    Urbański, A., Adamski, Z. & Rosiński, G. Developmental changes in haemocyte morphology in response to Staphylococcus aureus and latex beads in the beetle Tenebrio molitor L.. Micron 104, 8–20 (2018).PubMed 

    Google Scholar 
    Vommaro, M. L., Kurtz, J. & Giglio, A. Morphological characterisation of haemocytes in the mealworm beetle Tenebrio molitor (Coleoptera, Tenebrionidae). Insects 12, 423 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Söderhäll, K. & Cerenius, L. Role of the prophenoloxidase-activating system in invertebrate immunity. Curr. Opin. Immunol. 10, 23–28 (1998).PubMed 

    Google Scholar 
    Siva-Jothy, M. T., Moret, Y. & Rolff, J. Insect immunity: an evolutionary ecology perspective. in Advances in Insect Physiology vol. 32 1–48 (Elsevier, 2005).Nappi, A. J. & Ottaviani, E. Cytotoxicity and cytotoxic molecules in invertebrates. BioEssays 22, 469–480 (2000).CAS 
    PubMed 

    Google Scholar 
    Sadd, B. M. & Siva-Jothy, M. T. Self-harm caused by an insect’s innate immunity. Proc. R. Soc. B. 273, 2571–2574 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    Daukšte, J., Kivleniece, I., Krama, T., Rantala, M. J. & Krams, I. Senescence in immune priming and attractiveness in a beetle: Immunosenescence in a beetle. J. Evol. Biol. 25, 1298–1304 (2012).PubMed 

    Google Scholar 
    Krams, I. et al. Trade-off between cellular immunity and life span in mealworm beetles Tenebrio molitor. Curr. Zool. 59, 340–346 (2013).
    Google Scholar 
    Moon, H. J., Lee, S. Y., Kurata, S., Natori, S. & Lee, B. L. Purification and molecular cloning of cDNA for an inducible antibacterial protein from larvae of the coleopteran, Tenebrio molitor. J. Biochem. 116, 53–58 (1994).CAS 
    PubMed 

    Google Scholar 
    Lee, Y. J. et al. Structure and expression of the tenecin 3 gene in Tenebrio molitor. Biochem. Biophys. Res. Comm. 218, 6–11 (1996).CAS 
    PubMed 

    Google Scholar 
    Kim, D. H. et al. Bacterial expression of tenecin 3, an insect antifungal protein isolated from Tenebrio molitor, and its efficient purification. Mol. Cells 8, 786–789 (1998).CAS 
    PubMed 

    Google Scholar 
    Roh, K.-B. et al. Proteolytic cascade for the activation of the insect toll pathway induced by the fungal cell wall component. J. Biol. Chem. 284, 19474–19481 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Park, J.-W. et al. Beetle Immunity. in Invertebrate Immunity (ed. Söderhäll, K.) vol. 708 163–180 (Springer US, 2010).Chae, J.-H. et al. Purification and characterization of tenecin 4, a new anti-Gram-negative bacterial peptide, from the beetle Tenebrio molitor. Dev. Comp. Immunol. 36, 540–546 (2012).CAS 
    PubMed 

    Google Scholar 
    Haine, E. R., Pollitt, L. C., Moret, Y., Siva-Jothy, M. T. & Rolff, J. Temporal patterns in immune responses to a range of microbial insults (Tenebrio molitor). J. Insect Physiol. 54, 1090–1097 (2008).CAS 
    PubMed 

    Google Scholar 
    Dhinaut, J., Chogne, M. & Moret, Y. Immune priming specificity within and across generations reveals the range of pathogens affecting evolution of immunity in an insect. J. Anim. Ecol. 87, 448–463 (2018).PubMed 

    Google Scholar 
    Hoffmann, J. A., Reichhart, J.-M. & Hetru, C. Innate immunity in higher insects. Curr. Opin. Immunol. 8, 8–13 (1996).CAS 
    PubMed 

    Google Scholar 
    Moret, Y. Explaining variable costs of the immune response: selection for specific versus non-specific immunity and facultative life history change. Oikos 102, 213–216 (2003).
    Google Scholar 
    Khan, I., Prakash, A. & Agashe, D. Immunosenescence and the ability to survive bacterial infection in the red flour beetle Tribolium castaneum. J. Anim. Ecol. 85, 291–301 (2016).PubMed 

