Ecology

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

Rule-based mean sowing and maturity datesLocation- and climate-specific mean crop calendars are computed by combining two rule-based approaches published by19 and22 to simulate sowing and physiological maturity dates of grain crops, respectively. The assumption is that farmers select growing seasons based on the mean climatic characteristics of their specific location and on the physiological limitations (base and optimum temperatures for reproductive growth; sensitivity to terminal water stress) of the respective crop species. Accordingly, they select sowing dates and cultivars with phenologies that, on average, meet these adapted maturity dates.The climate is classified into (i) seasonality types, based on the coefficient of variation of monthly mean temperature and precipitation and (ii) temperature levels, based on the temperature of the warmest month as compared to the base and the optimum temperatures for the crop reproductive growth. Optimal temperatures for sowing, optimal temperature ranges for grain filling, as well as indicators of soil moisture conditions (based on precipitation/potential-evapotranspiration ratio (P/PET)), are defined as global parameters for each crop (Supplementary Table 1) and used as thresholds to identify the best timing for sowing and for the start or end of the crop grain-filling phase. To cope with fluctuations of daily values around these thresholds, mean daily temperature, precipitation and potential evapotranspiration are derived by linear interpolation between monthly values.We distinguish between spring and winter crop types. Maize, rice, sorghum, and soybean are simulated as spring crops only, for wheat we simulate both types. For spring crops, farmers sow the crops at the onset of the wet season (first day of the wettest 120 consecutive days), in case of prevailing precipitation seasonality, or on the day of the year when temperatures increase above crop-specific temperature threshold19 (Supplementary Table 1), in case of temperature-driven seasonality.For wheat, we distinguish three types: winter wheat with vernalization is chosen if monthly temperatures fall below 0 °C, but winter is neither too harsh (temperature of the coldest month is higher than −10 °C), nor too long (temperatures fall below the sowing temperature threshold (12 °C) after 15th September (North hemisphere) or 31st March (South hemisphere)19). Winter wheat without vernalization is grown if winters are mild (the temperature of the coldest month is higher than 0 °C) without dormancy. In this case, wheat is sown 75 days before the coldest month of the year. This rule was arbitrarily chosen based on observed wheat sowing dates in mild winter regions. If the conditions for growing any of the winter-wheat types are not met (winter too harsh and too long), then spring wheat (without vernalization) is chosen. Note that the computed sowing dates do not differ between rainfed and irrigated for any of the crops.The mean maturity date is chosen so that the crop grain-filling phase, the most critical for yield formation, occurs under the least stressful conditions possible in that location and climate as follows. Under precipitation seasonality, grain filling starts towards the end of the rainy season, when a P/PET threshold is crossed. Under temperature seasonality, (a) grain filling of spring crops starts in the warmest month of the year (if summer temperatures are optimal), or right after temperatures return within an optimal range; (b) grain filling of winter crops ends in the warmest month of the year (if summer temperatures are optimal), or right before temperatures exceed the optimal range; (c) eventually, maturity is advanced to escape terminal water stress. Note that the grain-filling phase has a static duration of 60 days for maize and 40 days for all the other crops. This assumption is based on empirical relationships between the total growth period and the post-flowering reproductive phase, showing that the partition between the vegetative and reproductive phase of grain crops follows a saturation curve that levels off after 90–100 days of total growth duration54. Different crops are assumed to have only one crop cycle (sowing-to-maturity) per year, therefore neither multi-cropping systems nor crop rotations are accounted for in the decision-making rules. A detailed description of the rules and parameterization can be found in refs. 19, 22.Simulated crop calendars reflect current farmers’ managementSimulated historical crop calendars, driven by the bias-corrected climate dataset WFDEI23, largely agree with observations11,12,13. We compare results both at the country and grid-cell level because, although the observed crop calendars used here are gridded datasets, their underlying sources are often reported per country. The country-level comparison highlights that the agreement is good for most countries, importantly, including those with large cropland area. The area-weighted Mean Absolute Error (MAE) is close or well below 30 days for all considered crops (Fig. 4). The simulated crop calendars compare well with the observed data also at the grid-cell level. Large areas, including major agricultural regions of importance for global yields, show deviations within ±15 days for both sowing and maturity dates (Supplementary Table 2 and Supplementary Figs. 21–24). However, evaluating the accuracy below 30 days is limited by the time resolution of the observations, which is either (i) monthly11 and converted by us into daily values, by taking the mid-day of the reported month, or (ii) daily12,13, but resulting from averages over large time windows (often  > 1 month). Overall, the accuracy of the model is in line with the original evaluations of this rule-base method19,22, as well as with other studies simulating average growing periods across large regions18,20.Fig. 4: Evaluation of simulated crop calendars.Country-level comparison of simulated and observed sowing (A) and maturity (B) dates (day of the year) for five crops. Each circle refers to a country and a crop, the size of the circle is scaled according to the cropland area per country. The area-weighted Mean Absolute Error (MAE, days) is reported for each crop. Crop-calendar simulations are based on WFDEI reanalysis climate forcing23 for the period 1979–2012. The observed crop calendar includes different sources11,12,13.Full