Philippa Kaur

Soil studies were carried out on 115,896.2 hectares of agricultural lands in fifteen villages of the municipal district obtained by subtracting from the available area of the village industrial lands, populated areas, forest plots occupied by water, etc.As a result of the land reform and redistribution of land for various purposes for the period from 1972 to 2021, the area of agricultural land decreased by 12.7% compared to the data of the previous survey.In the research area, the largest territories are occupied by black soils, which amount to 52,826.24 ha, including bleached soils—42,605.9 ha, alkaline – 6983.8 ha and shortened – 3236.54 ha. Slightly inferior to the black soils are dark gray forest soils with an area of 37,043.63 hectares, alluvial—12,287.4 hectares, gray forest—6371.96 hectares and forest soils of a rooted profile – 5058.94 hectares. The share of sod-carbonate soils accounts for 7792.7 hectares of land, which is 6.2%. The gradation did not include the soils of the ravine-beam complex, sand and gravel masses, existing ravines and disturbed lands, and quarries that occupy 5,452.4 hectares of territory (4.3%).One of the important indicators of soils, especially used in agricultural production, is the humus state. Thus, over 49 years there has been a slight decrease in the area under obese (high-humus) soils in the hectare ratio, due to a general decrease in the area of farmland, but in the context of the security group, they have increased by 1.3% (Table 1). The remaining levels of security have remained almost at the same level. The increase in the amount of fat chernozems was facilitated by the withdrawal of arable land from circulation and their transfer to perennial plantations. Earlier researches conducted on experimental fields of the Bashkir State Agrarian University identified and revealed changes in the quantitative and qualitative composition of organic matter from 15 to 30% when introducing a land plot for arable land26. To preserve and improve soil fertility, it is recommended to carry out a complex of agrotechnical, agrochemical and reclamation measures and the use of various meliorants, organic and mineral fertilizers27.Studies of the capacity of the humus horizon have shown that low–sized soils have become the most widespread—69,660.2 hectares or 60.1% of the total area of agricultural land (Fig. 2). A smaller area is occupied by medium-sized soils – 38,128.7 hectares (32.9%), not included in the gradation – 8107.3 hectares or 7.0%, respectively. It should be noted that the specific gravity of the soil of the ravine-beam complex, sand and gravel masses, active ravines and disturbed lands, and quarries increased by 2.5%.Figure 2Distribution of soils by humus horizon thickness by region.Full size imageThe granulometric composition of the soil is also of great agronomic importance28. Physical, physico-chemical, physico-mechanical properties and water, air, and nutrient regimes of soils depend on it29,30. In the Salavatskiy district there were practically no changes in soil areas in terms of granulometric composition, mainly clay soil varieties predominate. According to the mechanical composition of the soil there were distributed as follows: light clay – 71,807.38 ha or 62% (in 1972, 86,375 ha or 65.1%) of the total area of agricultural land and heavy loamy – 34,745.24 ha (30%) (in 1972—39,614 ha or 29.8%). The share of medium-loamy varieties accounts for 0.8% (in 1972—0.84%) (Fig. 3).Figure 3Distribution of Salavatskiy district soil areas by granulometric composition, %.Full size imageThe gradation did not include 8362.27 hectares of land. Heavy loamy, medium clay, sandy loam and sandy soils have not been identified.All arable soils of the analyzed territory are slightly susceptible to erosion processes, the processes of water and, to a lesser extent, wind erosion have developed. 67,445.21 hectares of land, or 58.2% (in 1972, 77,702 hectares) of the total area of agricultural lands are occupied under lightly washed soils, the share of medium and heavily washed accounts for 3.9% and 0.1%, respectively. Unwashed soils are distributed on 36,985.46 hectares (31.9%) (Table 2).Table 2 Soil areas by category of erosion feature (Salavatskiy district of the Republic of Bashkortostan).Full size tableAccording to the results of the field research and laboratory agrochemical analyses of soils, land refinements related to agricultural land were carried out. The basis for correcting and digitizing the contours of soil varieties were in the maps made in 1972 (Fig. 4).Figure 4Soil map within the boundaries of the Salavatskiy district of the Republic of Bashkortostan, 1972.Full size imageDigitization included scanning the topographic basis, then assigning coordinates to a raster image, decrypting and digitizing orthophotos (Fig. 5).Figure 5Orthophotoplan within the boundaries of the Salavatskiy district of the Republic of Bashkortostan, 2007.Full size imageAfter the carried-out activities, a soil map was obtained in the digital format of the Mapinfo program, after which it was converted into a raster basis with reference to the local coordinate system MSK 02 zone 1. The digitization of the 1972 soil map was carried out manually by outlining the contours of the topographic base and the scanned map.During digitization, information partially lost due to its wear and distortion during scanning was restored. A necessary condition is the use of the originals of the soil maps of the previous survey (1972).As a planned basis on which the created layers can be opened and information on soils can be obtained, a raster basis was ordinated into a local coordinate system (Fig. 6).Figure 6Completed soil map within the boundaries of the Salavat district of the Republic of Belarus, 2021.Full size imageThe result of the conducted research is the developed electronic digital soil map of the municipal district of Salavatskiy district which unites 15 rural settlements. The electronic soil map is presented in the form of a complex of electronic layers with the names of the type and subtype of soils, soil variety, mechanical or granulometric composition, soil-forming and underlying rocks. It also includes indicators of organic carbon, humus, mobile phosphorus, exchangeable potassium, soil acidity by pH value and the capacity of the humus-accumulative horizon. More

Ribic, C. A., Thompson, F. R. & Pietz, P. J. Video Surveillance of Nesting Birds (University of California Press, 2012).Book 

Google Scholar 
O’Brien, T. G. & Kinnaird, M. F. A picture is worth a thousand words: The application of camera trapping to the study of birds. Bird Conserv. Int. 18, S144–S162 (2008).Article 

Google Scholar 
Kristan, D. M., Golightly Jr, R. T. & Tomkiewicz Jr, S. M. A solar-powered transmitting video camera for monitoring raptor nests. Wildl. Soc. Bull. 24, 284–290 (1996).
Google Scholar 
Grubb, T. An infrared video camera system for monitoring diurnal and nocturnal raptors. J. Raptor Res. 32, 290–296 (1998).
Google Scholar 
Margalida, A. et al. A solar-powered transmitting video camera for monitoring cliff-nesting raptors. J. Field Ornithol. 77, 7–12 (2006).Article 

Google Scholar 
Bolton, M., Butcher, N., Sharpe, F., Stevens, D. & Fisher, G. Remote monitoring of nests using digital camera technology. J. Field Ornithol. 78, 213–220 (2007).Article 

Google Scholar 
Pierce, A. J. & Pobprasert, K. A portable system for continuous monitoring of bird nests using digital video recorders. J. Field Ornithol. 78, 322–328 (2007).Article 

Google Scholar 
Benson, T. J., Brown, J. D. & Bednarz, J. C. Identifying predators clarifies predictors of nest success in a temperate passerine. J. Anim. Ecol. 79, 225–234 (2010).Article 

Google Scholar 
Lewis, S. B., Fuller, M. R. & Titus, K. A comparison of 3 methods for assessing raptor diet during the breeding season. Wildl. Soc. Bull. 32, 373–385 (2004).Article 

Google Scholar 
Rogers, A. S. Quantifying Northern Goshawk diets using remote cameras and observations from blinds. J. Raptor Res. 39, 303–309 (2005).ADS 

Google Scholar 
Tornberg, R. & Reif, V. Assessing the diet of birds of prey: A comparison of prey items found in nests and images. Ornis Fenn. 84, 21 (2007).
Google Scholar 
López-López, P. & Urios, V. Use of digital trail cameras to study Bonelli’s eagle diet during the nestling period. Ital. J. Zool. 77, 289–295 (2010).Article 

Google Scholar 
Harrison, J. T., Kochert, M. N., Pauli, B. P. & Heath, J. A. Using motion-activated trail cameras to study diet and productivity of cliff-nesting Golden Eagles. J. Raptor Res. 53, 26–37 (2019).Article 

Google Scholar 
McRae, S. B., Weatherhead, P. J. & Montgomerie, R. American robin nestlings compete by jockeying for position. Behav. Ecol. Sociobiol. 33, 101–106 (1993).Article 

Google Scholar 
Nathan, A., Legge, S. & Cockburn, A. Nestling aggression in broods of a siblicidal kingfisher, the laughing kookaburra. Behav. Ecol. 12, 716–725 (2001).Article 

Google Scholar 
Grivas, C. et al. An audio–visual nest monitoring system for the study and manipulation of siblicide in bearded vultures Gypaetus barbatus on the island of Crete (Greece). J. Ethol. 27, 105–116 (2009).Article 

Google Scholar 
Gula, R., Theuerkauf, J., Rouys, S. & Legault, A. An audio/video surveillance system for wildlife. Eur. J. Wildl. Res. 56, 803–807 (2010).Article 

Google Scholar 
Sanaiotti, T. M., Seixas, G. H., Duleba, S. & Martins, F. D. Camera trapping at harpy eagle nests: Interspecies interactions under predation risk. J. Raptor Res. 51, 72–78 (2017).Article 

Google Scholar 
Allen, M. L., Inagaki, A. & Ward, M. P. Cannibalism in raptors: A review. J. Raptor Res. 54, 424–430 (2020).Article 

Google Scholar 
Academia, M. H. & Dalgleish, H. J. Use of nest web cameras and citizen science to quantify osprey prey delivery rate and nest success. J. Raptor Res. 56, 212–219 (2022).Article 

Google Scholar 
Gysel, L. W. & Davis, E. M. A simple automatic photographic unit for wildlife research. J. Wildl. Manag. 20, 451–453 (1956).Article 

Google Scholar 
Royama, T. A device of an auto-cinematic food-recorder. Jpn. J. Ornithol. 15, 172–176 (1959).Article 

Google Scholar 
Cox, W. A. et al. Development of camera technology for monitoring nests. In Chapter 15. Video Surveill. Nesting Birds Stud. Avian Biol. (eds Ribic, C. A. et al.) 185–210 (Univ. Calif. Press, 2012).Sanders, M. D. & Maloney, R. F. Causes of mortality at nests of ground-nesting birds in the Upper Waitaki Basin, South Island, New Zealand: A 5-year video study. Biol. Conserv. 106, 225–236 (2002).Article 

Google Scholar 
Reif, V. & Tornberg, R. Using time-lapse digital video recording for a nesting study of birds of prey. Eur. J. Wildl. Res. 52, 251–258 (2006).Article 

Google Scholar 
McKinnon, L. & Bêty, J. Effect of camera monitoring on survival rates of High-Arctic shorebird nests. J. Field Ornithol. 80, 280–288 (2009).Article 

Google Scholar 
Powell, L. A., Giovanni, M. D., Groepper, S. R., Reineke, M. & Schacht, W. H. Attendance Patterns and Survival of Western Meadowlark Nests (University of California Press, 2012).Book 

Google Scholar 
Herranz, J., Yanes, M. & Suárez, F. Does Photo-Monitoring Affect Nest Predation? J. Field Ornithol. 73, 97–101 (2002).Article 

Google Scholar 
Richardson, T. W., Gardali, T. & Jenkins, S. H. Review and meta-analysis of camera effects on avian nest success. J. Wildl. Manag. 73, 287–293 (2009).Article 

Google Scholar 
Cain, S. L. Nesting activity time budgets of Bald Eagles in southeast Alaska. (1985).García-Salgado, G. et al. Evaluation of trail-cameras for analyzing the diet of nesting raptors using the Northern Goshawk as a model. PLoS ONE 10, e0127585 (2015).Article 

Google Scholar 
Swann, D. E., Kawanishi, K. & Palmer, J. Evaluating types and features of camera traps in ecological studies: a guide for researchers. In Camera Traps in Animal Ecology 27–43 (Springer, 2011).Dykstra, C., Meyer, M. & Warnke, D. Bald Eagle reproductive performance following video camera placement. J. Raptor Res. 36, 136–139 (2002).
Google Scholar 
Del Moral, J. C. & Molina, B. El águila perdicera en España, población reproductora en 2018 y método de censo (SEO/BirdLife, 2018).
Google Scholar 
Generalitat Valenciana. Orden 2/2022, de 16 de febrero, de la Conselleria de Agricultura, Desarrollo Rural, Emergencia Climática y Transición Ecológica, por la que se actualizan los listados valencianos de especies protegidas de flora y fauna (2022).Real, J. & Mañosa, S. Demography and conservation of western European Bonelli’s eagle Hieraaetus fasciatus populations. Biol. Conserv. 79, 59–66 (1997).Article 

Google Scholar 
Hernández-Matías, A. et al. From local monitoring to a broad-scale viability assessment: A case study for the Bonelli’s Eagle in western Europe. Ecol. Monogr. 83, 239–261 (2013).Article 

Google Scholar 
Rollan, A. et al. Guiding local-scale management to improve the conservation of endangered populations: The example of Bonelli’s Eagle Aquila fasciata. Bird Conserv. Int. 31, 395–409 (2021).Article 

Google Scholar 
López-López, P., García-Ripollés, C. & Urios, V. Population size, breeding performance and territory quality of Bonelli’s Eagle Hieraaetus fasciatus in eastern Spain. Bird Study 54, 335–342 (2007).Article 

Google Scholar 
López-López, P. Informe científico valoración de la inclusión del águila perdicera como especie en peligro de extinción en el Catálogo Valenciano de Especies de Fauna Amenazadahttps://doi.org/10.13140/RG.2.2.32806.04166 (2021).López-López, P., Perona, A., Egea-Casas, O., Morant, J. & Urios, V. Tri-axial accelerometry shows differences in energy expenditure and parental effort throughout the breeding season in long-lived raptors. Curr. Zool. 68, 57–67 (2022).Article 

Google Scholar 
Perona, A. M., Urios, V. & López-López, P. Holidays? Not for all Eagles have larger home ranges on holidays as a consequence of human disturbance. Biol. Conserv. 231, 59–66 (2019).Article 

Google Scholar 
Morollón, S., Urios, V. & López-López, P. Fifteen days are enough to estimate home-range size in some long-lived resident eagles. J. Ornithol. 163, 849–854 (2022).Article 

Google Scholar 
Stewart-Oaten, A., Murdoch, W. W. & Parker, K. R. Environmental impact assessment:” Pseudoreplication” in time?. Ecology 67, 929–940 (1986).Article 

Google Scholar 
Underwood, A. Beyond BACI: The detection of environmental impacts on populations in the real, but variable, world. J. Exp. Mar. Biol. Ecol. 161, 145–178 (1992).Article 