    Google Scholar 
    Rolff, J. Effects of age and gender on immune function of dragonflies (Odonata, Lestidae) from a wild population. Can. J. Zool. 79, 2176–2180 (2001).
    Google Scholar 
    Doums, C., Moret, Y., Benelli, E. & Schmid-Hempel, P. Senescence of immune defence in Bombus workers. Ecol. Entomol. 27, 138–144 (2002).
    Google Scholar 
    Schmid, M. R., Brockmann, A., Pirk, C. W. W., Stanley, D. W. & Tautz, J. Adult honeybees (Apis mellifera L.) abandon hemocytic, but not phenoloxidase-based immunity. J. Insect Physiol. 54, 439–444 (2008).CAS 
    PubMed 

    Google Scholar 
    Moret, Y. & Schmid-Hempel, P. Immune responses of bumblebee workers as a function of individual and colony age: senescence versus plastic adjustment of the immune function. Oikos 118, 371–378 (2009).
    Google Scholar 
    Armitage, S. A. O. & Boomsma, J. J. The effects of age and social interactions on innate immunity in a leaf-cutting ant. J. Insect Physiol. 56, 780–787 (2010).CAS 
    PubMed 

    Google Scholar 
    Korner, P. & Schmid-Hempel, P. In vivo dynamics of an immune response in the bumble bee Bombus terrestris. J. Invert. Pathol. 87, 59–66 (2004).CAS 

    Google Scholar 
    Li, T., Yan, D., Wang, X., Zhang, L. & Chen, P. Hemocyte changes during immune melanization in Bombyx Mori infected with Escherichia coli. Insects 10, 301 (2019).PubMed Central 

    Google Scholar 
    Chase, M. R., Raina, K., Bruno, J. & Sugumaran, M. Purification, characterization and molecular cloning of prophenoloxidases from Sarcophaga bullata. Insect Biochem. Mol. Biol. 30, 953–967 (2000).CAS 
    PubMed 

    Google Scholar 
    Kanost, M. R. & Gorman, M. J. Phenoloxidases in insect immunity. in Insect Immunology 69–96 (Elsevier, 2008).Sadd, B. M. et al. Modulation of sexual signalling by immune challenged male mealworm beetles (Tenebrio molitor L.): Evidence for terminal investment and dishonesty. J. Evol. Biol. 19, 321–325 (2006).CAS 
    PubMed 

    Google Scholar 
    Gálvez, D. & Chapuisat, M. Immune priming and pathogen resistance in ant queens. Ecol. Evol. 4, 1761–1767 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Armitage, S. A. O. & Siva-Jothy, M. T. Immune function responds to selection for cuticular colour in Tenebrio molitor. Heredity 94, 650–656 (2005).CAS 
    PubMed 

    Google Scholar 
    Armitage, S. A. O., Thompson, J. J. W., Rolff, J. & Siva-Jothy, M. T. Examining costs of induced and constitutive immune investment in Tenebrio molitor. J. Evol. Biol. 16, 1038–1044 (2003).CAS 
    PubMed 

    Google Scholar 
    Kokoza, V. A. et al. Transcriptional regulation of the mosquito vitellogenin gene via a blood meal-triggered cascade. Gene 274, 47–65 (2001).CAS 
    PubMed 

    Google Scholar 
    Isaac, P. G. & Bownes, M. Ovarian and fat-body vitellogenin synthesis in Drosophila melanogaster. Europ. J. Biochem. 123, 527–534 (2005).
    Google Scholar 
    Hoffmann, J. A. The immune response of Drosophila. Nature 426, 33–38 (2003).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Tzou, P. et al. Tissue-specific inducible expression of antimicrobial peptide genes in Drosophila surface epithelia. Immunity 13, 737–748 (2000).CAS 
    PubMed 