size imageSimulation of daily crop phenology and yields with the LPJmL crop modelWe perform a modeling experiment across the global land grid at 0.5° × 0.5° resolution. We used the LPJmL5 crop model24,25 to simulate daily growth and phenological development of five crops, driven by climate projections from four General Circulation Models (GCMs) GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5 under the Representative Concentration Pathways 6.0 (RCP6.0) as provided in bias-adjusted form from the CMIP5 archive by the ISIMIP2b project42. Irrigated and rainfed production systems are simulated separately on their current harvested areas11, which is also used to compute total crop yields at grid-cell and global scale, as the product of yield by crop-specific area. A first 5000-year spin-up simulation is used to initialize all model pools (e.g., soil carbon and nitrogen content). A second spin-up simulation of 390 years is used to introduced effects of historical human-driven land-use change on these pools. A change in cropping area for the future scenarios is not considered in this study.Phenological development is simulated based on the thermal-time model, including the effect of vernalization. All crops are assumed to be insensitive to photoperiod, due to a lack of parameters for multiple-crops and global-scale simulations. Previous global studies15,18 that have focused on maize and wheat only, have found lower performances in the growing-period simulations when using a photo-thermal model, compared to a temperature-only driven approach and thus recommend caution when using the photoperiodic response. State-of-the-art global crop models13,16 also typically do not consider sensitivity to photoperiod or assume that the photoperiodic response of the cultivars chosen in each location are perfectly tuned to the given conditions.Sowing dates are prescribed based on the external rule-based algorithm. Crop cultivars are parametrized based on the phenological units required to reach the corresponding maturity dates (TUreq, °C days). In line with15, TUreq are derived consistently with the phenological module of the crop model LPJmL for each grid cell, crop, and rule-based computed growing period from the respective climate input. They are calculated as the sum of daily mean air temperature increments above a crop-specific base temperature (TU) (Supplementary Table 1) between rule-based sowing and maturity. In addition, winter-wheat cultivars require effective vernalization days (VUreq), that range between 0 (mild winters) and 70 (cold winters), depending on the temperature of the 5 coldest months (Eq. (1))15,18.$${{{{{mathrm{V}}}}}}{{{{{{mathrm{U}}}}}}}_{{{{{{{mathrm{req}}}}}}}}=frac{70}{5}times left(1-frac{{T}_{m}-3}{10-3}right)$$
(1)
where Tm is the mean temperature of the month.From the day of sowing, effective TU for phenological development are accumulated daily, as the difference between the mean air temperature on that day and the crop-specific base temperature for phenological development (Eq. (2)). The vernalization effectiveness is computed daily by a scaling factor (0–1), which is then multiplied to the TU (Eq. (2)). For crops that are insensitive to vernalization, VUd is set equal one.$${{{{{mathrm{T}}}}}}{{{{{{mathrm{U}}}}}}}_{{{{{{{mathrm{req}}}}}}}}=mathop{sum }_{d=1}^{{ndays}}left({max }left(0,{T}_{d}-{T}_{{base}}right)times mathop{sum }_{0}^{d}{{{{{mathrm{V}}}}}}{{{{{{mathrm{U}}}}}}}_{d}right)$$
(2)
where the scaling factor VUd is computed by a three-stage linear response function with a range of optimal temperatures (Eq. 3). Temperature for effective vernalization range between −4 °C and +17 °C, with an optimum range between 3 °C and 10 °C.$${{{{{{{mathrm{VU}}}}}}}}_{d}=left{begin{array}{cc}left({T}_{d}-left(-4right)right)/left(3-10right) & {{{{{{mathrm{if}}}}}}}-4 , < ,{T}_{d} , < , 3\ 1 & {{{{{{mathrm{if}}}}}}};3,le ,{T}_{d},le, 10\ left(17-{T}_{d}right)/left(17-10right) & {{{{{{mathrm{if}}}}}}};10 , < ,{T}_{d} , < , 17\ 0 & {{{{{{mathrm{otherwise}}}}}}}end{array}right}$$ (3) In this study, we have removed the effect of vernalization on slowing down TU accumulation until 10% of the total vernalization requirements is reached. In this way, the crop can accumulate both vernalization units and heat units in fall, so that there is some leaf growth before winter (in LPJmL, the LAI curve depends on accumulated heat units).The LPJmL model simulates phenology as one single phase from emergence to maturity. Although the flowering stage is not simulated as an explicit break point, the fraction of above-ground biomass that is allocated to the storage organs (fHI) depends on the phenological progress (fTUreq, fraction of TUreq that have been fulfilled), with the bulk of the storage organs start filling up after 40% of TUreq have been reached (Eq. (4)). In line with this, the LAI curve reaches a plateau when 45% (wheat) or 50% (other crops) of the TUreq are fulfilled, which could be considered a proxy of the flowering stage.$${{{{{{mathrm{fHI}}}}}}}=100times frac{{{{{{{{mathrm{fTU}}}}}}}}_{{{{{{{mathrm{req}}}}}}}}}{100times {{{{{{{mathrm{fTU}}}}}}}}_{{{{{{{mathrm{req}}}}}}}}+{{exp }}^{11.1-10.0times {{{{{{{mathrm{fTU}}}}}}}}_{{{{{{{mathrm{req}}}}}}}}}}$$ (4) Crop biomass growth is simulated by daily carbon accumulation and allocation to different plant organs (roots, leaves, storage organs, mobile reserves, and stem). The fraction of carbon allocated to each pool is a function of the fraction of completed phenological progress. Water stress increases allocation to the roots and reduces allocation to the leaves. The daily Net Primary Production (NPP) is the result of the Gross Primary Production (daily gross photosynthesis) reduced by the respiration costs. Gross photosynthesis is simulated as a function of absorbed photosynthetically active radiation, CO2 atmospheric mixing ratio, air temperature, day length, and canopy conductance. Photosynthesis rate is given by the minimum between light-limited and Rubisco-limited photosynthesis rates, with distinguished pathways for C3 and C4 crops. Respiration is tissue-specific and it is also driven by temperature. If accumulated NPP is insufficient to satisfy all organ demands, allocation follows a hierarchical order from roots, to leaves, to storage organs, and consequently penalizing the harvest index. Crops are subject to yield failure due to frost events (daily minimum temperature More