Google Scholar 
López-López, P., García-Ripollés, C., García-López, F., Aguilar, J. M. & Verdejo, J. Patrón de distribución del águila real Aquila chrysaetos y del águila-azor perdicera Hieraaetus fasciatus en la provincia de Castellón. Ardeola 51, 275–283 (2004).
Google Scholar 
López-López, P., García-Ripollés, C., Aguilar, J. M., Garcia-López, F. & Verdejo, J. Modelling breeding habitat preferences of Bonelli’s eagle (Hieraaetus fasciatus) in relation to topography, disturbance, climate and land use at different spatial scales. J. Ornithol. 147, 97–106 (2006).Article 

Google Scholar 
Gil-Sánchez, J. Effects of altitude and prey availability on the laying date of Bonelli’s Eagles (Hieraaetus fasciatus) in Granada (SE Spain). Ardeola 47, 1–8 (2000).
Google Scholar 
Forsman, D. Flight Identification of Raptors of Europe, North Africa and the Middle East (Bloomsbury Publishing, 2016).
Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R Vol. 574 (Springer, 2009).Book 
MATH 

Google Scholar 
Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).Article 

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).Article 

Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. R package version 1.46.0. (2022).Cutler, T. L. & Swann, D. E. Using remote photography in wildlife ecology: A review. Wildl. Soc. Bull. 27, 571–581 (1999).
Google Scholar 
Richardson, C. T. & Miller, C. K. Recommendations for protecting raptors from human disturbance: A review. Wildl. Soc. Bull. 25, 634–638 (1997).
Google Scholar 
Balbontin, J., Penteriani, V. & Ferrer, M. Variations in the age of mates as an early warning signal of changes in population trends? The case of Bonelli’s eagle in Andalusia. Biol. Conserv. 109, 417–423 (2003).Article 

Google Scholar 
Martinez, J. A. et al. Breeding performance, age effects and territory occupancy in a Bonelli’s eagle Hieraaetus fasciatus population. Ibis 150, 223–233 (2008).Article 

Google Scholar 
Sánchez-Zapata, J., Calvo, J., Carrete, M. & Martínez, J. Age and breeding success of a Golden Eagle Aquila chrysaetos population in southeastern Spain. Bird Study 47, 235–237 (2000).Article 

Google Scholar 
Ferrer, M., Penteriani, V., Balbontin, J. & Pandolfi, M. The proportion of immature breeders as a reliable early warning signal of population decline: Evidence from the Spanish imperial eagle in Donana. Biol. Conserv. 114, 463–466 (2003).Article 

Google Scholar 
Cano, A. & Parrinder, E. Studies of less familiar birds, Bonelli’s Eagle. Br. Birds 54, 422–427 (1961).
Google Scholar 
Blondel, J., Coulon, L., Girerd, B. & Hortigue, M. Deux cents heures d’observation aupre‘s de l’aire de l’Aigle de Bonelli Hieraaetus fasciatus. Nos Oiseaux 30, 37–60 (1969).
Google Scholar 
Vaucher, C. Notes sur 1’ethologie de I’Aigle de Bonelli. Nos Oiseaux 31, 101–111 (1971).
Google Scholar 
Elósegui, J. Informe preliminar sobre alimentación de aves rapaces en Navarra y provincias limítrofes. Ardeola 19, 249–256 (1974).
Google Scholar 
Cheylan, G. L. place trophique de l’Aigle de Bonelli Hieraaetus fasciatus dans les biocénoses méditerranéennes. Alauda 45, 1–15 (1977).
Google Scholar 
Palma, L., Cancela da Fonseca, L. & Oliveira, L. L’alimentation de l’aigle de Bonelli Hieraaetus fasciatus dans la coˆte portugaise. Rapinyaires Mediterranis 2, 87–96 (1984).
Google Scholar 
Real, J. Biases in diet study methods in the Bonelli’s eagle. J. Wildl. Manag. 60, 632–638 (1996).Article 

Google Scholar 
Gil-Sánchez, J. M., Molino, F., Valenzuela, G. & Moleón, M. Demografía y alimentación del Águila-azor Perdicera (Hieraaetus fasciatus) en la provincia de Granada. Ardeola 47, 69–75 (2000).
Google Scholar 
Ontiveros, D., Pleguezuelos, J. M. & Caro, J. Prey density, prey detectability and food habits: The case of Bonelli’s eagle and the conservation measures. Biol. Conserv. 123, 19–25 (2005).Article 

Google Scholar 
Moleón, M. et al. Large-scale spatio-temporal shifts in the diet of a predator mediated by an emerging infectious disease of its main prey. J. Biogeogr. 36, 1502–1515 (2009).Article 

Google Scholar 
Resano-Mayor, J. et al. Diet–demography relationships in a long-lived predator: From territories to populations. Oikos 125, 262–270 (2016).Article 

Google Scholar 
Di Vittorio, M. et al. Long-term changes in the breeding period diet of Bonelli’s eagle (Aquila fasciata) in Sicily, Italy. Wildl. Res. 46, 409–414 (2019).Article 

Google Scholar  More

Study species and pre-cultivationTo create the mesocosm communities, we selected nine herbaceous grassland species that are native to and widespread in Central Europe (Supplementary Table 9), where they can also co-occur. The species were Alopecurus pratensis L., Diplotaxis tenuifolia (L.) DC., Lolium perenne L., Poa pratensis L., Prunella vulgaris L., Sinapis arvensis L., Sonchus oleraceus L., Vicia cracca L., Vicia sativa L. To increase generalizability54, the species were selected from three functional groups (grasses, annual forbs, perennial forbs), and they represent five families.Seeds were obtained from different sources (Supplementary Table 9). For the transplanted-seedling community (see section ‘Experimental lay-out), seedlings were pre-cultivated in a greenhouse of the Botanical Garden of the University of Konstanz. As the species require different times for germination, they were sown on different dates (Supplementary Table 10) to ensure that seedlings of all species were at a similar developmental stage at transplantation. Seeds were sown separately per species in plastic trays filled with potting soil (Einheitserde®, Pikiererde CL P). The greenhouse had a regular day-night rhythm of c. 16:8 hours, and its ventilation windows automatically opened at 21 °C during the day and at 18 °C during the night. Two days before transplanting, the seedlings were placed outdoors to acclimatize. For the sown community, we sowed a seed mixture of the nine study species directly into the outdoor mesocosm pots.Experimental setupGlobal change treatmentsWe imposed six global change treatments: climate warming, light pollution, microplastic pollution, soil salinization, eutrophication, and fungicide accumulation, all of which frequently occur in the environment. These GCFs were chosen because they differ in their nature (i.e., physical, chemical), are likely to differ in their mode of action and effect direction21, and can be easily implemented. Each of the six GCFs have been shown to impact plants and their environment when applied on their own10,13,17,19,20,55,56,57,58,59,60. Furthermore, all of the chosen GCFs are likely to continue to increase in magnitude or extent in the near future61,62,63,64,65. For the climate-warming treatment, we used infrared-heater lamps (HS-2420; 240 V, 2000 W; Kalglo Electronics Co., Bethlehem, USA) set to 70% of their maximum capacity to achieve an average temperature increase of 2.0 °C (±SD = 0.2 °C) at plant level. This is within the range of temperature increases predicted by the RCP 4.5 scenario for the year 2100 [+1.1 − 2.6 °C; 63]. For the light-pollution treatment, we used LED spotlights (LED-Strahler Flare 10 W, IP 65, 900 lm, cool white 6500 K; REV Ritter GmbH, Mömbris, Germany), which were switched on daily from 9 pm to 5 am, corresponding to the times of sunset and -rise. The average light intensity was 24.5 lx at ground level, which is within the range of light intensities found below street lights, and matches the light intensities used in other light-pollution experiments14,56. For the microplastic pollution treatment, we used granules (1.0–2.5 mm diameter) of the synthetic rubber ethylene propylene diene monomer (EPDM Granulat, Gummi Appel GmbH + Co. KG, Kahl am Main, Germany) at a concentration of 1% (w/w, granules/dry soil, approximately corresponding to 1.5% v/v). EPDM granules are, for example, used in artificial sport turfs, from where they easily spread into the surroundings, and have been used previously to investigate the effects of microplastics on plants18. The chosen concentration is well within the range of concentrations used in previous studies18,66,67, and is at the low to intermediate range of concentrations found in sites polluted with plastics68. For the soil-salinization treatment, dissolved NaCl was added to the soil. Soil salinity is commonly measured as electrical conductivity, with a conductivity between 4 and 8 dS m−1 considered to be moderately saline69. For the experiment, we used a salinity of 6 dS m−1. To maintain a more or less constant salinity level, electrical conductivity was measured weekly, and, if required, adjusted by adding dissolved NaCl. For the eutrophication treatment, 3 g of a dissolved NPK fertilizer (Universol® blue oxide, ICL SF Germany & Austria, Nordhorn, Germany) was added per pot. For N, this corresponds to an input of 100 kg N ha−1, comparable to the yearly amounts of atmospheric N deposition in large parts of Europe52 and the yearly nitrogen input on agricultural field in the European Union70. To ensure a more or less constant nutrient availability during the experiments, we split total fertilizer input into three applications (directly after, 3 weeks after, and 6 weeks after starting the experiments) of 1 g fertilizer per pot per application. In addition, to avoid severe nutrient limitation in the other pots, all pots (irrespective of the eutrophication treatment) received basic fertilization. This was applied four times to the transplanted-seedling-community pots and five times to the sown community pots, with 0.2 g fertilizer per pot per application. For the pesticide treatment, we used the fungicide Landor® CT (Syngenta Agro GmbH, Maintal, Germany). This fungicide was chosen because it contains three azoles as active agents, which belong to the most widely used class of antifungal agents71. To each pot in this treatment, we added 1.5 μl fungicide dissolved in water (1‰). This corresponds to 60% of the maximum amount that should be used per hectare of cropland. A summary of the levels of the individual GCFs used in our experiment is provided in Supplementary Table 8.Combinations of simultaneously acting GCFsTo examine the potential effects of the numbers of simultaneously acting GCFs, we created five levels of increasing GCF numbers. These levels were: zero (i.e., the control without any GCF application), one (single), two, four and six GCFs. For the one-, two- and four-GCF levels, there were six different combinations, so that each of these levels included either six different GCFs in case of the one-factor, or six different GCF combinations in case of the two- and four-GCF levels. In the six-GCF level, all six factors were combined, so that there was only one combination. To avoid potential biases due to unequal representation of the different GCFs in each GCF-number level, we created the GCF combinations randomly but with the restriction that each GCF was present in an equal number of combinations for each GCF-number level (i.e., each GCF was included once in GCF-number levels 1 and 6, respectively, twice in GCF-number level 2, and four times in GCF-number level 4; Supplementary Table 11).Experimental lay-outThe experiment was conducted outdoors in the climate-warming-simulation facility of the Botanical Garden of the University of Konstanz, Germany (N: 47°69’19.56”, E: 9°17’78.42”). Twenty of the 2 m × 2 m plots of this facility were used for our experiment. As the climate-warming and light-pollution treatments could not be applied to each individual pot separately, we applied those treatments at the plot level. Therefore, we assigned four of the 20 plots to the climate-warming treatment, four plots to the light-pollution treatment and four plots to both climate-warming and light-pollution treatment combination. Each plot had a 145 cm high metal frame. The eight plots assigned to the climate-warming treatment were equipped with a 1.80 m long, horizontally hanging infrared-heating lamp at the top of the metal frame (i.e., at 145 cm above soil level). The heating lamp slowly oscillated along its longitudinal axis to ensure uniform heating of the whole 2 m × 2 m plot. The eight plots assigned to the light-pollution treatment, each had a LED spotlight attached to one of the sides of the metal frame at a height of 120 cm. To reduce illumination of the neighboring plots, light-pollution was only applied to the outer plots of the climate-warming-simulation facility (Supplementary Fig. 5), and LEDs were pointing away from the inner plots and were equipped with lamp shades made of black plastic pots (18 cm × 18 cm × 25.5 cm). Furthermore, to reduce the light intensity to a realistic light-pollution level (24.5 lx) as found below street lights, we covered the spotlight with a layer of white cloth (Supplementary Fig. 6). For further details on the artificial light treatment, see Supplementary Fig. 7.To create mesocosms with the transplanted-seedling and sown communities, we filled 10-L pots (CEP- Container, 10.0 F, Burger GmbH, Renningen-Malmsheim, Germany) with a mixture of 40% potting soil (see above), 40% quartz sand (0.5–0.8 mm), and—to inoculate the substrate with a natural soil community—20% top soil excavated from a seminatural grassland patch in the botanical garden. In total, the experiments with the transplanted-seedling and sown communities, each included 120 pots (i.e., 20 treatment combinations × six replicates × 2 experiments = 240 pots in total; see Supplementary Table 11), which were distributed across the 20 plots. To prevent leakage of fertilizer or salt solutions, each pot was placed onto a plastic dish. To reduce differences due to environmental variation within plots, the positions of pots within each plot were re-randomized every 14 days. Plants were watered regularly to avoid drought stress and to avoid differences in soil moisture due to application of fertilizer- and salt-solutions.For the sown community, we randomly distributed five seeds of each of the nine species on the substrate in each pot on 3 July 2020. For the transplanted-seedling community, two seedlings of each of the nine species were transplanted into each pot (i.e., 18 seedlings per pot) according to a fixed pattern (Supplementary Fig. 8) on 6 July 2020. Since there were a few seedlings missing for S. arvensis (six seedlings) and V. cracca (four seedlings), we re-sowed these species in germination trays on 6 July 2020. On 13 July 2020, dead seedlings, and the missing seedlings for S. arvensis were replaced. Since V. cracca took longer to germinate, the missing seedlings were transplanted on 17 July 2020.MeasurementsTo investigate the effects of single-GCFs and their number on the sown and transplanted-seedling communities, we used plant biomass as an indicator for plant performance72. As it was impossible to disentangle the roots, we only used aboveground biomass. On 14 and 15 September 2020, i.e., 10 weeks after transplanting, we harvested the transplanted-seedling communities. On 28 and 29 September, i.e., twelve weeks after sowing, we harvested the sown communities. For both community types, we harvested the plants separately by species. The harvested plants were stored in paper bags, dried at 70 °C for at least 72 hours and weighed.Statistical analysisAll analyses were done in R 3.6.273. As the transplanted-seedling and sown communities were harvested at different times, we treated them as separate experiments, and therefore analyzed them separately (but see the subsection “Community type specific responses” below).Community aboveground biomassTo analyze the effects GCF number on plant-community productivity, we fitted linear mixed-effects models separately for the transplanted-seedling and sown communities, using the lmer function in the “lme4” package74. Total aboveground biomass per pot was the response variable. To improve normality of the residuals, biomass of the transplanted-seedling and sown communities was square-root- and natural-log-transformed, respectively. We included GCF number as a continuous fixed variable. To account for non-independence of pots in the same GCF combination and of pots in the same plot, GCF combination and plot were included as random effects. The effects of the individual GCFs on biomass production were also assessed by fitting linear mixed-effects models, using only the data of the control and single-GCF treatments, and including GCF identity as fixed effect.Community compositionTo assess potential effects of single-GCFs and GCF number on the final composition of the transplanted-seedling and sown communities, we first assessed variation in species composition, based on biomass proportions, among pots using principal component analysis (PCA) [rda function of the “vegan” package75,]. For each PCA (Supplementary Fig. 1), we extracted the PC1 and PC2 values, which together explained more than 65% of the variation in community composition and included them as response variables in separate linear mixed models, as described above for community biomass.To evaluate whether GCF number affects the diversity and evenness of plant communities, we calculated the Shannon index (H)76, using the diversity function in the “vegan” package, and evenness index (J)77 based on species biomass proportions. Subsequently, the single-GCF and GCF-number effects on diversity and evenness of the sown and transplanted-seedling communities were analyzed using linear mixed-effects models, or—if adding random effects did not improve the model—more parsimonious linear models78,79. For all models, we used type II analysis of variance (ANOVA) tests (Anova function in the “car” package) to assess the significance of fixed effects.Hierarchical diversity-interaction modelingWhen there is a significant GCF-number effect, this could reflect that with increasing numbers of co-acting GCFs, there is a higher chance that it will include a GCF with a strong and dominant effect (i.e., sampling or selection effects). However, it could also be that the GCF-number effect is driven by interactions among the GCFs, and the effects of these interactions could be GCF-specific or general. As our experiment does not include all possible combinations of GCFs, it does not allow to test the contributions of each possible multi-way GCF interaction. Therefore, to gain insights into whether the GCF identities and specific or general GCF interactions underlie the significant GCF-number effects, we applied the hierarchical diversity-interaction modeling framework of Kirwan et al.80. This framework was originally developed for estimating contributions of species identities and their interactions to ecosystem functions, but we here applied it to GCF identities and interactions. For each of the response variables showing a significant GCF-number effect, we ran five hierarchical models specifying different assumptions about the potential contributions of individual GCFs and their interactions to the GCF-number effect, and compared them using likelihood ratio tests (Fig. 4). For these analyses, the data of the control treatment (i.e., GCF number zero) was excluded. Each of the five models specified different assumptions about the potential contributions of individual GCFs and their interactions to the GCF-number effect. The first model is the null model, which assumed that there were no GCF-specific contributions (i.e., all GCFs contributed equally) and that there were no contributions of GCF interactions. Therefore, the null model only included the centered sum of the GCFs of each treatment (M) as fixed effect. M accounts for differences in ‘initial abundances’ of GCFs—meaning that the other model terms are interpreted based on the average initial abundance—and was also included in the four other models80. This way, we could include the GCFs’ relative proportions in each GCF combination, instead of just considering GCF presence, while taking into account that, with increasing GCF number, the relative proportion of each individual GCF is automatically reduced. In the second model, the GCF identities (i.e., their proportions in the respective GCF combination) were added, assuming that individual GCFs contribute differently to the effect of GCF number. In the third model, separate-pairwise interactions between the GCFs were added, considering that, in addition to contributions of individual GCFs, specific pairwise interactions contributed to the GCF-number effect. In the fourth model, the average GCF-interaction model (which is also called the evenness model in Kirwan et al. 2009), the separate-pairwise GCF interactions were replaced by an average interaction effect. Thus, the average GCF-interaction model assumed equivalent contributions of all pairwise GCF interactions. In the fifth model, the additive GCF-specific interaction contributions model, the average interaction effect of the fourth model was replaced by average GCF-specific interaction effects. This model assumed that each GCF’s contribution to a pairwise interaction remains constant. For the calculation of the average GCF-specific and average interaction effect, we used the equations provided by Kirwan et al.80. For each of the response variables, we generally included the same random terms as in the main analyses of the GCF-number effect. However, as this resulted in singularity warnings for some of the hierarchical diversity-interaction models, e.g., those for species diversity and evenness measures, we used for these cases linear models instead of linear mixed models.Fig. 4: Hierarchical diversity-interaction-modeling framework to assess contributions of GCF identities and GCF interactions to GCF-number effects.The framework was adapted from Kirwan et al.80. The null model assumes equivalent contributions of all GCFs and no interactions between them. The subsequent models assume more complex effects of how the individual GCFs and their interactions determine the GCF-number effects. The questions that can be answered by comparing specific models are depicted next to the arrows connecting the two models.Full size imageCommunity type-specific responsesAs the transplanted-seedling and sown communities were harvested at different times, we treated them as separate experiments, and therefore analyzed them separately. However, to test explicitly whether both community types differed in their responses to single-GCFs and GCF number, we also analyzed them jointly. To this end, we fitted linear mixed-effects models for each response variable including GCF number (or single-factor treatments), community type and their interaction as fixed effects (Supplementary Table 5).Final number of plants per speciesTo test for effects of individual GCFs and GCF number on species presence, i.e., the number of individuals per species present at harvest, we fitted generalized linear mixed-effects models for the transplanted-seedling and sown communities separately. We included the survival rate (number of individuals present at harvest divided by the number of planted/sown individuals) as response variables. For the models testing the effects of GCF number, we included GCF combination, species, pot, and plot as random effects. For the models testing the effects of single-GCFs, the same random effects were included, except for GCF combination. Specific random effects were removed from the model if their incorporation resulted in singular fit warnings due to low variation. We assessed the effects of individual GCFs or GCF number using type III ANOVA tests (Anova function in the “car” package, Supplementary Table 7).Eutrophication effectsIn addition to the general assessment of individual GCF effects in the hierarchical diversity-interaction models, we specifically assessed the effects of eutrophication. This was done because eutrophication had the strongest effect on productivity as individual GCF, and this might also have dominated the GCF-number effect, indicating a sampling effect. To this end, we added a binary-coded variable to include information on whether eutrophication was included in the different GCF combinations. Subsequently, we fitted linear mixed-effects models for all response traits that were affected by GCF number. In these models, we included GCF number, community type, eutrophication, and the respective two-way interactions as fixed effects, and plot and GCF combination as random effects. Effects of fixed factors were assessed using type III ANOVA tests (Anova function in the “car” package; Supplementary Table 6).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