    Google Scholar 
    Haine, E. R., Moret, Y., Siva-Jothy, M. T. & Rolff, J. Antimicrobial defense and persistent infection in insects. Science 322, 1257–1259 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Moret, Y. & Siva-Jothy, M. T. Adaptive innate immunity? Responsive-mode prophylaxis in the mealworm beetle, Tenebrio molitor. Proc. R. Soc. B. 270, 2475–2480 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Du Rand, N. & Laing, M. D. Determination of insecticidal toxicity of three species of entomopathogenic spore-forming bacterial isolates against Tenebrio molitor L. (Coleoptera: Tenebrionidae). Afr. J. Microbiol. Res. 5, 2222–2228 (2011).
    Google Scholar 
    Jurat-Fuentes, J. L. & Jackson, T. Bacterial entomopathogens. In Insect Pathology 2nd edn (eds Kaya, H. & Vera, F.) 265–349 (Elsevier Academic Press, Cambridge, Mass, 2012).
    Google Scholar 
    Dhinaut, J., Balourdet, A., Teixeira, M., Chogne, M. & Moret, Y. A dietary carotenoid reduces immunopathology and enhances longevity through an immune depressive effect in an insect model. Sci. Rep. 7, 12429 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moreau, J., Martinaud, G., Troussard, J.-P., Zanchi, C. & Moret, Y. Trans-generational immune priming is constrained by the maternal immune response in an insect. Oikos 121, 1828–1832 (2012).
    Google Scholar 
    Lee, H. S. et al. The pro-phenoloxidase of coleopteran insect, Tenebrio molitor, larvae was activated during cell clump/cell adhesion of insect cellular defense reactions. FEBS Lett. 444, 255–259 (1999).CAS 
    PubMed 

    Google Scholar 
    Zanchi, C., Troussard, J.-P., Martinaud, G., Moreau, J. & Moret, Y. Differential expression and costs between maternally and paternally derived immune priming for offspring in an insect. J. Anim. Ecol. 80, 1174–1183 (2011).PubMed 

    Google Scholar 
    Moret, Y. ‘Trans-generational immune priming’: Specific enhancement of the antimicrobial immune response in the mealworm beetle, Tenebrio molitor. Proc. R. Soc. B. 273, 1399–1405 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dubuffet, A. et al. Trans-generational immune priming protects the eggs only against gram-positive bacteria in the mealworm beetle. PLoS Pathog. 11, e1005178 (2015).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Transposable elements maintain genome-wide heterozygosity in inbred populations

    Kristensen, T. N. et al. A test of quantitative genetic theory using Drosophila- effects of inbreeding and rate of inbreeding on heritabilities and variance components. J. Evol. Biol. 18, 763–770 (2005).CAS 
    PubMed 

    Google Scholar 
    Angeloni, F., Ouborg, N. J. & Leimu, R. Meta-analysis on the association of population size and life history with inbreeding depression in plants. Biol. Conserv. 144, 35–43 (2011).
    Google Scholar 
    Caballero, A., Bravo, I. & Wang, J. Inbreeding load and purging: implications for the short-term survival and the conservation management of small populations. Heredity 118, 177–185 (2017).CAS 
    PubMed 

    Google Scholar 
    Park, D. S., Ellison, A. M. & Davis, C. C. Mating system does not predict niche breath. Glob. Ecol. Biogeogr. 27, 804–813 (2018).
    Google Scholar 
    Buckley, J., Daly, R., Cobbold, C. A., Burgess, K. & Mable, B. K. Changing environments and genetic variation: natural variation in inbreeding does not compromise short-term physiological responses. Proc. R. Soc. B Biol. Sci. 286, 20192109 (2019).
    Google Scholar 
    Grossenbacher, D., Briscoe Runquist, R., Goldberg, E. E. & Brandvain, Y. Geographic range size is predicted by plant mating system. Ecol. Lett. 18, 706–713 (2015).PubMed 

    Google Scholar 
    Wright, S. I., Lauga, B. & Charlesworth, D. Rates and patterns of molecular evolution in inbred and outbred arabidopsis. Mol. Biol. Evol. 19, 1407–1420 (2002).CAS 
    PubMed 

    Google Scholar 
    Atwell, S. et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465, 627–631 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Platt, A. et al. The scale of population structure in Arabidopsis thaliana. PLoS Genet. 6, e1000843 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Lynch, M., Conery, J. & Burger, R. Mutation accumulation and the extinction of small populations. Am. Nat. 146, 489–518 (1995).
    Google Scholar 
    Willis, J. H. The role of genes of large effect on inbreeding depression in Mimulus guttatus. Evolution 53, 1678–1691 (1999).CAS 
    PubMed 