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

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

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

Phung, T. X., Nascimento, J. C. S., Novarro, A. J. & Wiens, J. J. Correlated and decoupled evolution of adult and larval body size in frogs. Proc. R. Soc. Lond. B 287, 20201474–10 (2020).
Google Scholar 
Hall, B. K. & Wake, M. H. The Origin and Evolution of Larval Forms (Gulf Professional Publishing, 1999).Hime, P. M. et al. Phylogenomics reveals ancient gene tree discordance in the amphibian tree of life. Syst. Biol. 70, 49–66 (2021).CAS 
PubMed 

Google Scholar 
Duellman, W. E. & Trueb, L. Biology of Amphibians (Johns Hopkins University Press, 1994).Nunes-de-Almeida, C. H., Batista Haddad, C. F. & Toledo, L. F. A revised classification of the amphibian reproductive modes. Salamandra 57, 413–427 (2021).
Google Scholar 
Vences, M. & Köhler, J. Global diversity of amphibians (Amphibia) in freshwater. Hydrobiologia 595, 569–580 (2007).
Google Scholar 
Blackburn, D. G. Evolution of vertebrate viviparity and specializations for fetal nutrition: a quantitative and qualitative analysis. J. Morphol. 276, 961–990 (2015).PubMed 

Google Scholar 
AmphibiaWeb. Electronic Database (University of California, Berkeley, CA, USA, 2019). https://amphibiaweb.org.Frost, D. R. Amphibian Species of the World: an Online Reference. Version 6.0 (date of access: 01.08.2019). Electronic Database (American Museum of Natural History, New York, USA, 2019). https://amphibiansoftheworld.amnh.org/.Bonett, R. M., Ledbetter, N. M., Hess, A. J., Herrboldt, M. A. & Denoël, M. Repeated ecological and life cycle transitions make salamanders an ideal model for evolution and development. Developmental Dynamics 251, 957–972 (2022).Salthe, S. N. Reproductive modes and the number and sizes of ova in the urodeles. Am. Midl. Naturalist 81, 467490 (1969).
Google Scholar 
Salthe, S. N. & Duellman, W. E. in Evolutionary Biology of the Anurans (ed. Vial, J. L.) 229–249 (University of Missouri Press Columbia, 1973).Haddad, C. & Prado, C. P. A. Reproductive modes in frogs and their unexpected diversity in the Atlantic Forest of Brazil. BioScience 55, 207–217 (2005).
Google Scholar 
Lutz, B. Ontogenetic evolution in frogs. Evolution 2, 29–39 (1948).CAS 
PubMed 

Google Scholar 
Crump, M. L. Anuran reproductive modes: evolving perspectives. J. Herpetol. 49, 1–16 (2015).
Google Scholar 
Schoch, R. Evolution of life cycles in early amphibians. Annu. Rev. Earth Planet. Sci. 37, 135–162 (2009).ADS 
CAS 

Google Scholar 
Meegaskumbura, M. et al. Patterns of reproductive-mode evolution in Old World tree frogs (Anura, Rhacophoridae). Zool. Scr. 44, 509–522 (2015).
Google Scholar 
Portik, D. M. & Blackburn, D. C. The evolution of reproductive diversity in Afrobatrachia: A phylogenetic comparative analysis of an extensive radiation of African frogs. Evolution 70, 2017–2032 (2016).PubMed 
PubMed Central 

Google Scholar 
San Mauro, D. et al. Life-history evolution and mitogenomic phylogeny of caecilian amphibians. Mol. Phylogenet. Evol. 73, 177–189 (2014).PubMed 

Google Scholar 
Pereira, E. B., Collevatti, R. G., de Carvalho Kokubum, M. N., de Oliveira Miranda, N. E. & Maciel, N. M. Ancestral reconstruction of reproductive traits shows no tendency toward terrestriality in leptodactyline frogs. BMC Evol. Biol. 15, 91 (2015).Gomez-Mestre, I., Pyron, R. A. & Wiens, J. J. Phylogenetic analyses reveal unexpected patterns in the evolution of reproductive modes in frogs. Evolution 66, 3687–3700 (2012).PubMed 

Google Scholar 
Wake, D. B. & Hanken, J. Direct development in the lungless salamanders: what are the consequences for developmental biology, evolution and phylogenesis? Int. J. Dev. Biol. 40, 859–869 (1996).CAS 
PubMed 

Google Scholar 
Dubois, A. Developmental pathway, speciation and supraspecific taxonomy in amphibians: 1. Why are there so many frog species in Sri Lanka? Alytes 22, 19–37 (2004).
Google Scholar 
Hedges, S. B., Duellman, W. E. & Heinicke, M. P. New World direct-developing frogs (Anura: Terrarana): molecular phylogeny, classification, biogeography, and conservation. Zootaxa 1737, 1–182 (2008).
Google Scholar 
Dugo-Cota, Á., Vilà, C., Rodríguez, A. & Gonzalez-Voyer, A. Ecomorphological convergence in Eleutherodactylus frogs: a case of replicate radiations in the Caribbean. Ecol. Lett. 22, 884–893 (2019).PubMed 

Google Scholar 
Simpson, G. G. The Major Features of Evolution (Columbia University Press, 1953).Vági, B., Végvári, Z., Liker, A., Freckleton, R. P. & Szekely, T. Parental care and the evolution of terrestriality in frogs. Proc. R. Soc. Lond. B 286, 20182737–10 (2019).
Google Scholar 
Furness, A. I. & Capellini, I. The evolution of parental care diversity in amphibians. Nat. Commun. 10, 1–12 (2019).CAS 

Google Scholar 
Furness, A. I., Venditti, C. & Capellini, I. Terrestrial reproduction and parental care drive rapid evolution in the trade-off between offspring size and number across amphibians. PLoS Biol. 20, e3001495 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wollenberg, K. C., Vieites, D. R., Glaw, F. & Vences, M. Speciation in little: the role of range and body size in the diversification of Malagasy mantellid frogs. BMC Evol. Biol. 11, 217 (2011).PubMed 
PubMed Central 