IPBES. Global assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES secretariat, 2019).Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).Article 
ADS 
CAS 

Google Scholar 
IPCC. Summary for Policymakers. in Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2022).Travis, J. M. J. Climate change and habitat destruction: a deadly anthropogenic cocktail. P. R. Soc. B. 270, 467–473 (2003).Article 
CAS 

Google Scholar 
Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. P. R. Soc. B. 285, 20180792 (2018).Article 

Google Scholar 
Anderson, K. J., Allen, A. P., Gillooly, J. F. & Brown, J. H. Temperature-dependence of biomass accumulation rates during secondary succession. Ecol. Lett. 9, 673–682 (2006).Article 

Google Scholar 
Fridley, J. D. & Wright, J. P. Temperature accelerates the rate fields become forests. Proc. Natl Acad. Sci. USA 115, 4702–4706 (2018).Article 
ADS 
CAS 

Google Scholar 
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).Article 
ADS 
CAS 

Google Scholar 
Auffret, A. G., Kimberley, A., Plue, J. & Waldén, E. Super-regional land-use change and effects on the grassland specialist flora. Nat. Commun. 9, 3464 (2018).Article 
ADS 

Google Scholar 
Auffret, A. G. & Thomas, C. D. Synergistic and antagonistic effects of land use and non-native species on community responses to climate change. Glob. Change Biol. 25, 4303–4314 (2019).Article 
ADS 

Google Scholar 
Hill, M. O. Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods Ecol. Evol. 3, 195–205 (2012).Article 

Google Scholar 
Isaac, N. J. B., Strien, A. J., van, August, T. A., Zeeuw, M. Pde & Roy, D. B. Statistics for citizen science: extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060 (2014).Article 

Google Scholar 
Tyler, T., Herbertsson, L., Olofsson, J. & Olsson, P. A. Ecological indicator and traits values for Swedish vascular plants. Ecol. Indic. 120, 106923 (2021).Article 
CAS 

Google Scholar 
Jiang, M., Bullock, J. M. & Hooftman, D. A. P. Mapping ecosystem service and biodiversity changes over 70 years in a rural English county. J. Appl. Ecol. 50, 841–850 (2013).Article 

Google Scholar 
IPCC. Summary for Policymakers. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2021).Van Calster, H. et al. Unexpectedly high 20th century floristic losses in a rural landscape in northern France. J. Ecol. 96, 927–936 (2008).Article 

Google Scholar 
Staude, I. R. et al. Replacements of small- by large-ranged species scale up to diversity loss in Europe’s temperate forest biome. Nat. Ecol. Evol. 4, 802–808 (2020).Article 

Google Scholar 
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).Article 
ADS 
CAS 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
Platts, P. J. et al. Habitat availability explains variation in climate-driven range shifts across multiple taxonomic groups. Sci. Rep. 9, 1–10 (2019).Article 
ADS 
MathSciNet 
CAS 

Google Scholar 
Macgregor, C. J. et al. Climate-induced phenology shifts linked to range expansions in species with multiple reproductive cycles per year. Nat. Commun. 10, 4455 (2019).Article 
ADS 

Google Scholar 
Dullinger, S. et al. Extinction debt of high-mountain plants under twenty-first-century climate change. Nat. Clim. Change 2, 619–622 (2012).Article 
ADS 

Google Scholar 
Svenning, J.-C. & Sandel, B. Disequilibrium vegetation dynamics under future climate change. Am. J. Bot. 100, 1266–1286 (2013).Article 

Google Scholar 
Cannone, N. & Pignatti, S. Ecological responses of plant species and communities to climate warming: upward shift or range filling processes? Climatic Change 123, 201–214 (2014).Article 
ADS 

Google Scholar 
Wiens, J. J. Climate-Related Local Extinctions Are Already Widespread among Plant and Animal Species. PLOS Biol. 14, e2001104 (2016).Article 

Google Scholar 
Hill, M. O. & Preston, C. D. Disappearance of boreal plants in southern Britain: habitat loss or climate change? Biol. J. Linn. Soc. 115, 598–610 (2015).Article 

Google Scholar 
Lynn, J. S., Klanderud, K., Telford, R. J., Goldberg, D. E. & Vandvik, V. Macroecological context predicts species’ responses to climate warming. Glob. Change Biol. 27, 2088–2101 (2021).Article 
ADS 
CAS 

Google Scholar 
Liu, D. et al. Species selection under long-term experimental warming and drought explained by climatic distributions. N. Phytol. 217, 1494–1506 (2018).Article 

Google Scholar 
Buitenwerf, R., Sandel, B., Normand, S., Mimet, A. & Svenning, J.-C. Land surface greening suggests vigorous woody regrowth throughout European semi-natural vegetation. Glob. Change Biol. 24, 5789–5801 (2018).Article 

Google Scholar 
Suggitt, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).Article 
ADS 

Google Scholar 
De Frenne, P. et al. Latitudinal gradients as natural laboratories to infer species’ responses to temperature. J. Ecol. 101, 784–795 (2013).Article 

Google Scholar 
Ash, J. D., Givnish, T. J. & Waller, D. M. Tracking lags in historical plant species’ shifts in relation to regional climate change. Glob. Change Biol. 23, 1305–1315 (2017).Article 
ADS 

Google Scholar 
Savage, J. & Vellend, M. Elevational shifts, biotic homogenization and time lags in vegetation change during 40 years of climate warming. Ecography 38, 546–555 (2015).Article 

Google Scholar 
Gerstner, K., Dormann, C. F., Stein, A., Manceur, A. M. & Seppelt, R. Effects of land use on plant diversity—a global meta-analysis. J. Appl. Ecol. 51, 1690–1700 (2014).Article 

Google Scholar 
Kempel, A. et al. Nationwide revisitation reveals thousands of local extinctions across the ranges of 713 threatened and rare plant species. Conserv. Lett. 13, e12749 (2020).Article 

Google Scholar 
Bilz, M., Kell, S. P., Maxted, N. & Lansdown, R. V. European Red List of Vascular Plants (Publications Office of the EU, 2011).Timmermann, A., Damgaard, C., Strandberg, M. T. & Svenning, J.-C. Pervasive early 21st-century vegetation changes across Danish semi-natural ecosystems: more losers than winners and a shift towards competitive, tall-growing species. J. Appl. Ecol. 52, 21–30 (2015).Article 

Google Scholar 
Staude, I. R. et al. Directional turnover towards larger-ranged plants over time and across habitats. Ecol. Lett. 25, 466–482 (2022).Article 

Google Scholar 
Finderup Nielsen, T., Sand‐Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).Article 

Google Scholar 
Christiansen, D. M., Iversen, L. L., Ehrlén, J. & Hylander, K. Changes in forest structure drive temperature preferences of boreal understorey plant communities. J. Ecol. 110, 631–643 (2022).Article 

Google Scholar 
Gossner, M. M. et al. Land-use intensification causes multitrophic homogenization of grassland communities. Nature 540, 266–269 (2016).Article 
ADS 
CAS 

Google Scholar 
Duprè, C. et al. Changes in species richness and composition in European acidic grasslands over the past 70 years: the contribution of cumulative atmospheric nitrogen deposition. Glob. Change Biol. 16, 344–357 (2010).Article 
ADS 

Google Scholar 
Tyler, T. et al. Climate warming and land‐use changes drive broad‐scale floristic changes in Southern Sweden. Glob. Change Biol. 24, 2607–2621 (2018).Article 
ADS 

Google Scholar 
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231 (2018).Article 
ADS 
CAS 

Google Scholar 
Halley, J. M., Monokrousos, N., Mazaris, A. D., Newmark, W. D. & Vokou, D. Dynamics of extinction debt across five taxonomic groups. Nat. Commun. 7, 12283 (2016).Article 
ADS 
CAS 

Google Scholar 
Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).Article 
ADS 
CAS 

Google Scholar 
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).Article 

Google Scholar 
Plue, J. et al. Buffering effects of soil seed banks on plant community composition in response to land use and climate. Glob. Ecol. Biogeogr. 30, 128–139 (2021).Article 

Google Scholar 
Honnay, O. & Bossuyt, B. Prolonged clonal growth: escape route or route to extinction? Oikos 108, 427–432 (2005).Article 