    Google Scholar 
    Coron, C., Méléard, S., Porcher, E. & Robert, A. Quantifying the mutational meltdown in diploid populations. Am. Nat. 181, 623–636 (2013).PubMed 

    Google Scholar 
    Kyriazis, C. C., Wayne, R. K. & Lohmueller, K. E. Strongly deleterious mutations are a primary determinant of extinction risk due to inbreeding depression. Evol. Lett. https://doi.org/10.1002/evl3.209 (2020).Barrett, S. C. H. & Charlesworth, D. Effects of a change in the level of inbreeding on the genetic load. Nature 352, 522–524 (1991).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hedrick, P. W. & Garcia-Dorado, A. Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol. Evol. 31, 940–952 (2016).PubMed 

    Google Scholar 
    Robinson, J. A., Brown, C., Kim, B. Y., Lohmueller, K. E. & Wayne, R. K. Purging of strongly deleterious mutations explains long-term persistence and absence of inbreeding depression in island foxes. Curr. Biol. 28, 3487–3494.e4 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Khan, A. et al. Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. Proc. Natl. Acad. Sci. USA 118, e2023018118 (2021).Byers, D. L. & Waller, D. M. Do plant populations purge their genetic load? Effects of population size and mating history on inbreeding depression. Annu. Rev. Ecol. Syst. 30, 479–513 (1999).
    Google Scholar 
    Goodwillie, C., Kalisz, S. & Eckert, C. G. The evolutionary enigma of mixed mating systems in plants: occurrence, theoretical explanations, and empirical evidence. Annu. Rev. Ecol. Evol. Syst. 36, 47–79 (2005).
    Google Scholar 
    Hill, W. G. & Robertson, A. The effect of linkage on limits to artificial selection. Genet. Res. 8, 269–294 (1966).CAS 
    PubMed 

    Google Scholar 
    Covert, A. W. III, Lenski, R. E., Wilke, C. O. & Ofria, C. Experiments on the role of deleterious mutations as stepping stones in adaptive evolution. Proc. Natl Acad. Sci. USA 110, E3171–E3178 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Castellano, D., Coronado-Zamora, M., Campos, J. L., Barbadilla, A. & Eyre-Walker, A. Adaptive evolution is substantially impeded by hill–robertson interference in Drosophila. Mol. Biol. Evol. 33, 442–455 (2016).CAS 
    PubMed 

    Google Scholar 
    Anderson, J. T., Lee, C.-R., Rushworth, C. A., Colautti, R. I. & Mitchell-Olds, T. Genetic trade-offs and conditional neutrality contribute to local adaptation. Mol. Ecol. 22, 699–708 (2013).PubMed 

    Google Scholar 
    Hämälä, T. & Savolainen, O. Genomic patterns of local adaptation under gene flow in arabidopsis lyrata. Mol. Biol. Evol. 36, 2557–2571 (2019).
    Google Scholar 
    Taylor, M. A. et al. Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 116, 17890–17899 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Storz, J. F. Causes of molecular convergence and parallelism in protein evolution. Nat. Rev. Genet. 17, 239–250 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mimura, M. & Aitken, S. N. Local adaptation at the range peripheries of Sitka spruce. J. Evol. Biol. 23, 249–258 (2010).CAS 
    PubMed 

    Google Scholar 
    Stanton-Geddes, J., Tiffin, P. & Shaw, R. G. Role of climate and competitors in limiting fitness across range edges of an annual plant. Ecology 93, 1604–1613 (2012).PubMed 

    Google Scholar 
    Vergeer, P. & Kunin, W. E. Adaptation at range margins: common garden trials and the performance of Arabidopsis lyrata across its northwestern European range. N. Phytol. 197, 989–1001 (2013).
    Google Scholar 
    Volis, S., Ormanbekova, D. & Shulgina, I. Role of selection and gene flow in population differentiation at the edge vs. interior of the species range differing in climatic conditions. Mol. Ecol. 25, 1449–1464 (2016).CAS 
    PubMed 