Google Scholar 
Cayuela, H. et al. Determinants and consequences of dispersal in vertebrates with complex life cycles: a review of pond-breeding amphibians. Q. Rev. Biol. 95, 1–36 (2020).
Google Scholar 
Phillimore, A. B., Freckleton, R. P., Orme, C. D. L. & Owens, I. P. F. Ecology predicts large‐scale patterns of phylogenetic diversification in birds. Am. Nat. 168, 220–229 (2006).PubMed 

Google Scholar 
Chen, J.-M. et al. An integrative phylogenomic approach illuminates the evolutionary history of Old World tree frogs (Anura: Rhacophoridae). Mol. Phylogenet. Evol. 145, 106724 (2020).PubMed 

Google Scholar 
Zimkus, B. M., Lawson, L., Loader, S. P. & Hanken, J. Terrestrialization, miniaturization and rates of diversification in African Puddle Frogs (Anura: Phrynobatrachidae). PLoS ONE 7, e35118 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moen, D. S. Improving inference and avoiding over-interpretation of hidden-state diversification models: specialized plant breeding has no effect on diversification in frogs. Evolution 76, 373–384 (2022).PubMed 

Google Scholar 
Eastman, J. M. & Storfer, A. Correlations of life-history and distributional-range variation with salamander diversification rates: evidence for species selection. Syst. Biol. 60, 503–518 (2011).PubMed 

Google Scholar 
Bonett, R. M., Steffen, M. A., Lambert, S. M., Wiens, J. J. & Chippindale, P. T. Evolution of paedomorphosis in plethodontid salamanders: ecological correlates and re-evolution of metamorphosis. Evolution 68, 466–482 (2014).PubMed 

Google Scholar 
Akaike, H. A new look at the statistical-model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
MathSciNet 
MATH 

Google Scholar 
Wagenmakers, E.-J. & Farrell, S. AIC model selection using Akaike weights. Psychon. Bull. Rev. 11, 192–196 (2004).PubMed 

Google Scholar 
Beaulieu, J. M., Oliver, J. C., O’Meara, B. & Boyko, J. R package ‘corHMM’: hidden markov models of character evolution. (2020).Pagel, M. & Meade, A. BayesTraits, version 4. University of Reading, Berkshire, UK. http://www.evolution.rdg.ac.uk (2022).Pagel, M. & Meade, A. Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo. Am. Nat. 167, 808–825 (2006).PubMed 

Google Scholar 
Maddison, W., Midford, P. & Otto, S. Estimating a binary character’s effect on speciation and extinction. Syst. Biol. 56, 701–710 (2007).PubMed 

Google Scholar 
FitzJohn, R. G., Maddison, W. P. & Otto, S. P. Estimating trait-dependent speciation and extinction rates from incompletely resolved phylogenies. Syst. Biol. 58, 595–611 (2009).PubMed 

Google Scholar 
Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).PubMed 

Google Scholar 
Herrera-Alsina, L., van Els, P. & Etienne, R. S. Detecting the dependence of diversification on multiple traits from phylogenetic trees and trait data. Syst. Biol. 68, 317–328 (2018).
Google Scholar 
Strathmann, R. R. Hypotheses on the origins of marine larvae. Annu. Rev. Ecol. Syst. 24, 89–117 (1993).
Google Scholar 
Wray, G. A. Evolution of larvae and developmental modes. In Ecology of Marine Invertebrate Larvae. (ed. McEdward, L.) 413–447 (CRC Press, 1995).Raff, R. A. Origins of the other metazoan body plans: the evolution of larval forms. Philos. T R. Soc. B 363, 1473–1479 (2008).
Google Scholar 
Collin, R. & Moran, A. in Evolutionary Ecology of Marine Invertebrate Larvae 50–66 (Oxford University Press, 2018).Collin, R. & Miglietta, M. P. Reversing opinions on Dollo’s Law. Trends Ecol. Evol. 23, 602–609 (2008).PubMed 

Google Scholar 
Wiens, J. J. Re-evolution of lost mandibular teeth in frogs after more than 200 million years, and re-evaluating Dollo’s law. Evolution 65, 1283–1296 (2011).PubMed 

Google Scholar 
Wiens, J. J., Kuczynski, C. A., Duellman, W. E. & Reeder, T. W. Loss and re-evolution of complex life cycles in marsupial frogs: does ancestral trait reconstruction mislead? Evolution 61, 1886–1899 (2007).CAS 
PubMed 

Google Scholar 
Chippindale, P. T., Bonett, R. M., Baldwin, A. S. & Wiens, J. J. Phylogenetic evidence for a major reversal of life-history evolution in plethodontid salamanders. Evolution 58, 2809–2815 (2004).CAS 
PubMed 

Google Scholar 
Castroviejo-Fisher, S. et al. Phylogenetic systematics of egg-brooding frogs (Anura: Hemiphractidae) and the evolution of direct development. Zootaxa 4004, 1–75 (2015).PubMed 

Google Scholar 
Naumann, B., Schweiger, S., Hammel, J. U. & Müller, H. Parallel evolution of direct development in frogs—skin and thyroid gland development in African Squeaker Frogs (Anura: Arthroleptidae: Arthroleptis). Dev. Dyn. 250, 584–600 (2021).PubMed 

Google Scholar 
Goldberg, J., Taucce, P. P. G., Quinzio, S. I., Haddad, C. F. B. & Candioti, F. V. Increasing our knowledge on direct-developing frogs: the ontogeny of Ischnocnema henselii (Anura: Brachycephalidae). Zool. Anz. 284, 78–87 (2020).
Google Scholar 
Wassersug, R. J. & Duellman, W. E. Oral structures and their development in egg-brooding hylid frog embryos and larvae: evolutionary and ecological implications. J. Morphol. 182, 1–37 (1984).PubMed 