Google Scholar 
Ozinga, W. A. et al. Dispersal failure contributes to plant losses in NW Europe. Ecol. Lett. 12, 66–74 (2009).Article 

Google Scholar 
Svenning, J.-C., Normand, S. & Skov, F. Postglacial dispersal limitation of widespread forest plant species in nemoral Europe. Ecography 31, 316–326 (2008).Article 

Google Scholar 
Lenoir, J., Gégout, J. C., Marquet, P. A., de Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).Article 
ADS 
CAS 

Google Scholar 
Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).Article 
ADS 
CAS 

Google Scholar 
Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5 °C rather than 2 °C. Science 360, 791–795 (2018).Article 
CAS 

Google Scholar 
Garrido, P. et al. Experimental rewilding may restore abandoned wood-pastures if policy allows. Ambio 50, 101–112 (2021).Article 

Google Scholar 
Kowalczyk, R., Kamiński, T. & Borowik, T. Do large herbivores maintain open habitats in temperate forests? For. Ecol. Manag. 494, 119310 (2021).Article 

Google Scholar 
Auffret, A. G., Schmucki, R., Reimark, J. & Cousins, S. A. O. Grazing networks provide useful functional connectivity for plants in fragmented systems. J. Veg. Sci. 23, 970–977 (2012).Article 

Google Scholar 
Fricke, E. C., Ordonez, A., Rogers, H. S. & Svenning, J.-C. The effects of defaunation on plants’ capacity to track climate change. Science 375, 210–214 (2022).Article 
ADS 
CAS 

Google Scholar 
Blomgren, E., Falk, E. & Herloff, B. Bohusläns Flora (Föreningen Bohusläns Flora, 2011).Fries, H. Göteborgs och Bohus Läns Fanerogamer och Ormbunkar (Elanders Boktryckeri, 1945).Lidberg, R. & Lindström, H. Medelpads Flora (The vascular plants of Medelpad) (SBF Förlaget, 2010).Sterner, R. Flora der insel Öland Vol. IX (Almqvist & Wiksells, 1938).Almquist, E. Upplands vegetation och flora. Acta Phytogeogr. Suec. 1, 1–622 (1929).
Google Scholar 
Jonsell, L. Upplands Flora (SBF Förlaget, 2010).Maad, J., Sundberg, S., Stolpe, P. & Jonsell, L. Floraförändringar i Uppland under 1900-talet—en analys från Projekt Upplands flora [Floristic changes during the 20th century in Uppland, east central Sweden; with English summary]. Sven. Botanisk Tidskr. 103, 67–104 (2009).
Google Scholar 
Auffret, A. G. et al. HistMapR: Rapid digitization of historical land-use maps in R. Methods Ecol. Evol. 8, 1453–1457 (2017).Article 

Google Scholar 
August, T. et al. sparta: Trend analysis for unstructured data. R package version 0.1.44 (2018).Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Glob. Change Biol. 27, 1097–1110 (2021).Article 
ADS 
CAS 

Google Scholar 
Redhead, J. W. et al. Potential landscape-scale pollinator networks across Great Britain: structure, stability and influence of agricultural land cover. Ecol. Lett. 21, 1821–1832 (2018).Article 

Google Scholar 
Gillings, S. et al. Breeding and wintering bird distributions in Britain and Ireland from citizen science bird atlases. Glob. Ecol. Biogeogr. 28, 866–874 (2019).Article 

Google Scholar 
Stroh, P. A., Walker, K. J., Humphrey, T. A., Pescott, O. L. & Burkmar, R. J. Plant Atlas 2020: Mapping Changes in the Distribution of the British and Irish Flora (Princeton, planned publication date: 21/03/2023).Pearce-Higgins, J. W., Ausden, M. A., Beale, C. M., Oliver, T. H. & Crick, H. Q. P. Research on the assessment of risks & opportunities for species in England as a result of climate change – NECR175. Natural England Commissioned Reports Vol. 175 (2015).R. Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Telfer, M. G., Preston, C. D. & Rothery, P. A general method for measuring relative change in range size from biological atlas data. Biol. Conserv. 107, 99–109 (2002).Article 

Google Scholar 
Bates, D., Maechler, M., Bolker, B. M. & Walker, S. lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-7. http://CRAN.R-project.org/package=lme4 (2014).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2009).Article 

Google Scholar 
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).Article 

Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
Borcard, D. & Legendre, P. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 153, 51–68 (2002).Article 

Google Scholar 
Oksanen, J. et al. vegan: Community ecology package. R package version 2.3-5. http://CRAN.R-project.org/package=vegan (2016).Meineri, E. & Hylander, K. Fine-grain, large-domain climate models based on climate station and comprehensive topographic information improve microrefugia detection. Ecography 40, 1003–1013 (2017).Article 

Google Scholar 
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. performance: an R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021).Article 
ADS 

Google Scholar 
Breheny, P. & Burchett, W. Visualization of regression models using visreg. R. J. 9, 57–71 (2017).Article 

Google Scholar 
Hijmans, R. J. raster: Geographic data analysis and modeling. R package version 2.5-8. http://CRAN.R-project.org/package=raster (2016). More

Asevedo, L. et al. Palynological analysis of dental calculus from Pleistocene proboscideans of southern Brazil: a new approach for paleodiet and paleoenvironmental reconstructions. Palaeogeogr. Palaeoclimatol. Palaeoecol. 540, 109523 (2020).Article 

Google Scholar 
Cristiani, E. et al. Wild cereal grain consumption among Early Holocene foragers of the Balkans predates the arrival of agriculture. Elife 10, e72976 (2021).Article 
CAS 

Google Scholar 
Nava, A. et al. Multipronged dental analyses reveal dietary differences in last foragers and first farmers at Grotta Continenza, central Italy (15,500–7000 BP). Sci. Rep. 11, 1–14 (2021).Article 

Google Scholar 
Ottoni, C. et al. Tracking the transition to agriculture in Southern Europe through ancient DNA analysis of dental calculus. Proc. Natl. Acad. Sci. USA 118, e2102116118 (2021).Article 
CAS 

Google Scholar 
Cammidge, T. S., Kooyman, B. & Theodor, J. M. Diet reconstructions for end-Pleistocene Mammut americanum and Mammuthus based on comparative analysis of mesowear, microwear, and dental calculus in modern Loxodonta africana. Palaeogeogr. Palaeoclimatol. Palaeoecol. 538, 109403 (2020).Article 

Google Scholar 
de Oliveira, K. et al. From oral pathology to feeding ecology: the first dental calculus paleodiet study of a South American native megamammal. J. S. Am. Earth Sci. 109, 103281 (2021).Article 

Google Scholar 
Mothé, D. et al. The micro from mega: dental calculus description and the first record of fossilized oral bacteria from an extinct proboscidean. Int. J. Paleopathol. 33, 55–60 (2021).Article 

Google Scholar 
Eglinton, G. & Logan, G. A. Molecular preservation. Philosophical Transactions of the Royal Society of London. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 333, 315–328 (1991).CAS 

Google Scholar 
Romanowski, G., Lorenz, M. G. & Wackernagel, W. Adsorption of plasmid DNA to mineral surfaces and protection against Dnase I. Appl. Environ. Microbiol. 57, 1057–1061 (1991).Article 
CAS 

Google Scholar 
Milanesi, C. et al. Ultrastructural study of archaeological Vitis vinifera L. seeds using rapid-freeze fixation and substitution. Tissue Cell 41, 443–447 (2009).Article 
CAS 

Google Scholar 
Power, R. C., Salazar-García, D. C., Wittig, R. M., Freiberg, M., & Henry, A. G. Dental calculus evidence of Taï Forest chimpanzee plant consumption and life history transitions. Sci. Rep. 5, 15161 (2015).Goude, G. et al. A multidisciplinary approach to Neolithic life reconstruction. J. Archaeol. Method Theory 26, 537–560 (2019).Article 

Google Scholar 
Farrer, A. G. et al. Effectiveness of decontamination protocols when analyzing ancient DNA preserved in dental calculus. Sci. Rep. 11, 1–14 (2021).Article 

Google Scholar 
Weyrich, L. S., Dobney, K. & Cooper, A. Ancient DNA analysis of dental calculus. J. Hum. Evol. 79, 119–124 (2015).Article 

Google Scholar 
Ozga, A. T. et al. Successful enrichment and recovery of whole mitochondrial genomes from ancient human dental calculus. Am. J. Phys. Anthropol. 160, 220–228 (2016).Article 

Google Scholar 
Mann, A. E. et al. Do I have something in my teeth? The trouble with genetic analyses of diet from archaeological dental calculus. Quat. Int. https://doi.org/10.1016/j.quaint.2020.11.019 (2020).Wright, S. L., Dobney, K. & Weyrich, L. S. Advancing and refining archaeological dental calculus research using multiomic frameworks. Sci. Technol. Archaeol. Res. 7, 13–30 (2021).
Google Scholar 
Sawafuji, R., Saso, A., Suda, W., Hattori, M. & Ueda, S. Ancient DNA analysis of food remains in human dental calculus from the Edo period, Japan. PLoS One 15, e0226654 (2020).Article 
CAS 

Google Scholar 
Weyrich, L. S. et al. Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus. Nature 544, 357–361 (2017).Article 
CAS 

Google Scholar 
Ottoni, C. et al. Metagenomic analysis of dental calculus in ancient Egyptian baboons. Sci. Rep. 9, 1–10 (2019).Article 

Google Scholar 
Hollingsworth, P. M., Graham, S. W. & Little, D. P. Choosing and using a Plant DNA barcode. PLoS One 6, 1–13 (2011).Article 

Google Scholar 
Gismondi, A., Fanali, F., Labarga, J. M. M., Caiola, M. G. & Canini, A. Crocus sativus L. genomics and different DNA barcode applications. Plant Syst. Evol. 299, 1859–1863 (2013).Article 
CAS 

Google Scholar 
ICSN. The international code for starch nomenclature, accessed 15 September 2021; http://fossilfarm.org/ICSN/Code.html (2011).Gismondi, A. et al. Starch granules: a data collection of 40 food species. Plant Biosyst. 153, 273–279 (2019).Article 

Google Scholar 
Henry, A. G., Brooks, A. S. & Piperno, D. R. Plant foods and the dietary ecology of Neanderthals and early modern humans. J. Hum. Evol. 69, 44–54 (2014).Article 

Google Scholar 
PalDat. A palynological database (2000 onwards), accessed 19 January 2022; https://www.paldat.org/ (2019).Berglund, B. E. & Ralska-Jasiewiczowa, M. Pollen analysis and pollen diagrams. In Handbook of Holocene Palaeoecology and Palaeohydrology (ed. Berglund, B. E.) 455–484 (Wiley, 1986).Faegri, K. & Iversen, J. Textbook of Pollen analysis, 4th edn (eds Faegri, K. et al.) (John Wiley and Sons-Chichester, 1989).Grímsson, F. et al. Fagaceae pollen from the early Cenozoic of West Greenland: revisiting Engler’s and Chaney’s Arcto-Tertiary hypotheses. Plant Syst. Evol. 301, 809–832 (2015).Article 

Google Scholar 
Denk, T. & Tekleva, M. V. Pollen morphology and ultrastructure of Quercus with focus on Group Ilex (= Quercus Subgenus Heterobalanus (Oerst.) Menitsky): Implications for oak systematics and evolution. Grana 53, 255–282 (2014).Article 

Google Scholar 
Grímsson, F. & Zetter, R. Combined LM and SEM study of the middle Miocene (Sarmatian) palynofora from the Lavanttal Basin, Austria: Part II. Pinophyta (Cupressaceae, Pinaceae and Sciadopityaceae). Grana 50, 262–310 (2011).Article 

Google Scholar 
Mohanty, R. P., Buchheim, M. A., Portman, R. & Levetin, E. Molecular and ultrastructural detection of plastids in Juniperus (Cupressaceae) pollen. Phytologia 98, 298–310 (2016).
Google Scholar 
Martin, A. C. & Harvey, W. J. The Global Pollen Project: a new tool for pollen identifcation and the dissemination of physical reference collections. Methods Ecol. Evol. 8, 892–897 (2017).Article 

Google Scholar 
Maciejewska-Rutkowska, I., Bocianowski, J. & Wrońska-Pilarek, D. Pollen morphology and variability of Polish native species from genus Salix L. PLoS One 16, e0243993 (2021).Article 
CAS 

Google Scholar 
Abreu, I., Costa, I., Oliveira, M., Cunha, M. & De Castro, R. Ultrastructure and germination of Vitis vinifera cv. Loureiro pollen. Protoplasma 228, 131–135 (2006).Article 
CAS 

Google Scholar 
Nagels, A. et al. Palynological diversity and major evolutionary trends in Cyperaceae. Plant Syst. Evol. 277, 117 (2009).Article 

Google Scholar 
El Ghazali, G. E. Pollen morphological studies in Amaranthaceae s. lat. (incl. Chenopodiaceae) and their taxonomic significance: a review. Grana 61, 1–7 (2022).Article 

Google Scholar 
Petraco, N., & Kubic, T. Color Atlas and Manual of Microscopy for Criminalists, Chemists, and Conservators (Boca Raton-CRC Press, 2003).D’Agostino, A. et al. Environmental implications and evidence of natural products from dental calculi of a Neolithic–Chalcolithic community (central Italy). Sci. Rep. 11, 1–13 (2021).Article 

Google Scholar 
Frangiote-Pallone, S. & de Souza, L. A. Pappus and cypsela ontogeny in Asteraceae: structural considerations of the tribal category. Rev. Mex. Biodivers. 85, 62–77 (2014).Article 

Google Scholar 
Eglinton, G., Gonzalez, A. G., Hamilton, R. J. & Raphael, R. A. Hydrocarbon constituents of the wax coatings of plant leaves: a taxonomic survey. Phytochemistry 1, 89–102 (1962).Article 
CAS 

Google Scholar 
Buckley, S. A., Stott, A. W. & Evershed, R. P. Studies of organic residues from ancient Egyptian mummies using high temperature-gas chromatography-mass spectrometry and sequential thermal desorption-gas chromatography-mass spectrometry and pyrolysis-gas chromatography-mass spectrometry. Analyst 124, 443–452 (1999).Article 
CAS 

Google Scholar 
Hardy, K. et al. Neanderthal medics? Evidence for food, cooking, and medicinal plants entrapped in dental calculus. Naturwissenschaften 99, 617–626 (2012).Article 
CAS 

Google Scholar 
Luong, S., Tocheri, M. W., Sutikna, T., Saptomo, E. W. & Roberts, R. G. Incorporating terpenes, monoterpenoids and alkanes into multiresidue organic biomarker analysis of archaeological stone artefacts from Liang Bua (Flores, Indonesia). J. Archaeol. Sci. Rep. 19, 189–199 (2018).
Google Scholar 
Luong, S. et al. Combined organic biomarker and use-wear analyses of stone artefacts from Liang Bua, Flores, Indonesia. Sci. Rep. 9, 1–17 (2019).Article 
CAS 