    Google Scholar 
    Glémin, S., Bazin, E. & Charlesworth, D. Impact of mating systems on patterns of sequence polymorphism in flowering plants. Proc. R. Soc. B Biol. Sci. 273, 3011–3019 (2006).
    Google Scholar 
    Almeida‐Rocha, J. M., Soares, L. A. S. S., Andrade, E. R., Gaiotto, F. A. & Cazetta, E. The impact of anthropogenic disturbances on the genetic diversity of terrestrial species: A global meta‐analysis. Mol. Ecol. 29, 4812–4822 (2020).PubMed 

    Google Scholar 
    Schrader, L. et al. Transposable element islands facilitate adaptation to novel environments in an invasive species. Nat. Commun. 5, 5495 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Lu, L. et al. Tracking the genome-wide outcomes of a transposable element burst over decades of amplification. Proc. Natl Acad. Sci. USA 114, E10550–E10559 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schrader, L. & Schmitz, J. The impact of transposable elements in adaptive evolution. Mol. Ecol. 28, 1537–1549 (2019).PubMed 

    Google Scholar 
    Habig, M., Lorrain, C., Feurtey, A., Komluski, J. & Stukenbrock, E. H. Epigenetic modifications affect the rate of spontaneous mutations in a pathogenic fungus. Nat. Commun. 12, 1–13 (2021).
    Google Scholar 
    Wicker, T. et al. DNA transposon activity is associated with increased mutation rates in genes of rice and other grasses. Nat. Commun. 7, 1–9 (2016).
    Google Scholar 
    Dubin, M. J., Mittelsten Scheid, O. & Becker, C. Transposons: a blessing curse. Curr. Opin. Plant Biol. 42, 23–29 (2018).CAS 
    PubMed 

    Google Scholar 
    Stapley, J., Santure, A. W. & Dennis, S. R. Transposable elements as agents of rapid adaptation may explain the genetic paradox of invasive species. Mol. Ecol. 24, 2241–2252 (2015).CAS 
    PubMed 

    Google Scholar 
    Sultana, T., Zamborlini, A., Cristofari, G. & Lesage, P. Integration site selection by retroviruses and transposable elements in eukaryotes. Nat. Rev. Genet. 18, 292–308 (2017).CAS 
    PubMed 

    Google Scholar 
    Baduel, P. et al. Genetic and environmental modulation of transposition shapes the evolutionary potential of Arabidopsis thaliana. Genome Biol. 22, 1–26 (2021).
    Google Scholar 
    Quesneville, H. Twenty years of transposable element analysis in the Arabidopsis thaliana genome. Mob. DNA 11, 28 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Ossowski, S. et al. The rate and molecular spectrum of spontaneous mutations in Arabidopsis thaliana. Science 327, 92–94 (2010).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Coulondre, C., Miller, J. H., Farabaugh, P. J. & Gilbert, W. Molecular basis of base substitution hotspots in Escherichia coli. Nature 274, 775–780 (1978).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Duncan, B. K. & Miller, J. H. Mutagenic deamination of cytosine residues in DNA. Nature 287, 560–561 (1980).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Linquist, S. et al. Distinguishing ecological from evolutionary approaches to transposable elements. Biol. Rev. 88, 573–584 (2013).PubMed 

    Google Scholar 
    Dupeyron, M., Singh, K. S., Bass, C. & Hayward, A. Evolution of Mutator transposable elements across eukaryotic diversity. Mob. DNA 10, 1–14 (2019).
    Google Scholar 
    Batstone, R. T. Genomes within genomes: nested symbiosis and its implications for plant evolution. New Phytol. https://doi.org/10.1111/nph.17847 (2021).Pietzenuk, B. et al. Recurrent evolution of heat-responsiveness in Brassicaceae COPIA elements. Genome Biol. 17, 209 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Horváth, V., Merenciano, M. & González, J. Revisiting the relationship between transposable elements and the eukaryotic stress response. Trends Genet. 33, 832–841 (2017).PubMed 

    Google Scholar 
    Liu, S. et al. Role of H1 and DNA methylation in selective regulation of transposable elements during heat stress. N. Phytol. 229, 2238–2250 (2021).CAS 

    Google Scholar 
    Mao, H. et al. A transposable element in a NAC gene is associated with drought tolerance in maize seedlings. Nat. Commun. 6, 1–13 (2015).ADS 
    CAS 