Google Scholar 
Kerney, R. R., Blackburn, D. C., Müller, H. & Hanken, J. Do larval traits re-evolve? Evidence from the embryogenesis of a direct-developing salamander, Plethodon cinereus. Evolution 66, 252–262 (2011).PubMed 

Google Scholar 
Theska, T. Musculoskeletal development of the Central African caecilian Idiocranium russeli (Amphibia: Gymnophiona: Indotyphlidae) and its bearing on the re-evolution of larvae in caecilian amphibians. Zoomorphology 138, 137–158 (2019).
Google Scholar 
Laslo, M., Denver, R. J. & Hanken, J. Evolutionary conservation of thyroid hormone receptor and deiodinase expression dynamics in ovo in a direct-developing frog, Eleutherodactylus coqui. Front. Endocrinol. 10, 307 (2019).
Google Scholar 
Gao, W. et al. Genomic and transcriptomic investigations of the evolutionary transition from oviparity to viviparity. Proc. Natl Acad. Sci. USA 116, 3646–3655 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Altig, R. & Crother, B. I. The evolution of three deviations from the biphasic anuran life cycle: alternatives to selection. Herpetol. Rev. 37, 321–356 (2006).
Google Scholar 
Venturelli, D. P., da Silva, W. R. & Giaretta, A. A. Tadpoles’ resistance to desiccation in species of Leptodactylus (Anura, Leptodactylidae). J. Herpetol. 55, 265–270 (2021).
Google Scholar 
Seymour, R. S. Respiration of aquatic and terrestrial amphibian embryos. American Zoologist 39, 261–270 (1999).Blackburn, D. G. Convergent evolution of viviparity, matrotrophy, and specializations for fetal nutrition in reptiles and other vertebrates. Am. Zool. 32, 313–321 (1992).
Google Scholar 
Buckley, D., Alcobendas, M., García-París, M. & Wake, M. H. Heterochrony, cannibalism, and the evolution of viviparity in Salamandra salamandra. Evol. Dev. 9, 105–115 (2007).PubMed 

Google Scholar 
Kusrini, M. D., Rowley, J. J. L., Khairunnisa, L. R., Shea, G. M. & Altig, R. The reproductive biology and larvae of the first tadpole-bearing frog, Limnonectes larvaepartus. PLoS ONE 10, e116154–e116159 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Lanza, B. & Leo, P. Sul primo caso sicuro di riproduzione vivipara nel genere Speleomantes. 1–54 (2000).Lunghi, E. et al. Comparative reproductive biology of European cave salamanders (Genus Hydromantes): nesting selection and multiple annual breeding. Salamandra 54, 101–108 (2018).
Google Scholar 
Liedtke, H. C. et al. Terrestrial reproduction as an adaptation to steep terrain in African toads. Proc. R. Soc. Lond. B 284, 20162598–20162599 (2017).
Google Scholar 
Wake, M. H. The reproductive biology of Eleutherodactylus jasperi (Amphibia, Anura, Leptodactylidae), with comments on the evolution of live-bearing systems. J. Herpetol. 12, 121–133 (1978).
Google Scholar 
Jennings, D. H. & Hanken, J. Mechanistic basis of life history evolution in anuran amphibians: thyroid gland development in the direct-developing frog, Eleutherodactylus coqui. Gen. Comp. Endocr. 111, 225–232 (1998).CAS 
PubMed 

Google Scholar 
Callery, E. M. & Elinson, R. P. Thyroid hormone-dependent metamorphosis in a direct developing frog. Proc. Natl Acad. Sci. USA 97, 2615–2620 (2000).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Callery, E. M., Hung, F. & Elinson, R. P. Frogs without polliwogs: evolution of anuran direct development. BioEssays 23, 233–241 (2001).CAS 
PubMed 

Google Scholar 
Buckley, L. B. & Jetz, W. Environmental and historical constraints on global patterns of amphibian richness. Proc. R. Soc. Lond. B 274, 1167–1173 (2007).
Google Scholar 
Pyron, R. A. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. Lond. B 280, 20131622 (2013).
Google Scholar 
Gómez-Rodríguez, C., Baselga, A. & Wiens, J. J. Is diversification rate related to climatic niche width? Glob. Ecol. Biogeogr. 24, 383–395 (2015).
Google Scholar 
Moen, D. S. & Wiens, J. J. Microhabitat and climatic niche change explain patterns of diversification among frog families. Am. Nat. 190, 29–44 (2017).PubMed 

Google Scholar 
Kozak, K. H. & Wiens, J. J. Accelerated rates of climatic-niche evolution underlie rapid species diversification. Ecol. Lett. 13, 1378–1389 (2010).PubMed 

Google Scholar 
Jaramillo, A. F. et al. Vastly underestimated species richness of Amazonian salamanders (Plethodontidae: Bolitoglossa) and implications about plethodontid diversification. Mol. Phylogenet. Evol. 149, 106841 (2020).PubMed 

Google Scholar 
Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).PubMed 

Google Scholar 
Bars-Closel, M., Kohlsdorf, T., Moen, D. S. & Wiens, J. J. Diversification rates are more strongly related to microhabitat than climate in squamate reptiles (lizards and snakes). Evolution 71, 2243–2261 (2017).PubMed 

Google Scholar 
Cyriac, V. P. & Kodandaramaiah, U. Digging their own macroevolutionary grave: fossoriality as an evolutionary dead end in snakes. J. Evolution. Biol. 31, 587–598 (2018).CAS 

Google Scholar 
Zamudio, K. R., Bell, R. C., Nali, R. C., Haddad, C. F. B. & Prado, C. P. A. Polyandry, predation, and the evolution of frog reproductive modes. Am. Nat. 188, S41–S61 (2016).PubMed 