Google Scholar 
Dabney, J., Meyer, M. & Pääbo, S. Ancient DNA damage. Cold Spring Harb. Perspect. Biol. 5, a012567 (2013).Article 

Google Scholar 
Mann, A. E. et al. Differential preservation of endogenous human and microbial DNA in dental calculus and dentin. Sci. Rep. 8, 1–15 (2018).Article 

Google Scholar 
Horrocks, M., Nieuwoudt, M. K., Kinaston, R., Buckley, H. & Bedford, S. Microfossil and Fourier Transform InfraRed analyses of Lapita and post-Lapita human dental calculus from Vanuatu, Southwest Pacific. J. R. Soc. N. Z. 44, 17–33 (2014).Article 

Google Scholar 
Radini, A., Nikita, E., Buckley, S., Copeland, L. & Hardy, K. Beyond food: the multiple pathways for inclusion of materials into ancient dental calculus. Am. J. Phys. Anthropol. 162, 71–83 (2017).Article 

Google Scholar 
Henry, A. G. Other microparticles: volcanic glass, minerals, insect remains, feathers, and other plant parts. In Handbook for the Analysis of Micro-Particles in Archaeological Samples 289–295 (Springer, Cham, 2020).MacKenzie, L., Speller, C. F., Holst, M., Keefe, K., & Radini, A. Dental calculus in the industrial age: human dental calculus in the Post-Medieval period, a case study from industrial Manchester. Quat. Int. https://doi.org/10.1016/j.quaint.2021.09.020 (2021).Radini, A., & Nikita, E. Beyond dirty teeth: Integrating dental calculus studies with osteoarchaeological parameters. Quat. Int. https://doi.org/10.1016/j.quaint.2022.03.003 (2022).Dobney, K. & Brothwell, D. A scanning electron microscope study of archaeological dental calculus. In Scanning Electron Microscopy in Archaeology BAR International Series (ed. & Olsen S), vol. 452, pp. 372–385 (Oxford, UK: BAR, 1988).Henry, A. G. & Piperno, D. R. Using plant microfossils from dental calculus to recover human diet: a case study from Tell al-Raqā’i, Syria. J. Archaeol. Sci. 35, 1943–1950 (2008).Article 

Google Scholar 
Wesolowski, V., de Souza, S. M. F. M., Reinhard, K. J. & Ceccantini, G. Evaluating microfossil content of dental calculus from Brazilian sambaquis. J. Archaeol. Sci. 37, 1326–1338 (2010).Article 

Google Scholar 
González-Guarda, E. et al. Multiproxy evidence for leaf-browsing and closed habitats in extinct proboscideans (Mammalia, Proboscidea) from Central Chile. Proc. Natl. Acad. Sci. USA 115, 9258–9263 (2018).Article 

Google Scholar 
Radley, J. A. Starch and its Derivatives (Chapman and Hall, London, 1968).Power, R. C., Salazar-García, D. C., Wittig, R. M. & Henry, A. G. Assessing use and suitability of scanning electron microscopy in the analysis of micro remains in dental calculus. J. Archaeol. Sci. 49, 160–169 (2014).Article 
CAS 

Google Scholar 
Rottoli, M. & Castiglioni, E. Prehistory of plant growing and collecting in northern Italy, based on seed remains from the early Neolithic to the Chalcolithic (c. 5600–2100 cal BC). Veg. Hist. Archaeobot. 18, 91–103 (2009).Article 

Google Scholar 
Fiorentino, G. et al. Climate changes and human–environment interactions in the Apulia region of southeastern Italy during the Neolithic period. Holocene 23, 1297–1316 (2013).Article 

Google Scholar 
Rottoli, M., & Pessina, A. Neolithic agriculture in Italy: an update of archaeobotanical data with particular emphasis on northern settlements. In The Origins and Spread of Domestic Plants in Southwest Asia and Europe 157–170 (Routledge, 2016).Arobba, D., Panelli, C., Caramiello, R., Gabriele, M. & Maggi, R. Cereal remains, plant impressions and 14C direct dating from the Neolithic pottery of Arene Candide Cave (Finale Ligure, NW Italy). J. Archaeol. Sci. Rep. 12, 395–404 (2017).
Google Scholar 
Ucchesu, M., Sau, S. & Lugliè, C. Crop and wild plant exploitation in Italy during the Neolithic period: New data from Su Mulinu Mannu, Middle Neolithic site of Sardinia. J. Archaeol. Sci. Rep. 14, 1–11 (2017).
Google Scholar 
Scorrano, G. et al. Effect of Neolithic transition on an Italian community: Mora Cavorso (Jenne, Rome). Archaeol. Anthropol. Sci. 11, 1443–1459 (2019).Article 

Google Scholar 
De Angelis, F. et al. Exploring mobility in Italian Neolithic and Copper Age communities. Sci. Rep. 11, 1–14 (2021).Article 

Google Scholar 
Oxilia, G. et al. Exploring late Paleolithic and Mesolithic diet in the Eastern Alpine region of Italy through multiple proxies. Am. J. Phys. Anthropol. 174, 232–253 (2021).Article 

Google Scholar 
Fahmy, A. G. E. Palaeoethnobotanical studies of the Neolithic settlement in Hidden Valley, Farafra Oasis, Egypt. Veg. Hist. Archaeobot. 10, 235–246 (2001).Article 

Google Scholar 
Reed, K. From the field to the hearth: plant remains from Neolithic Croatia (ca. 6000–4000 cal bc). Veg. Hist. Archaeobot. 24, 601–619 (2015).Article 

Google Scholar 
Lucarini, G., Radini, A., Barton, H. & Barker, G. The exploitation of wild plants in Neolithic North Africa. Use-wear and residue analysis on non-knapped stone tools from the Haua Fteah cave, Cyrenaica, Libya. Quat. Int. 410, 77–92 (2016).Article 

Google Scholar 
García-Granero, J. J., Urem-Kotsou, D., Bogaard, A. & Kotsos, S. Cooking plant foods in the northern Aegean: microbotanical evidence from Neolithic Stavroupoli (Thessaloniki, Greece). Quat. Int. 496, 140–151 (2018).Article 

Google Scholar 
Bouby, L. et al. Early Neolithic (ca. 5850-4500 cal BC) agricultural diffusion in the Western Mediterranean: an update of archaeobotanical data in SW France. PLoS One 15, e0230731 (2020).Article 
CAS 

Google Scholar 
Delhon, C., Binder, D., Verdin, P. & Mazuy, A. Phytoliths as a seasonality indicator? The example of the Neolithic site of Pendimoun, south-eastern France. Veg. Hist. Archaeobot. 29, 229–240 (2020).Article 

Google Scholar 
Lu, H. et al. Phytoliths analysis for the discrimination of foxtail millet (Setaria italica) and common millet (Panicum miliaceum). PLoS One 4, e4448 (2009).Article 

Google Scholar 
Celant, A. Indagini paleobotaniche su macroresti vegetali dai siti neo-eneolitici del territorio di Roma. In Roma prima del mito. Abitati e necropoli dal Neolitico alla prima età dei Metalli nel territorio di Roma (VI-III millennio a.C.) (eds Anzidei, A. P. & Carboni, C.) Vol. 2, 687–704 (Archaeopress Archaeol., 2020).Carra, M. et al. Plant foods in the Late Palaeolithic of Southern Italy and Sicily: Integrating carpological and dental calculus evidence. Quat. Int. https://doi.org/10.1016/j.quaint.2022.06.007 (2022) .Bednar, G. E. et al. Starch and fiber fractions in selected food and feed ingredients affect their small intestinal digestibility and fermentability and their large bowel fermentability in vitro in a canine model. J. Nutr. 131, 276–286 (2001).Article 
CAS 

Google Scholar 
Hoover, R., Hughes, T., Chung, H. J. & Liu, Q. Composition, molecular structure, properties, and modification of pulse starches: a review. Food Res. Int. 43, 399–413 (2010).Article 
CAS 

Google Scholar 
Wani, I. A. et al. Isolation, composition, and physicochemical properties of starch from legumes: a review. Starch‐Stärke 68, 834–845 (2016).Article 
CAS 

Google Scholar 
Tayade, R., Kulkarni, K. P., Jo, H., Song, J. T. & Lee, J. D. Insight into the prospects for the improvement of seed starch in legume—a review. Front. Plant Sci. 10, 1213 (2019).Article 

Google Scholar 
Stafford, H. A. Distribution of tartaric acid in the leaves of certain angiosperms. Am. J. Bot. 46, 347–352 (1959).Article 
CAS 

Google Scholar 
DeBolt, S., Cook, D. R. & Ford, C. M. L-Tartaric acid synthesis from vitamin C in higher plants. Proc. Natl. Acad. Sci. USA 103, 5608–5613 (2006).Article 
CAS 

Google Scholar 
Fernández-García, E. et al. Carotenoids bioavailability from foods: from plant pigments to efficient biological activities. Food Res. Int. 46, 438–450 (2012).Article 

Google Scholar 
Gliszczyńska, A. & Brodelius, P. E. Sesquiterpene coumarins. Phytochem. Rev. 11, 77–96 (2012).Article 

Google Scholar 
Eerkens, J. The preservation and identification of Piñon resins by GC‐MS in pottery from the Western Great Basin. Archaeometry 44, 95–105 (2002).Article 
CAS 

Google Scholar 
Barnard, H. et al. Mixed results of seven methods for organic residue analysis applied to one vessel with the residue of a known foodstuff. J. Archaeol. Sci. 34, 28–37 (2007).Article 

Google Scholar 
Wysocka, W., Przybył, A. & Brukwicki, T. The structure of angustifoline, an alkaloid of Lupinus angustifolius, in solution. Monatsh. Chem. 125, 1267–1272 (1994).Article 
CAS 

Google Scholar 
Ohmiya, S., Saito, K., & Murakoshi, I. Lupine alkaloids. In The alkaloids: Chemistry and Pharmacology Vol. 47, 1–114) (Academic Press, 1995).Mancinotti, D., Frick, K. M. & Geu-Flores, F. Biosynthesis of quinolizidine alkaloids in lupins: mechanistic considerations and prospects for pathway elucidation. Nat. Prod. Rep. 39, 1423–1437 (2022).Article 
CAS 

Google Scholar 
Silvestri, L., Achino, K. F., Gatta, M., Rolfo, M. F. & Salari, L. Grotta Mora Cavorso: physical, material and symbolic boundaries of life and death practices in a Neolithic cave of central Italy. Quat. Int. 539, 29–38 (2020).Article 

Google Scholar 
Steele, V. J., Stern, B. & Stott, A. W. Olive oil or lard?: distinguishing plant oils from animal fats in the archaeological record of the eastern Mediterranean using gas chromatography/combustion/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 24, 3478–3484 (2010).Article 
CAS 

Google Scholar 
Buonasera, T. Investigating the presence of ancient absorbed organic residues in groundstone using GC–MS and other analytical techniques: a residue study of several prehistoric milling tools from central California. J. Archaeol. Sci. 34, 1379–1390 (2007).Article 

Google Scholar 
Luong, S. et al. Development and application of a comprehensive analytical workflow for the quantification of non-volatile low molecular weight lipids on archaeological stone tools. Anal. Met. 9, 4349–4362 (2017).Article 
CAS 

Google Scholar 
Baeten, J., Jervis, B., De Vos, D. & Waelkens, M. Molecular evidence for the mixing of Meat, Fish and Vegetables in Anglo‐Saxon coarseware from Hamwic, UK. Archaeometry 55, 1150–1174 (2013).Article 
CAS 

Google Scholar 
Evershed, R. P. Chemical composition of a bog body adipocere. Archaeometry 34, 253–265 (1992).Article 
CAS 

Google Scholar 
Garnier, N., Bernal-Casasola, D., Driard, C. & Pinto, I. V. Looking for ancient fish products through invisible biomolecular residues in the roman production vats from the Atlantic. Coast J. Marit. Archaeol. 13, 285–328 (2018).Article 

Google Scholar 
Copley, M. S., Bland, H. A., Rose, P., Horton, M. & Evershed, R. P. Gas chromatographic, mass spectrometric and stable carbon isotopic investigations of organic residues of plant oils and animal fats employed as illuminants in archaeological lamps from Egypt. Analyst 130, 860–871 (2005).Article 
CAS 

Google Scholar 
Reber, E. A. & Hart, J. P. Pine resins and pottery sealing: analysis of absorbed and visible pottery residues from central New York State. Archaeometry 50, 999–1017 (2008).Article 
CAS 

Google Scholar 
Simopoulos, A. P. Omega‐3 fatty acids in wild plants, nuts and seeds. Asia Pac. J. Clin. Nutr. 11, S163–S173 (2002).Article 
CAS 

Google Scholar 
Harris, W. S. et al. Stearidonic acid-enriched soybean oil increased the omega-3 index, an emerging cardiovascular risk marker. Lipids 43, 805–811 (2008).Article 
CAS 

Google Scholar 
Gismondi, A., Rolfo, M. F., Leonardi, D., Rickards, O. & Canini, A. Identification of ancient Olea europaea L. and Cornus mas L. seeds by DNA barcoding. C. R. Biol. 335, 472–479 (2012).Article 
CAS 

Google Scholar 
Steffens, W. & Wirth, M. Freshwater fish-an important source of n-3 polyunsaturated fatty acids: a review. Fish. Aquat. Sci. 13, 5–16 (2005).
Google Scholar 
Swanson, D., Block, R. & Mousa, S. A. Omega-3 fatty acids EPA and DHA: health benefits throughout life. Adv. Nutr. 3, 1–7 (2012).Article 
CAS 

Google Scholar 
Wiermann, R., & Gubatz, S. Pollen wall and sporopollenin. In International Review of Cytology 35–72 (Academic Press, 1992).Cristiani, E., Radini, A., Edinborough, M. & Borić, D. Dental calculus reveals Mesolithic foragers in the Balkans consumed domesticated plant foods. Proc. Natl. Acad. Sci. USA 113, 10298–10303 (2016).Article 
CAS 

Google Scholar 
Hardy, K. et al. Dental calculus reveals potential respiratory irritants and ingestion of essential plant-based nutrients at Lower Palaeolithic Qesem Cave Israel. Quat. Int. 398, 129–135 (2016).Article 

Google Scholar 
Radini, A. et al. Neanderthals, trees and dental calculus: new evidence from El Sidrón. Antiquity 90, 290–301 (2016).Article 