    Google Scholar 
    Castelletti, S., Tuberosa, R., Pindo, M. & Salvi, S. A MITE transposon insertion is associated with differential methylation at the maize flowering time QTL vgt1. G3 Genes, Genomes, Genet. 4, 805–812 (2014).CAS 

    Google Scholar 
    Legrand, S. et al. Differential retention of transposable element-derived sequences in outcrossing Arabidopsis genomes. Mob. DNA 10, 30 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Quadrana, L. et al. Transposition favors the generation of large effect mutations that may facilitate rapid adaption. Nat. Commun. 10, 1–10 (2019).CAS 

    Google Scholar 
    Teschendorf, A. E. & Relton, C. L. Statistical and integrative system-level analysis of DNA methylation data. Nat. Rev. Genet. 19, 129–147 (2019).
    Google Scholar 
    Bonchev, G. & Willi, Y. Accumulation of transposable elements in selfing populations of Arabidopsis lyrata supports the ectopic recombination model of transposon evolution. N. Phytol. 219, 767–778 (2018).CAS 

    Google Scholar 
    Lockton, S., Ross-Ibarra, J. & Gaut, B. S. Demography and weak selection drive patterns of transposable element diversity in natural populations of Arabidopsis lyrata. Proc. Natl Acad. Sci. USA 105, 13965–13970 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lockton, S. & Gaut, B. S. The evolution of transposable elements in natural populations of self-fertilizing Arabidopsis thaliana and its outcrossing relative Arabidopsis lyrata. BMC Evol. Biol. 10, 10 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Mable, B. K., Dart, A. V. R., Berardo, C., Di & Witham, L. Breakdown of self-incompatibility in the perennial Arabidopsis lyrata (Brassicaceae) and its genetic consequences. Evolution 59, 1437–1448 (2005).PubMed 

    Google Scholar 
    Foxe, J. P. et al. Reconstructing origins of loss of self-incompatibility and selfing in North American Arabidopsis lyrata: a population genetic context. Evolution 64, 3495–3510 (2010).PubMed 

    Google Scholar 
    Quadrana, L. et al. The Arabidopsis thaliana mobilome and its impact at the species level. Elife 5, e15716 (2016).Stuart, T. et al. Population scale mapping of transposable element diversity reveals links to gene regulation and epigenomic variation. Elife 5, e20777 (2016).Willi, Y. Mutational meltdown in selfing Arabidopsis lyrata. Evolution 67, 806–815 (2013).PubMed 

    Google Scholar 
    Joschinski, J., van Kleunen, M. & Stift, M. Costs associated with the evolution of selfing in North American populations of Arabidopsis lyrata? Evol. Ecol. 29, 749–764 (2015).
    Google Scholar 
    Koonin, E. V. & Krupovic, M. Evolution of adaptive immunity from transposable elements combined with innate immune systems. Nat. Rev. Genet. 16, 184–192 (2015).CAS 
    PubMed 

    Google Scholar 
    Li, Z.-W. et al. Transposable elements contribute to the adaptation of Arabidopsis thaliana. Genome Biol. Evol. 10, 2140–2150 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Catlin, N. S. & Josephs, E. B. The important contribution of transposable elements to phenotypic variation and evolution. Curr. Opin. Plant Biol. 65, 102140 (2022).CAS 
    PubMed 

    Google Scholar 
    Casacuberta, E. & González, J. The impact of transposable elements in environmental adaptation. Mol. Ecol. 22, 1503–1517 (2013).CAS 
    PubMed 

    Google Scholar 
    Baduel, P., Quadrana, L., Hunter, B., Bomblies, K. & Colot, V. Relaxed purifying selection in autopolyploids drives transposable element over-accumulation which provides variants for local adaptation. Nat. Commun. 10, 1–10 (2019).
    Google Scholar 
    Wos, G., Choudhury, R. R., Kolář, F. & Parisod, C. Transcriptional activity of transposable elements along an elevational gradient in Arabidopsis arenosa. Mob. DNA 12, 7 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stebbins, G. L. Self fertilization and population variability in the higher plants. Am. Nat. 91, 337–354 (1957).
    Google Scholar 
    Takebayashi, N. & Morrell, P. L. Is self-fertilization an evolutionary dead end? Revisiting an old hypothesis with genetic theories and a macroevolutionary approach. Am. J. Bot. 88, 1143–1150 (2001).CAS 
    PubMed 