Google Scholar 
Lion, M. B. et al. Global patterns of terrestriality in amphibian reproduction. Glob. Ecol. Biogeogr. 4, 679–13 (2019).
Google Scholar 
Müller, H. et al. Forests as promoters of terrestrial life-history strategies in East African amphibians. Biol. Lett. 9, 20121146–20121146 (2013).PubMed 
PubMed Central 

Google Scholar 
Velo-Antón, G., García-París, M., Galán, P. & Cordero Rivera, A. The evolution of viviparity in Holocene islands: ecological adaptation versus phylogenetic descent along the transition from aquatic to terrestrial environments. J. Zool. Syst. Evol. Res. 45, 345–352 (2007).
Google Scholar 
Liedtke, H. C. AmphiNom: an amphibian systematics tool. Syst. Biodivers. 17, 1–6 (2019).
Google Scholar 
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020). http://www.R-project.org/.IUCN. IUCN 2020. The IUCN Red List of Threatened Species. Version 2020-2. https://www.iucnredlist.org (2019).AmphibiaChina. The database of Chinese amphibians. Electronic Database (Kunming Institute of Zoology (CAS), Kunming, Yunnan, China, 2019) http://www.amphibiachina.org/.Ron, S. R., Yanez-Muñoz, M. H., Merino-Viteri, A. & Ortiz, D. A. Anfibios del Ecuador. Version 2019.0 (Museo de Zoología, Pontificia Universidad Católica del Ecuador, 2019). https://bioweb.bio/faunaweb/amphibiaweb.Greven, H. In Reproductive Biology and Phylogeny of Urodela 447–475 (Taylor & Francis, 2003).Marks, S. B. & Collazo, A. Direct development in Desmognathus aeneus (Caudata: Plethodontidae): a staging table. Copeia 1998, 637–648 (1998).
Google Scholar 
Müller, H., Loader, S. P., Ngalason, W., Howell, K. M. & Gower, D. J. Reproduction in brevicipitid frogs (Amphibia: Anura: Brevicipitidae)—evidence from Probreviceps m. macrodactylus. Copeia 2007, 726–733 (2007).
Google Scholar 
Velo-Antón, G., Santos, X., Sanmartín-Villar, I., Cordero-Rivera, A. & Buckley, D. Intraspecific variation in clutch size and maternal investment in pueriparous and larviparous Salamandra salamandra females. Evol. Ecol. 29, 185–204 (2014).
Google Scholar 
Beaulieu, J. M., O’Meara, B. C. & Donoghue, M. J. Identifying hidden rate changes in the evolution of a binary morphological character: the evolution of plant habit in campanulid angiosperms. Systematic biology 62, 725–737 (2013).Pupko, T., Pe’er, I., Shamir, R. & Graur, D. A fast algorithm for joint reconstruction of ancestral amino acid sequences. Mol. Biol. Evol. 17, 890–896 (2000).CAS 
PubMed 

Google Scholar 
Bollback, J. P. SIMMAP: stochastic character mapping of discrete traits on phylogenies. BMC Bioinforma. 7, 88 (2006).
Google Scholar 
Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R. News 6, 7–11 (2006).
Google Scholar 
Tuffley, C. & Steel, M. Modeling the covarion hypothesis of nucleotide substitution. Math. Biosci. 147, 63–91 (1998).MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Xie, W., Lewis, P. O., Fan, Y., Kuo, L. & Chen, M. H. Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Syst. Biol. 60, 150–160 (2011).PubMed 

Google Scholar 
Nakov, T., Beaulieu, J. M. & Alverson, A. J. Diatoms diversify and turn over faster in freshwater than marine environments. Evolution 73, 2497–2511 (2019).PubMed 

Google Scholar 
Etienne, R. S. et al. Diversity-dependence brings molecular phylogenies closer to agreement with the fossil record. Proc. R. Soc. Lond. B 279, 1300–1309 (2011).
Google Scholar  More

Hobbs, R. J. et al. Novel ecosystems: Theoretical and management aspects of the new ecological world order. Glob. Ecol. Biogeogr. 15, 1–7 (2006).
Google Scholar 
Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).
Google Scholar 
Mayfield, M. M. et al. What does species richness tell us about functional trait diversity? Predictions and evidence for responses of species and functional trait diversity to land-use change. Glob. Ecol. Biogeogr. 19, 423–431 (2010).
Google Scholar 
Zobel, M. The species pool concept as a framework for studying patterns of plant diversity. J. Veg. Sci. 27, 8–18 (2016).
Google Scholar 
Birkhofer, K. et al. Land-use type and intensity differentially filter traits in above- and below-ground arthropod communities. J. Anim. Ecol. 86, 511–520 (2017).PubMed 

Google Scholar 
Temperton, V. M. Assembly Rules and Restoration Ecology: Bridging the Gap Between Theory and Practice (Island Press, 2004).
Google Scholar 
Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).PubMed 

Google Scholar 
Gascon, C. et al. Matrix habitat and species richness in tropical forest remnants. Biol. Conserv. 91, 223–229 (1999).
Google Scholar 
Filloy, J., Zurita, G. A., Corbelli, J. M. & Bellocq, M. I. On the similarity among bird communities: Testing the influence of distance and land use. Acta Oecol. 36, 333–338 (2010).ADS 