Google Scholar 
Lippi, M. M., Pisaneschi, L., Sarti, L., Lari, M. & Moggi-Cecchi, J. Insights into the Copper-Bronze Age diet in central Italy: plant microremains in dental calculus from Grotta dello Scoglietto (Southern Tuscany, Italy). J. Archaeol. Sci. Rep. 15, 30–39 (2017).
Google Scholar 
Modi, A. et al. Combined metagenomic and archaeobotanical analyses on human dental calculus: a cross-section of lifestyle conditions in a Copper Age population of central Italy. Quat. Int. https://doi.org/10.1016/j.quaint.2021.12.003 (2021).Warinner, C. et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. https://doi.org/10.1038/ng.2906 (2014).Lieverse, A. R. Diet and the aetiology of dental calculus. Int. J. Osteoarchaeol. 9, 219–232 (1999).Article 

Google Scholar 
Lukacs, J. R. & Largaespada, L. L. Explaining sex differences in dental caries prevalence: saliva, hormones, and “life‐history” etiologies. Am. J. Hum. Biol. 18, 540–555 (2006).Article 

Google Scholar 
Moore, P. D., Webb, J. A., & Collison, M. E. Pollen Analysis (Blackwell Scientific Publications, 1991).Borojević, K., Forenbaher, S., Kaiser, T. & Berna, F. Plant use at Grapčeva cave and in the eastern Adriatic Neolithic. J. Field Archaeol. 33, 279–303 (2008).Article 

Google Scholar 
Martin, L., Jacomet, S. & Tiebault, S. Plant economy during the Neolithic in a mountain context: the case of “Le Chenet des Pierres” in the French Alps (Bozel-Savoie, France). Veg. Hist. Archaeobot. 17, 113–122 (2008).Article 

Google Scholar 
Moser, D., Di Pasquale, G., Scarciglia, F. & Nelle, O. Holocene mountain forest changes in central Mediterranean: soil charcoal data from the Sila Massif (Calabria, southern Italy). Quat. Int. 457, 113–130 (2017).Article 

Google Scholar 
D’Agostino, A. et al. Pollen record of the Late Pleistocene–Holocene stratigraphic sequence and current plant biodiversity from Grotta Mora Cavorso (Simbruini Mountains, Central Italy). Ecol. Evol. 12, e9486 (2022).Radaeski, J. N., Bauermann, S. G. & Pereira, A. B. Poaceae pollen from Southern Brazil: distinguishing grasslands (campos) from forests by analyzing a diverse range of Poaceae species. Front. Plant Sci. 7, 1833 (2016).Article 

Google Scholar 
Turner, S. D. & Brown, A. G. Vitis pollen dispersal in and from organic vineyards: I. Pollen trap and soil pollen data. Rev. Palaeobot. Palynol. 129, 117–132 (2004).Article 

Google Scholar 
Marvelli, S., De’Siena, S., Rizzoli, E. & Marchesini, M. The origin of grapevine cultivation in Italy: the archaeobotanical evidence. Ann. Bot. 3, 155–163 (2013).
Google Scholar 
Riaz, S. et al. Genetic diversity analysis of cultivated and wild grapevine (Vitis vinifera L.) accessions around the Mediterranean basin and Central Asia. BMC Plant Biol. 18, 1–14 (2018).Article 

Google Scholar 
Arnold, C., Gillet, F., & Gobat, J. M. Situation de la vigne sauvage Vitis vinifera subsp. silvestris en Europe. Vitis 159–170 (1998).Terral, J. F. et al. Evolution and history of grapevine (Vitis vinifera) under domestication: new morphometric perspectives to understand seed domestication syndrome and reveal origins of ancient European cultivars. Ann. Bot. 105, 443–455 (2010).Article 

Google Scholar 
Buckley, S., Usai, D., Jakob, T., Radini, A. & Hardy, K. Dental calculus reveals unique insights into food items, cooking and plant processing in prehistoric central Sudan. PLoS One 9, e100808 (2014).Article 

Google Scholar 
Petrov, P. R., Popova, E. D. & Zlatanova, D. P. Niche partitioning among the red fox Vulpes vulpes (L.), stone marten Martes foina (Erxleben) and pine marten Martes martes (L.) in two mountains in Bulgaria. Acta Zool. Bulg. 68, 375–390 (2016).
Google Scholar 
Mikrjukov, K. A. Revision of genera and species composition of lower Centroheliozoa. II. Family Raphidiophryidae n. tam. Arch. Protistenkd. 147, 205–212 (1996).Article 

Google Scholar 
Cavalier-Smith, T. & von der Heyden, S. Molecular phylogeny, scale evolution and taxonomy of centrohelid heliozoa. Mol. Phylogen. Evol. 44, 1186–1203 (2007).Article 
CAS 

Google Scholar 
Mertens, K. N., Rengefors, K., Moestrup, Ø. & Ellegaard, M. A review of recent freshwater dinoflagellate cysts: taxonomy, phylogeny, ecology and palaeocology. Phycologia 51, 612–619 (2012).Article 

Google Scholar 
Zlatogursky, V. V. Raphidiophrys heterophryoidea sp. nov. (Centrohelida: Raphidiophryidae), the first heliozoan species with a combination of siliceous and organic skeletal elements. Eur. J. Protist. 48, 9–16 (2012).Article 

Google Scholar 
Prokina, K. I. & Mylnikov, A. P. Centrohelid heliozoans from freshwater habitats of different types of South Patagonia and Tierra del Fuego, Chile. Inland Water Biol. 12, 10–20 (2019).Article 

Google Scholar 
Siemensma, F. J. & Roijackers, M. M. A study of new and little- known acanthocystid heliozoans, and a proposed division of the genus Acanthocystis (Actinopoda, Heliozoea). Arch. Protistenkd. 135, 197 (1988a).Article 

Google Scholar 
Siemensma, F. J. & Roijackers, M. M. The genus Raphidiophrys (Actinopoda, Heliozoea): scale morphology and species distinctions. Arch. Protistenkd. 136 237–248 (1988).Taylor, W.D. & Sanders, R. W. PROTOZOA. In Ecology and Classification of North American Freshwater Invertebrates (eds Thorp, J. H. & Covich, A. P.) 43–96 (Academic Press, 2001).Manconi, R., & Pronzato, R. Global diversity of sponges (Porifera: Spongillina) in freshwater. In Freshwater Animal Diversity Assessment 27–33 (Springer, Dordrecht, 2007).Malone, C. & Stoddart, S. The neolithic site of San Marco, Gubbio (Perugia), Umbria: survey and excavation 1985–7. Pap. Br. Sch. Rome 60, 1–69 (1992).Article 

Google Scholar 
Rottoli, M. La Marmotta, Anguillara Sabazia (RM). Scavi 1989. Analisi paletnobotaniche: prime risultanze, Appendice 1 M.A. In La Marmotta” (Anguillara Sabazia, RM). Scavi 1989. Un abitato perilacustre di età neolitica (eds. Fugazzola Delpino, M. A., D’Eugenio, G. & Pessina, A.) Bullettino di Paletnologia Italiana 84, 305–315 (1993).Pini, R. Late Neolithic vegetation history at the pile‐dwelling site of Palù di Livenza (northeastern Italy). J. Quat. Sci. 19, 769–781 (2004).Article 

Google Scholar 
Tinner, W. et al. Holocene environmental and climatic changes at Gorgo Basso, a coastal lake in southern Sicily, Italy. Quat. Sci. Rev. 28, 1498–1510 (2009).Article 

Google Scholar 
Bieniek, A. Archaeobotanical analysis of some early Neolithic settlements in the Kujawy region, central Poland, with potential plant gathering activities emphasized. Veg. Hist. Archaeobot. 11, 33–40 (2002).Article 

Google Scholar 
Tolar, T., Jacomet, S., Velušček, A. & Čufar, K. Plant economy at a late Neolithic lake dwelling site in Slovenia at the time of the Alpine Iceman. Veg. Hist. Archaeobot. 20, 207–222 (2011).Article 

Google Scholar 
D’Agostino, A. et al. Investigating plant micro-remains embedded in dental calculus of the Phoenician inhabitants of Motya (Sicily, Italy). Plants 9, 1395 (2020).Article 

Google Scholar 
Mercader, J. et al. Exaggerated expectations in ancient starch research and the need for new taphonomic and authenticity criteria. Facets 3, 777–798 (2018).Article 

Google Scholar 
Adojoh, O., Fabienne, M., Duller, R. & Osterloff, P. Taxonomy and phytoecology of palynomorphs and non-pollen palynomorphs: a refined compendium from the West Africa Margin. Biodivers. Int. J. 3, 188–200 (2019).Article 

Google Scholar 
Knapp, M., Clarke, A. C., Horsburgh, K. A. & Matisoo-Smith, E. A. Setting the stage building and working in an ancient DNA laboratory. Ann. Anat. 194, 3 (2012).Article 
CAS 

Google Scholar 
Knapp, M., Lalueza-Fox, C. & Hofreiter, M. Re-inventing ancient human DNA. Investig. Genet. 6, 1 (2015).Article 

Google Scholar 
Gismondi, A. et al. Grapevine carpological remains revealed the existence of a Neolithic domesticated Vitis vinifera L. specimen containing ancient DNA partially preserved in modern ecotypes. J. Archaeol. Sci. 69, 75–84 (2016).Article 
CAS 

Google Scholar 
Llamas, B. et al. From the field to the laboratory: controlling DNA contamination in human ancient DNA research in the high-throughput sequencing era. Sci. Technol. Archaeol. Res. 3, 1–14 (2017).Le Moyne, C. & Crowther, A. Effects of chemical pre-treatments on modified starch granules: recommendations for dental calculus decalcification for ancient starch research. J. Archaeol. Sci. Rep. 35, 102762 (2021).
Google Scholar 
Rolfo, M. F., Achino, K. F., Fusco, I., Salari, L. & Silvestri, L. Reassessing human occupation patterns in the inner central Apennines in prehistory: the case-study of Grotta Mora Cavorso. J. Archaeol. Sci. Rep. 7, 358–367 (2016).
Google Scholar  More

Duggins, D. O., Simenstad, C. A. & Estes, J. A. Magnification of secondary producition by kelp detritus in coastal marine ecosystems. Science 1979(245), 170–173 (1989).Article 
ADS 

Google Scholar 
Hill, R. et al. Can macroalgae contribute to blue carbon? An Australian perspective. Limnol. Oceanogr. 60, 1689–1706 (2015).Article 
ADS 

Google Scholar 
Mann, K. H. Seaweeds: Their productivity and strategy for growth. Science 1979(182), 975–981 (1973).Article 
ADS 

Google Scholar 
Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).Article 

Google Scholar 
Giordano, M., Beardall, J. & Raven, J. A. CO2 concentrating mechanisms in algae: Mechanisms, environmental modulation, and evolution. Annu. Rev. Plant Biol. 56, 99–131 (2005).Article 
CAS 

Google Scholar 
Raven, J. A. & Beardall, J. The ins and outs of CO2. J. Exp. Bot. 67, 1–13 (2016).Article 
CAS 

Google Scholar 
Raven, J. A. et al. Seaweeds in cold seas: Evolution and carbon acquisition. Ann. Bot. 90, 525–536. https://doi.org/10.1093/aob/mcf171 (2002).Article 
CAS 

Google Scholar 
Raven, J. et al. Ocean Acidification due to Increasing Atmospheric Carbon Dioxide 1–68 (The Royal Society, 2005).
Google Scholar 
Kübler, J. E. & Dudgeon, S. R. Predicting effects of ocean acidification and warming on algae lacking carbon concentrating mechanisms. PLoS ONE 10, 1–19 (2015).Article 

Google Scholar 
Fernández, P. A., Hurd, C. L. & Roleda, M. Y. Bicarbonate uptake via an anion excange protein is the main mechanism of inorganic carbon acquisition by the giant kelp Macrocystis pyrifera (Laminariales, Phaeophyceae) under variable pH1. J. Phycol. 50, 1–11 (2014).Article 

Google Scholar 
Raven, J. A. et al. Mechanistic interpretation of carbon isotope discrimination by marine macroalgae and seagrasses. Funct. Plant Biol. 29, 355 (2002).Article 
CAS 

Google Scholar 
Raven, J. A., Cockell, C. S. & De La Rocha, C. L. The evolution of inorganic carbon concentrating mechanisms in photosynthesis. Philos. Trans. R. Soc. B 363, 2641–2650 (2008).Article 
CAS 

Google Scholar 
Bidwell, R. G. S. S. & McLachlan, J. Carbon nutrition of seaweeds: Photosynthesis, photorespiration and respiration. J. Exp. Mar. Biol. Ecol. 86, 15–46 (1985).Article 
CAS 

Google Scholar 
Hurd, C. L. Water motion, marine macroalgal physiology and production. J. Phycol. 36, 453–472. https://doi.org/10.1046/j.1529-8817.2000.99139.x (2000).Article 
CAS 

Google Scholar 
Hurd, C. L., Stevens, C. L., Laval, B. E., Lawrence, G. A. & Harrison, P. J. Visualization of seawater flow around morphologically distinct forms of the giant kelp Macrocystis integrifolia from wave-sheltered and exposed sites. Limnol. Oceanogr. 42, 156–163. https://doi.org/10.4319/lo.1997.42.1.0156 (1997).Article 
ADS 

Google Scholar 
Smith, F. A. A. & Walker, N. A. A. Photosynthesis by aquatic plants: Effects of unstirred layers in relation to assimilation of CO2 and HCO3- to carbon isotope discrimination. N. Phytol. 86, 245–259 (1980).Article 
CAS 

Google Scholar 
Wheeler, W. N. Effect of boundary layer transport on the fixation of carbon by the giant kelp Macrocystis pyrifera. Mar. Biol. 56, 103–110 (1980).Article 
ADS 
CAS 

Google Scholar 
Hurd, C. L., Lenton, A., Tilbrook, B. & Boyd, P. W. Current understanding and challenges for oceans in a higher-CO2 world. Nat. Clim. Chang. 8, 686–694 (2018).Article 
ADS 
CAS 

Google Scholar 
Stocker, T. F. et al. Technical Summary. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 33–115 (2013).Hepburn, C. D. et al. Diversity of carbon use strategies in a kelp forest community: Implications for a high CO2 ocean. Glob. Chang. Biol. 17, 2488–2497 (2011).Article 
ADS 

Google Scholar 
Beer, S. & Koch, E. Photosynthesis of marine macroalgae and seagrasses in globally changing CO2 environments. Mar. Ecol. Prog. Ser. 141, 199–204 (1996).Article 
ADS 