    Google Scholar 
    Igic, B. & Busch, J. W. Is self‐fertilization an evolutionary dead end? N. Phytol. 198, 386–397 (2013).
    Google Scholar 
    Goldberg, E. E. et al. Species selection maintains self-incompatibility. Science 330, 493–495 (2010).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Abu Awad, D. & Billiard, S. The double edged sword: The demographic consequences of the evolution of self-fertilization. Evolution 71, 1178–1190 (2017).PubMed 

    Google Scholar 
    Fijarczyk, A. & Babik, W. Detecting balancing selection in genomes: limits and prospects. Mol. Ecol. 24, 3529–3545 (2015).CAS 
    PubMed 

    Google Scholar 
    Wu, Q. et al. Long-term balancing selection contributes to adaptation in Arabidopsis and its relatives. Genome Biol. 18, 1–15 (2017).CAS 

    Google Scholar 
    Kerwin, R. et al. Natural genetic variation in Arabidopsis thaliana defense metabolism genes modulates field fitness. Elife 2015, 1–28 (2015).
    Google Scholar 
    Waller, D. M. Addressing Darwin’s dilemma: can pseudo-overdominance explain persistent inbreeding depression and load? Evolution 75, 779–793 (2021).CAS 
    PubMed 

    Google Scholar 
    Gilbert, K. J., Pouyet, F., Excoffier, L. & Peischl, S. Transition from background selection to associative overdominance promotes diversity in regions of low recombination. Curr. Biol. 30, 101–107.e3 (2020).CAS 
    PubMed 

    Google Scholar 
    Buckley, J. et al. R-gene variation across Arabidopsis lyrata subspecies: effects of population structure, selection and mating system. BMC Evol. Biol. 16, 93 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Schmickl, R., Jørgensen, M. H., Brysting, A. K. & Koch, M. A. Phylogeographic implications for the north american boreal-arctic Arabidopsis lyrata complex. Plant Ecol. Divers. 1, 245–254 (2008).
    Google Scholar 
    Buckley, J., Holub, E. B., Koch, M. A., Vergeer, P. & Mable, B. K. Restriction associated DNA-genotyping at multiple spatial scales in Arabidopsis lyrata reveals signatures of pathogen-mediated selection. BMC Genomics 19, 1–21 (2018).
    Google Scholar 
    Flutre, T., Duprat, E., Feuillet, C. & Quesneville, H. Considering transposable element diversification in de novo annotation approaches. PLoS ONE 6, e16526 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Luu, K., Bazin, E. & Blum, M. G. B. pcadapt: an R package to perform genome scans for selection based on principal component analysis. in. Mol. Ecol. Resour. 17, 67–77 (2017).CAS 
    PubMed 

    Google Scholar 
    Privé, F., Luu, K., Vilhjálmsson, B. J. & Blum, M. G. B. Performing highly efficient genome scans for local adaptation with R Package pcadapt Version 4. Mol. Biol. Evol. 37, 2153–2154 (2020).PubMed 

    Google Scholar 
    Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    Charlesworth, J. & Eyre-Walker, A. The McDonald-Kreitman test and slightly deleterious mutations. Mol. Biol. Evol. 25, 1007–1015 (2008).CAS 
    PubMed 

    Google Scholar 
    Hernandez, R. D. et al. Ultrarare variants drive substantial cis heritability of human gene expression. Nat. Genet. 51, 1349–1355 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Williamson, R. J. et al. Evidence for widespread positive and negative selection in coding and conserved noncoding regions of capsella grandiflora. PLoS Genet. 10, e1004622 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R. J. 9, 378–400 (2017).
    Google Scholar 
    Mattila, T. M., Tyrmi, J., Pyhäjärvi, T. & Savolainen, O. Genome-wide analysis of colonization history and concomitant selection in Arabidopsis lyrata. Mol. Biol. Evol. 34, 2665–2677 (2017).CAS 
    PubMed 

    Google Scholar  More