Google Scholar 
Vaccaro, A., Filloy, J. & Bellocq, M. What land use better preserves the functional and taxonomic diversity of birds in a grassland biome?. Avian Conserv. Ecol. 14, 1 (2019).
Google Scholar 
Vaccaro, A. S. & Bellocq, M. I. Diversidad taxonómica y funcional de aves: Diferencias entre hábitats antrópicos en un bosque subtropical. Ecol. Austral 29, 391–404 (2019).
Google Scholar 
Sekercioglu, C. H. Bird functional diversity and ecosystem services in tropical forests, agroforests and agricultural areas. J. Ornithol. 153, 153–161 (2012).
Google Scholar 
Zurita, G. A. & Bellocq, M. I. Bird assemblages in anthropogenic habitats: Identifying a suitability gradient for native species in the Atlantic Forest. Biotropica 44, 412–419 (2012).
Google Scholar 
Azpiroz, A. B. et al. Ecology and conservation of grassland birds in southeastern South America: A review. J. Field Ornithol. 83, 217–246 (2012).
Google Scholar 
Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).PubMed 

Google Scholar 
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).
Google Scholar 
Corbelli, J. M. et al. Integrating taxonomic, functional and phylogenetic beta diversities: Interactive effects with the biome and land use across taxa. PLoS ONE 10, 1–17 (2015).
Google Scholar 
Purschke, O. et al. Contrasting changes in taxonomic, phylogenetic and functional diversity during a long-term succession: Insights into assembly processes. J. Ecol. 101, 857–866 (2013).
Google Scholar 
Srivastava, D. S., Cadotte, M. W., Macdonald, A. A. M., Marushia, R. G. & Mirotchnick, N. Phylogenetic diversity and the functioning of ecosystems. Ecol. Lett. 15, 637–648 (2012).PubMed 

Google Scholar 
Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715 (2009).PubMed 

Google Scholar 
Mouquet, N. et al. Ecophylogenetics: Advances and perspectives. Biol. Rev. 87, 769–785 (2012).PubMed 

Google Scholar 
Ackerly, D. D., Schwilk, D. W. & Webb, C. O. Niche evolution and adaptive radiation: Testing the order of trait divergence. Ecology 87, S50–S61 (2006).CAS 
PubMed 

Google Scholar 
Cavender-Bares, J., Ackerly, D. D., Baum, D. A. & Bazzaz, F. A. Phylogenetic overdispersion in Floridian oak communities. Am. Nat. 163, 823–843 (2004).CAS 
PubMed 

Google Scholar 
Losos, J. B. et al. Niche lability in the evolution of a Caribbean lizard community. Nature 424, 542–545 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Stevens, R. D., Gavilanez, M. M., Tello, J. S. & Ray, D. A. Phylogenetic structure illuminates the mechanistic role of environmental heterogeneity in community organization. J. Anim. Ecol. 81, 455–462 (2012).PubMed 

Google Scholar 
García-Navas, V. & Thuiller, W. Farmland bird assemblages exhibit higher functional and phylogenetic diversity than forest assemblages in France. J. Biogeogr. 47, 2392–2404 (2020).
Google Scholar 
Henwood, W. D. Toward a strategy for the conservation and protection of the world’s temperate grasslands. Univ. Neb. Press 20, 121–134 (2010).ADS 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Landi, M., Oesterheld, M. & Deregibus, V. A. Manual de especies forrajeras de los pastizales naturales de Entre Ríos (1987).Viglizzo, E. F. et al. Ecological lessons and applications from one century of low external-input farming in the pampas of Argentina. Agric. Ecosyst. Environ. 83, 65–81 (2001).
Google Scholar 
Galindo Leal, C. & de Gusmão Câmara, I. The Atlantic Forest of South America: Biodiversity Status, Threats and Outlook (Island Press, 2003).
Google Scholar 
Oliveira-Filho, A. T. & Fontes, M. A. L. Patterns of floristic differentiation among Atlantic Forests in Southeastern Brazil and the influence of climate. Biotropica 32, 793–810 (2000).
Google Scholar 
DeGraaf, R. M., Geis, A. D. & Healy, P. A. Bird population and habitat surveys in urban areas. Landsc. Urban Plan. 21, 181–188 (1991).
Google Scholar 
Ralph, C. J. et al. Manual de métodos de campo para el monitoreo de aves terrestres. Pacific Southwest Research Station, Forest Service, U.S. Department of Agriculture, Albany, CA 46 http://www.srs.fs.usda.gov/pubs/31462. https://doi.org/10.3145/epi.2006.jan.15 (1996).Bibby, C., Jones, M. & Marsden, S. Expedition field techniques: Bird surveys. in (ed. Society, R. G.) (1998).Zurita, G. A. & Bellocq, M. I. Spatial patterns of bird community similarity: Bird responses to landscape composition and configuration in the Atlantic forest. Landsc. Ecol. 25, 147–158 (2010).
Google Scholar 
Koper, N. & Schmiegelow, F. K. K. A multi-scaled analysis of avian response to habitat amount and fragmentation in the Canadian dry mixed-grass prairie. Landsc. Ecol. 21, 1045 (2006).
Google Scholar 
Xeno-canto-Foundation. Xeno-canto website. https://www.xeno-canto.org (2018).Petchey, O. L. & Gaston, K. J. Functional diversity (FD), species richness and community composition. Ecol. Lett. 5, 402–411 (2002).
Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org (2018).Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 

Google Scholar 
Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: Software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).CAS 
PubMed 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. R Core Team. nlme: Linear and Nonlinear mixed effects models. R package version 3.1–117. (2014).Lenth, R. V. Least-Squares Means: The R Package lsmeans. J. Stat. Softw. https://doi.org/10.18637/jss.v069.i01 (2016).Article 

Google Scholar 
Cadotte, M. W. & Tucker, C. M. Should environmental filtering be abandoned?. Trends Ecol. Evol. 32, 429–437 (2017).PubMed 