Google Scholar 
Ihnken, S., Roberts, S. & Beardall, J. Differential responses of growth and photosynthesis in the marine diatom Chaetoceros muelleri to CO2 and light availability. Phycologia 50, 182–193 (2011).Article 
CAS 

Google Scholar 
Gerard, V. A. In situ water motion and nutrient uptake by the giant kelp Macrocystis pyrifera. Mar. Biol. 69, 51–54 (1982).Article 

Google Scholar 
Hepburn, C. D., Holborow, J. D., Wing, S. R., Frew, R. D. & Hurd, C. L. Exposure to waves enhances the growth rate and nitrogen status of the giant kelp Macrocystis pyrifera. Mar. Ecol. Prog. Ser. 339, 99–108 (2007).Article 
ADS 
CAS 

Google Scholar 
Hurd, C. L. Shaken and stirred: The fundamental role of water motion in resource acquisition and seaweed productivity. Persp. Phycol. 4, 73–81 (2017).ADS 

Google Scholar 
Sültemeyer, D. F., Miller, A. G., Espie, G. S., Fock, H. P. & Canvin, D. T. Active CO2 transport by the green alga Chlamydomonas reinhardtii. Plant Physiol. 89, 1213–1219 (1989).Article 

Google Scholar 
Koch, M., Bowes, G., Ross, C. & Zhang, X. H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Chang. Biol. 19, 103–132 (2013).Article 
ADS 

Google Scholar 
Britton, D., Cornwall, C. E., Revill, A. T., Hurd, C. L. C. L. & Johnson, C. R. Ocean acidification reverses the positive effects of seawater pH fluctuations on growth and photosynthesis of the habitat-forming kelp Ecklonia radiata. Sci. Rep. 6, 1–10 (2016).Article 

Google Scholar 
Cornwall, C. E. et al. Carbon-use strategies in macroalgae: Differential responses to lowered ph and implications for ocean acidification. J. Phycol. 48, 137–144 (2012).Article 
CAS 

Google Scholar 
Kram, S. L. et al. Variable responses of temperate calcified and fleshy macroalgae to elevated pCO2 and warming. ICES J. Mar. Sci. 73, 693–703 (2016).Article 

Google Scholar 
Kübler, J. E., Johnston, A. M. & Raven, J. A. The effects of reduced and elevated CO2 and O2 on the seaweed Lomentaria articulata. Plant Cell Environ. 22, 1303–1310 (1999).Article 

Google Scholar 
van der Loos, L. M. et al. Responses of macroalgae to CO2 enrichment cannot be inferred solely from their inorganic carbon uptake strategy. Ecol. Evol. 9, 125–140 (2019).Article 

Google Scholar 
Cornwall, C. E. & Hurd, C. L. Variability in the benefits of ocean acidification to photosynthetic rates of macroalgae without CO2-concentrating mechanisms. Mar. Freshw. Res. 71, 275–280 (2019).Article 

Google Scholar 
Cornwall, C. E., Revill, A. T. & Hurd, C. L. High prevalence of diffusive uptake of CO2 by macroalgae in a temperate subtidal ecosystem. Photosynth. Res. 124, 181–190 (2015).
Article 
CAS 

Google Scholar 
Lovelock, C. E., Reef, R., Raven, J. A. & Pandolfi, J. M. Regional variation in δ13C of coral reef macroalgae. Limnol. Oceanogr. 65, 2291–2302 (2020).Article 
ADS 
CAS 

Google Scholar 
Fischer, G. & Wiencke, C. Stable carbon isotope composition, depth distribution and fate of macroalgae from the Antarctic Peninsula region. Polar. Biol. 12, 341–348 (1992).Article 

Google Scholar 
Stephens, T. A. & Hepburn, C. D. Mass-transfer gradients across kelp beds influence Macrocystis pyrifera growth over small spatial scales. Mar. Ecol. Prog. Ser. 515, 97–109 (2014).Article 
ADS 

Google Scholar 
Kregting, L. T., Hepburn, C. D. & Savidge, G. Seasonal differences in the effects of oscillatory and uni-directional flow on the growth and nitrate-uptake rates of juvenile Laminaria digitata (Phaeophyceae). J. Phycol. 51, 1116–1126 (2015).Article 
CAS 

Google Scholar 
Parker, H. S. Influence of relative water motion on the growth, ammonium uptake and carbon and nitrogen composition of Ulva lactuca (Chlorophyta). Mar. Biol. 63, 309–318 (1981).Article 
CAS 

Google Scholar 
Bergstrom, E. et al. Inorganic carbon uptake strategies in coralline algae: Plasticity across evolutionary lineages under ocean acidification and warming. Mar. Environ. Res. 161, 105107 (2020).Article 
CAS 

Google Scholar 
Maberly, S. C., Raven, J. A. & Johnston, A. M. Discrimination between C-12 and C-13 by marine plants. Oecologia 91, 481–492 (1992).Article 
ADS 
CAS 

Google Scholar 
Gattuso, J. P. et al. Package ‘Seacarb ’. Preprint at http://cran.r-project.org/package=seacarb (2015).Raven, J. A., Beardall, J. & Giordano, M. Energy costs of carbon dioxide concentrating mechanisms in aquatic organisms. Photosynth. Res. 121, 111–124 (2014).Article 
CAS 

Google Scholar 
Raven, J. A., Walker, D. I., Johnston, A. M., Handley, L. L. & Kübler, J. E. Implications of 13C natural abundance measurements for photosynthetic performance by marine macrophytes in their natural environment. Mar. Ecol. Prog. Ser. 123, 193–205 (1995).Article 
ADS 

Google Scholar 
Raven, J. A. Inorganic carbon acquisition by marine autotrophs. Adv. Bot. Res. 27, 85–209 (1997).Article 
CAS 

Google Scholar 
Fernández, P. A., Roleda, M. Y. & Hurd, C. L. Effects of ocean acidification on the photosynthetic performance, carbonic anhydrase activity and growth of the giant kelp Macrocystis pyrifera. Photosynth. Res. 124, 293–304 (2015).Article 

Google Scholar 
Bailly, J. & Coleman, J. R. Effect of CO(2) concentration on protein biosynthesis and carbonic anhydrase expression in Chlamydomonas reinhardtii. Plant Physiol. 87, 833–840 (1988).Article 
CAS 

Google Scholar 
Dionisio-Sese, M. L., Fukuzawa, H. & Miyachi, S. Light-induced carbonic anhydrase expression in Chlamydomonas reinhardtii. Plant Physiol. 94, 1103–1110 (1990).Article 
CAS 

Google Scholar 
Pollock, S. V., Colombo, S. L., Prout, D. L., Godfrey, A. C. & Moroney, J. V. Rubisco activase is required for optimal photosynthesis in the green alga Chlamydomonas reinhardtii in a low-CO2 atmosphere. Plant Physiol. 133, 1854–1861 (2003).Article 
CAS 

Google Scholar 
Carlberg, S., Axelsson, L., Larsson, C., Ryberg, H. & Uusitalo, J. Inducible CO2 concentrating mechanisms in green seaweeds I. Taxonomical and physiological aspects. In Current Research in Photosynthesis (ed. Baltscheffsky, M.) (Springer, 1990). https://doi.org/10.1007/978-94-009-0511-5_749.Chapter 

Google Scholar 
Wheeler, W. N. Effect of boundary-layer transport on the fixation of carbon by the giant-kelp Macrocystis pyrifera. Mar. Biol. 56, 103–110 (1980).Article 
ADS 
CAS 

Google Scholar 
Johnston, A. M. & Raven, J. A. Effects of culture in high CO2 on the photosynthetic physiology of Fucus serratus. Br. J. Phycol. 25, 75–82 (1990).Article 

Google Scholar 
Connell, S. D., Kroeker, K. J., Fabricius, K. E., Kline, D. I. & Russell, B. D. The other ocean acidification problem: CO2 as a resource among competitors for ecosystem dominance. Philos. Trans. R. Soc. Lond. 368, 20120442 (2013).Article 

Google Scholar 
Porter, E. T., Sanford, L. P. & Suttles, S. E. Gypsum dissolution is not a universal integrator of water motion. Limnol. Oceanogr. 45, 145–158 (2000).Article 
ADS 

Google Scholar 
Gerard, V. A. & Mann, K. H. Growth and production of Laminaria longicruris (Phaeophyta) populations exposed to different intensities of water movement. J. Phycol. 15, 33–41 (1979).Article 

Google Scholar 
Bivand, R., Keitt, T. & Rowlingson, B. Package ‘rgdal’. R Package https://doi.org/10.1353/lib.0.0050 (2016).Article 

Google Scholar 
LINZ. LINZ Data Service. https://data.linz.govt.nz/layer/50258-nz-coastlines-topo-150k/history/ Accessed July 2021 (2021).Kirk, J. T. Characteristics of the light field in highly turbid waters: A Monte Carlo study. Limnol. Oceanogr. 39, 702–706 (1994).Article 
ADS 

Google Scholar 
Strickland, J. D. H. & Parsons, T. R. A Practical Handbook of Seawater Analysis (Fisheries Research Board of Canada, 1968).
Google Scholar 
Kohler, K. E. & Gill, S. M. Coral Point Count with Excel extensions (CPCe): A visual basic program for the determination of coral and substrate coverage using random point count methodology. Comput. Geosci. 32, 1259–1269 (2006).Article 
ADS 

Google Scholar 
Axelsson, L., Mercado, J. & Figueroa, F. Utilization of HCO3− at high ph by the brown macroalga laminaria saccharina. Eur. J. Phycol. 35, 53–59 (2000).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. Preprint at (2017).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).Article 
MathSciNet 
MATH 

Google Scholar  More

National negotiators inked a deal to protect nature in the early hours of 19 December in Montreal.Credit: Julian Haber/UN Biodiversity (CC BY 2.0)

Despite earlier signals of possible failure, countries around the world have cemented a deal to safeguard nature — and for the first time, the agreement sets quantitative biodiversity targets akin to the one that nations set seven years ago to limit global warming to 1.5–2 ºC above pre-industrial levels.In the early hours of 19 December, more than 190 countries eked out the deal, known as the Kunming-Montreal Global Biodiversity Framework, during the COP15 international biodiversity summit in Montreal, Canada. A key target it sets is for nations to protect and restore 30% of the world’s land and seas globally by 2030, while also respecting the rights of Indigenous peoples who depend on and steward much of Earth’s remaining biodiversity. Another target is for nations to reduce the extinction rate by 10-fold for all species by 2050.
10 startling images of nature in crisis — and the struggle to save it
Steven Guilbeault, the Canadian environment minister, described COP15 as the most significant biodiversity conference ever held. “We have taken a great step forward in history,” he said at a plenary session where the framework was adopted.At several points during the United Nations summit, which ran from 7–19 December, arguments over details threatened to derail a deal. In the final hours of negotiations, the Democratic Republic of the Congo (DRC) objected to how the framework would be funded. Nonetheless, Huang Runqiu, China’s environment minister and president of COP15, brought the gavel down on the agreement.Negotiators from several African countries, which are home to biodiversity hotspots but say they need funding to preserve those areas, thought that China’s presidency strong-armed the deal. Uganda called it “fraud”. A source who spoke to Nature from the African delegation, and who asked not to be named to maintain diplomacy, said the negotiating process was not equitable towards developing countries and that the deal will not enable significant progress towards stemming biodiversity loss. “It was a coup d’état,” they say. However, a legal expert for the Convention on Biological Diversity — the treaty within which the framework now sits — told COP15 attendees that the adoption of the framework is legitimate.Concerns and disappointmentsScientists and conservation groups have welcomed the deal, emphasizing that there has never been an international agreement to protect nature on this scale. Kina Murphy, an ecologist and chief scientist at the Campaign for Nature, a conservation group, says, “It’s a historic moment for biodiversity.”

Huang Runqiu, China’s environment minister and president of COP15, brought the gavel down on the biodiversity deal, despite objections from representatives of the Democratic Republic of the Congo.Credit: Julian Haber/UN Biodiversity (CC BY 2.0)

But some concerns and disappointments remain. For one, the deal lacks a mandatory requirement for companies to track and disclose their impact on biodiversity. “Voluntary action is not enough,” says Eva Zabey, executive director of Business for Nature, a global coalition of 330 businesses seeking such a requirement so that firms can compete on a level playing field. Nevertheless, it sends a powerful signal to industry that it will need to reduce negative impacts over time, says Andrew Deutz, an environmental law and finance specialist at the Nature Conservancy, a conservation group in Arlington, Virginia.In addition, the deal is weak on tackling the drivers of biodiversity loss, because it does not specifically call out the most ecologically damaging industries, such as commercial fishing and agriculture, or set precise targets for them to put biodiversity conservation at the centre of their operations, researchers say.
Can the world save a million species from extinction?
“I would have liked more ambition and precision in the targets” to address those drivers, says Sandra Diaz, an ecologist at the National University of Córdoba, in Argentina.The deal is not legally binding, but countries will have to demonstrate progress towards achieving the framework’s goals through national and global reviews. Countries failed to meet the previous Aichi Biodiversity Targets, which were set in 2010 and expired in 2020; scientists have suggested that this failure occurred because of a lack of an accountability mechanism.With the reviews included, the framework “is a very good start, with clear quantitative targets” that will allow us to understand progress and the reasons for success and failure, says Stuart Pimm, an ecologist at Duke University in Durham, North Carolina, and head of Saving Nature, a non-profit conservation organization.A long time comingScientists have estimated that one million species are under threat because of habitat loss, mainly through converting land for agriculture. And they have warned that this biodiversity loss could threaten the health of ecosystems on which humans depend for clean water and disease prevention, and called for a new international conservation effort.
Crucial biodiversity summit will go ahead in Canada, not China: what scientists think
The new agreement took 4 years to resolve, in part because of delays caused by the COVID-19 pandemic (the summit was supposed to take place in Kunming, China, in 2020), but also because of arguments over how to finance conservation efforts. Nations finally agreed that by 2030, funding for biodiversity from all public and private sources must rise to at least US$200 billion per year. This includes at least $30 billion per year, contributed from wealthy to low-income nations. These figures fall short of the approximately $700 billion that researchers say is needed to fully safeguard and restore nature, but represents a tripling of existing donations.Low- and middle-income countries (LMICs), including the DRC, had called for a brand-new, independent fund for biodiversity financing. Lee White, environment minister from Gabon, told Nature that biodiversity-rich LMICs have difficulty accessing the Global Environment Facility (GEF), the current fund held by the World Bank in Washington DC, and that it is slow to distribute funds.But France and the European Union strongly objected to a new fund, arguing it would take too long to set up. The framework instead compromises by establishing a trust fund by next year under the GEF. The final agreement also calls on the GEF to reform its process to address the concerns of LMICs.Progress without drastic changeAnother sticking point during negotiations was how to fairly and equitably share the benefits of ‘digital sequence information’ — genetic data collected from plants, animals and other organisms. Communities in biodiversity-rich regions where genetic material is collected have little control over the commercialization of the data, and no way to recoup financial or other benefits from them. But countries came to an agreement to set up a mechanism to share profits, the details of which will be worked out by the next international biodiversity summit, COP16, in 2024.Overall, the deal marks progress toward tackling biodiversity loss, but it is not the drastic change scientists say they were hoping for. “I am not so sure that it has enough teeth to curb the activities that do most of the harm,” Diaz says. More