Google Scholar 
Concepción, E. D. et al. Contrasting trait assembly patterns in plant and bird communities along environmental and human-induced land-use gradients. Ecography 40, 753–763 (2016).
Google Scholar 
Cerezo, A., Conde, M. C. & Poggio, S. L. Pasture area and landscape heterogeneity are key determinants of bird diversity in intensively managed farmland. Biodivers. Conserv. 20, 2649–2667 (2011).
Google Scholar 
Pretelli, M. G., Isacch, J. P. & Cardoni, D. A. Year-round abundance, richness and nesting of the bird assemblage of tall grasslands in the south-east Pampas region, Argentina. Ardeola 60, 327–343 (2013).
Google Scholar 
Molinari, R. L. Biografía de la Pampa: 4 siglos de historia del campo argentino (Fundación Colombina “V Centenario,” 1987).
Google Scholar 
Filloy, J. & Bellocq, M. I. Patterns of bird abundance along the agricultural gradient of the Pampean region. Agric. Ecosyst. Environ. 120, 291–298 (2007).
Google Scholar 
Le Viol, I. et al. More and more generalists: Two decades of changes in the European avifauna. Biol. Lett. 8, 780–782 (2012).PubMed 
PubMed Central 

Google Scholar 
Concepción, E. D., Moretti, M., Altermatt, F., Nobis, M. P. & Obrist, M. K. Impacts of urbanisation on biodiversity: The role of species mobility, degree of specialisation and spatial scale. Oikos 124, 1571–1582 (2015).
Google Scholar 
Emerson, B. C. & Gillespie, R. G. Phylogenetic analysis of community assembly and structure over space and time. Trends Ecol. Evol. 23, 619–630 (2008).PubMed 

Google Scholar 
Morse, N. B. et al. Novel ecosystems in the Anthropocene: A revision of the novel ecosystem concept for pragmatic applications. Ecol. Soc. 19, 12 (2014).
Google Scholar 
Loyn, R. H., McNabb, E. G., Macak, P. & Noble, P. Eucalypt plantations as habitat for birds on previously cleared farmland in south-eastern Australia. Biol. Conserv. 137, 533–548 (2007).
Google Scholar 
Marsden, S., Whiffin, M. & Galetti, M. Bird diversity and abundance in forest fragments and Eucalyptus plantations around an Atlantic forest reserve, Brazil. Biodivers. Conserv. 10, 737–751 (2001).
Google Scholar 
Zurita, G. A., Rey, N., Varela, D. M., Villagra, M. & Bellocq, M. I. Conversion of the Atlantic Forest into native and exotic tree plantations: Effects on bird communities from the local and regional perspectives. For. Ecol. Manag. 235, 164–173 (2006).
Google Scholar 
Flynn, D. F. B., Mirotchnick, N., Jain, M., Palmer, M. I. & Naeem, S. Functional and phylogenetic diversity as predictors of biodiversity ecosystem-function. Ecology 92, 1573–1581 (2011).PubMed 

Google Scholar 
Sol, D. et al. The worldwide impact of urbanisation on avian functional diversity. Ecol. Lett. 23, 962–972 (2020).PubMed 

Google Scholar 
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar 
Palacio, F. X., Ibañez, L. M., Maragliano, R. E. & Montalti, D. Urbanization as a driver of taxonomic, functional, and phylogenetic diversity losses in bird communities. Can. J. Zool. 96, 1114–1121 (2018).
Google Scholar 
Sol, D., Bartomeus, I., González-Lagos, C. & Pavoine, S. Urbanisation and the loss of phylogenetic diversity in birds. Ecol. Lett. 20, 721–729 (2017).PubMed 

Google Scholar 
Luck, G. W., Carter, A. & Smallbone, L. Changes in bird functional diversity across multiple land uses: Interpretations of functional redundancy depend on functional group identity. PLoS ONE 8, e63671 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Coetzee, B. W. T. & Chown, S. L. Land-use change promotes avian diversity at the expense of species with unique traits. Ecol. Evol. 6, 7610–7622 (2016).PubMed 
PubMed Central 

Google Scholar  More

These authors contributed equally: Laura H. Antão, Benjamin Weigel.These authors jointly supervised this work: Tomas Roslin, Anna-Liisa Laine.Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, FinlandLaura H. Antão, Benjamin Weigel, Giovanni Strona, Maria Hällfors, Elina Kaarlejärvi, Otso Ovaskainen, Marjo Saastamoinen, Jarno Vanhatalo, Tomas Roslin & Anna-Liisa LaineDepartment of Biological Sciences, University of South Carolina, Columbia, SC, USATad DallasDepartment of Biology, Lund University, Lund, SwedenØystein H. OpedalFinnish Environment Institute (SYKE), Helsinki, FinlandJanne Heliölä, Mikko Kuussaari, Juha Pöyry & Kristiina VuorioNatural Resources Institute Finland (Luke), Helsinki, FinlandHeikki Henttonen, Otso Huitu, Andreas Lindén, Päivi Merilä, Maija Salemaa & Tiina TonteriSection of Ecology, Department of Biology, University of Turku, Turku, FinlandErkki KorpimäkiFinnish Museum of Natural History, University of Helsinki, Helsinki, FinlandAleksi LehikoinenKainuu Centre for Economic Development, Transport and the Environment, Kajaani, FinlandReima LeinonenUniversity of Helsinki, Helsinki, FinlandHannu PietiäinenDepartment of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, FinlandOtso OvaskainenCentre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, NorwayOtso OvaskainenHelsinki Institute of Life Science, University of Helsinki, Helsinki, FinlandMarjo SaastamoinenDepartment of Mathematics and Statistics, Faculty of Science, University of Helsinki, Helsinki, FinlandJarno VanhataloSpatial Foodweb Ecology Group, Department of Agricultural Sciences, University of Helsinki, Helsinki, FinlandTomas RoslinSpatial Foodweb Ecology Group, Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, SwedenTomas RoslinDepartment of Evolutionary Biology and Environmental Studies, University of Zürich, Zürich, SwitzerlandAnna-Liisa Laine More