Orr, M. R. & Smith, T. B. Ecology and speciation. Trends Ecol. Evol. 13, 502–506 (1998).Article 
CAS 

Google Scholar 
Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates, 2004).
Google Scholar 
Gillespie, R. G. Adaptive radiation: Convergence and non-equilibrium. Curr. Biol. 23, R71–R74 (2013).Article 
CAS 

Google Scholar 
Price, T. Speciation in Birds (Roberts and Company Publishers, 2008).
Google Scholar 
Schluter, D. Evidence for ecological speciation and its alternative. Science 323, 737–741 (2009).Article 
ADS 
CAS 

Google Scholar 
Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47, 507–532 (2016).Article 

Google Scholar 
Jønsson, K. A. et al. Ecological and evolutionary determinants for the adaptive radiation of the Madagascan vangas. Proc. Natl. Acad. Sci. 109, 6620–6625 (2012).Article 
ADS 

Google Scholar 
Wiens, J. J. Speciation and ecology revisited: Phylogenetic niche conservatism and the origin of species. Evolution 58, 193–197 (2004).
Google Scholar 
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).Article 

Google Scholar 
Wiens, J. J. & Graham, C. H. Niche Conservatism: Integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).Article 

Google Scholar 
Petitpierre, B. et al. Climatic niche shifts are rare among terrestrial plant invaders. Science 335, 1344–1348 (2012).Article 
ADS 
CAS 

Google Scholar 
Winger, B. M., Barker, F. K. & Ree, R. H. Temperate origins of long-distance seasonal migration in New World songbirds. Proc. Natl. Acad. Sci. 111, 12115–12120 (2014).Article 
ADS 
CAS 

Google Scholar 
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: Evolution and determinants. Oikos 103, 247–260 (2003).Article 

Google Scholar 
Gómez, C., Tenorio, E. A., Montoya, P. & Cadena, C. D. Niche-tracking migrants and niche-switching residents: Evolution of climatic niches in New World warblers (Parulidae). Proc. R. Soc. B Biol. Sci. 283, 20152458 (2016).Article 

Google Scholar 
Menchaca, A., Arteaga, M. C., Medellin, R. A. & Jones, G. Conservation units and historical matrilineal structure in the tequila bat (Leptonycteris yerbabuenae). Glob. Ecol. Conserv. 23, e01164 (2020).Article 

Google Scholar 
Medellín, R. A. et al. Follow me: Foraging distances of Leptonycteris yerbabuenae (Chiroptera: Phyllostomidae) in Sonora determined by fluorescent powder. J. Mammal. 99, 306–311 (2018).Article 

Google Scholar 
Broennimann, O. et al. Evidence of climatic niche shift during biological invasion. Ecol. Lett. 10, 701–709 (2007).Article 
CAS 

Google Scholar 
Martínez-Meyer, E., Peterson, A. T. & Hargrove, W. W. Ecological niches as stable distributional constraints on mammal species, with implications for Pleistocene extinctions and climate change projections for biodiversity. Glob. Ecol. Biogeogr. 13, 305–314 (2004).Article 

Google Scholar 
Soto-Centeno, J. A. & Steadman, D. W. Fossils reject climate change as the cause of extinction of Caribbean bats. Sci. Rep. 5, 7971 (2015).Article 
ADS 
CAS 

Google Scholar 
Avise, J. C. Phylogeography: The History and Formation of Species (Harvard University Press, 2000).Book 

Google Scholar 
Hickerson, M. J. et al. Phylogeography’s past, present, and future: 10 years after Avise, 2000. Mol. Phylogenet. Evol. 54, 291–301 (2010).Article 
CAS 

Google Scholar 
Pahad, G., Montgelard, C. & Jansen van Vuuren, B. Phylogeography and niche modelling: Reciprocal enlightenment. Mammalia 84, 10–25 (2019).Article 

Google Scholar 
Flanders, J. et al. Phylogeography of the greater horseshoe bat, Rhinolophus ferrumequinum: Contrasting results from mitochondrial and microsatellite data. Mol. Ecol. 18, 306–318 (2009).Article 
CAS 

Google Scholar 
Machado, A. F. et al. Integrating phylogeography and ecological niche modelling to test diversification hypotheses using a Neotropical rodent. Evol. Ecol. 33, 111–148 (2019).Article 

Google Scholar 
Kalkvik, H. M., Stout, I. J., Doonan, T. J. & Parkinson, C. L. Investigating niche and lineage diversification in widely distributed taxa: Phylogeography and ecological niche modeling of the Peromyscus maniculatus species group. Ecography 35, 54–64 (2012).Article 

Google Scholar 
Wang, Y. et al. Ring distribution patterns—diversification or speciation? Comparative phylogeography of two small mammals in the mountains surrounding the Sichuan Basin. Mol. Ecol. 30, 2641–2658 (2021).Article 

Google Scholar 
Soto-Centeno, J. A., Barrow, L. N., Allen, J. M. & Reed, D. L. Reevaluation of a classic phylogeographic barrier: New techniques reveal the influence of microgeographic climate variation on population divergence. Ecol. Evol. 3, 1603–1613 (2013).Article 

Google Scholar 
Amador, L. I., Moyers Arévalo, R. L., Almeida, F. C., Catalano, S. A. & Giannini, N. P. Bat systematics in the light of unconstrained analyses of a comprehensive molecular supermatrix. J. Mamm. Evol. 25, 37–70 (2018).Article 

Google Scholar 
Rojas, D., Warsi, O. M. & Dávalos, L. M. Bats (Chiroptera: Noctilionoidea) challenge a recent origin of extant neotropical diversity. Syst. Biol. 65, 432–448 (2016).Article 

Google Scholar 
Shi, J. J. & Rabosky, D. L. Speciation dynamics during the global radiation of extant bats. Evolution 69, 1528–1545 (2015).Article 

Google Scholar 
Dumont, E. R. et al. Morphological innovation, diversification and invasion of a new adaptive zone. Proc. Biol. Sci. 279, 1797–1805 (2012).
Google Scholar 
Leiser-Miller, L. B. & Santana, S. E. Morphological diversity in the sensory system of phyllostomid bats: Implications for acoustic and dietary ecology. Funct. Ecol. 34, 1416–1427 (2020).Article 

Google Scholar 
Hedrick, B. P. & Dumont, E. R. Putting the leaf-nosed bats in context: A geometric morphometric analysis of three of the largest families of bats. J. Mammal. 99, 1042–1054 (2018).Article 

Google Scholar 
Clare, E. L. Cryptic species? Patterns of maternal and paternal gene flow in eight neotropical bats. PLoS One 6, e21460 (2011).Article 
ADS 
CAS 

Google Scholar 
Chaverri, G. et al. Unveiling the hidden bat diversity of a neotropical montane forest. PLoS One 11, e0162712 (2016).Article 

Google Scholar 
Calahorra-Oliart, A., Ospina-Garcés, S. M. & León-Paniagua, L. Cryptic species in Glossophaga soricina (Chiroptera: Phyllostomidae): Do morphological data support molecular evidence?. J. Mammal. 102, 54–68 (2021).Article 

Google Scholar 
Lim, B. K., Loureiro, L. O. & Garbino, G. S. T. Cryptic diversity and range extension in the big-eyed bat genus Chiroderma (Chiroptera, Phyllostomidae). Zookeys 918, 41–63 (2020).Article 

Google Scholar 
Loureiro, L. O., Engstrom, M., Lim, B., González, C. L. & Juste, J. Not all Molossus are created equal: Genetic variation in the mastiff bat reveals diversity masked by conservative morphology. Acta Chiropterologica 21, 51 (2019).Article 

Google Scholar 
Morales, A., Villalobos, F., Velazco, P. M., Simmons, N. B. & Piñero, D. Environmental niche drives genetic and morphometric structure in a widespread bat. J. Biogeogr. 43, 1057–1068 (2016).Article 

Google Scholar 
Hedrick, B. P. et al. Morphological diversification under high integration in a hyper diverse mammal clade. J. Mamm. Evol. 27, 563–575 (2020).Article 

Google Scholar 
Morales, A. E. & Carstens, B. C. Evidence that myotis lucifugus “subspecies” are five nonsister species, despite gene flow. Syst. Biol. 67, 756–769 (2018).Article 

Google Scholar 
Simmons, N. B. & Cirranello, A. L. Bat species of the world: A taxonomic and geographic database. https://batnames.org.Russell, A. L., Pinzari, C. A., Vonhof, M. J., Olival, K. J. & Bonaccorso, F. J. Two tickets to paradise: Multiple dispersal events in the founding of hoary bat populations in Hawai’i. PLoS One 10, 1–13 (2015).
Google Scholar 
Shump, K. A. & Shump, A. U. Lasiurus cinereus. Mamm. Species 185, 1–5 (1982).
Google Scholar 
Ziegler, A. C., Howarth, F. G. & Simmons, N. B. A second endemic land mammal for the Hawaiian Islands: A new genus and species of fossil bat (Chiroptera: Vespertilionidae). Am. Museum Novit. 1–52 (2016).Bonaccorso, F. J. & McGuire, L. P. Modeling the colonization of Hawaii by hoary bats (Lasiurus cinereus). In Bat Evolution, Ecology, and Conservation (eds Adams, R. A. & Pedersen, S. C.) 187–205 (Springer, 2013).Chapter 

Google Scholar 
Baird, A. B. et al. Molecular systematic revision of tree bats (Lasiurini): Doubling the native mammals of the Hawaiian Islands. J. Mammal. 96, 1255–1274 (2015).Article 

Google Scholar 
Jacobs, D. S. Morphological divergence in an insular bat, Lasiurus cinereus semotus. Funct. Ecol. 10, 622–630 (1996).Article 

Google Scholar 
Baird, A. B. et al. Nuclear and mtDNA phylogenetic analyses clarify the evolutionary history of two species of native Hawaiian bats and the taxonomy of Lasiurini (Mammalia: Chiroptera). PLoS One 12, e0186085 (2017).Article 

Google Scholar 
Kumar, S. & Subramanian, S. Mutation rates in mammalian genomes. Proc. Natl. Acad. Sci. U.S.A. 99, 803–808 (2002).Article 
ADS 
CAS 

Google Scholar 
Gillespie, R. G. et al. Comparing adaptive radiations across space, time, and taxa. J. Hered. 111, 1–20 (2020).Article 

Google Scholar 
Fišer, C., Robinson, C. T. & Malard, F. Cryptic species as a window into the paradigm shift of the species concept. Mol. Ecol. 27, 613–635 (2018).Article 

Google Scholar 
Espíndola, A. et al. Identifying cryptic diversity with predictive phylogeography. Proc. R. Soc. B Biol. Sci. 283, 20161529 (2016).Article 

Google Scholar 
Padial, J. M., Miralles, A., De la Riva, I. & Vences, M. The integrative future of taxonomy. Front. Zool. 7, 1–14 (2010).Article 

Google Scholar 
Fujita, M. K., Leaché, A. D., Burbrink, F. T., McGuire, J. A. & Moritz, C. Coalescent-based species delimitation in an integrative taxonomy. Trends Ecol. Evol. 27, 480–488 (2012).Article 

Google Scholar 
Solari, S., Sotero-Caio, C. G. & Baker, R. J. Advances in systematics of bats: Towards a consensus on species delimitation and classifications through integrative taxonomy. J. Mammal. 100, 838–851 (2018).Article 

Google Scholar 
Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).
Google Scholar 
Morales, A. E., De-la-Mora, M. & Piñero, D. Spatial and environmental factors predict skull variation and genetic structure in the cosmopolitan bat Tadarida brasiliensis. J. Biogeogr. 45, 1529–1540 (2018).Article 

Google Scholar 
Pavan, A. C. & Marroig, G. Integrating multiple evidences in taxonomy: Species diversity and phylogeny of mustached bats (Mormoopidae: Pteronotus). Mol. Phylogenet. Evol. 103, 184–198 (2016).Article 

Google Scholar 
Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: A fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).Article 
CAS 

Google Scholar 
Robinson, D. & Foulds, L. Comparison of phylogenetic trees. Math. Biosci. 53, 131–147 (1981).Article 
MathSciNet 
MATH 

Google Scholar 
Pattengale, N. D., Alipour, M., Bininda-Emonds, O. R., Moret, B. M. & Stamatakis, A. How many bootstrap replicates are necessary?. J. Comput. Biol. 17, 337–354 (2010).Article 
MathSciNet 
CAS 

Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).Article 
ADS 
CAS 

Google Scholar 
Ronquist, F. et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).Article 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).Article 
CAS 

Google Scholar 
Kapli, P. et al. Multi-rate Poisson Tree Processes for single-locus species delimitation under Maximum Likelihood and Markov Chain Monte Carlo. Bioinformatics 33, 1630–1638 (2017).CAS 

Google Scholar 
Yang, Z. & Rannala, B. Unguided species delimitation using DNA sequence data from multiple loci. Mol. Biol. Evol. 31, 3125–3135 (2014).Article 
CAS 

Google Scholar 
Flouri, T., Jiao, X., Rannala, B. & Yang, Z. Species tree inference with BPP using genomic sequences and the multispecies coalescent. Mol. Biol. Evol. 35, 2585–2593 (2018).Article 
CAS 

Google Scholar 
Van Buuren, S. & Groothuis-Oudshoorn, K. Multivariate imputation by chained equations. J. Stat. Softw. 45, 1–67 (2011).Article 

Google Scholar 
Penone, C. et al. Imputation of missing data in life-history trait datasets: Which approach performs the best?. Methods Ecol. Evol. 5, 961–970 (2014).Article 

Google Scholar 
Berner, D. Size correction in biology: How reliable are approaches based on (common) principal component analysis?. Oecologia 166, 961–971 (2011).Article 
ADS 

Google Scholar 
Simmons, N. B. Order Chiroptera. In Mammal Species of the World: A Taxonomic and Geographic Reference (eds Wilson, D. E. & Reeder, D. M.) 312–529 (The John Hopkins University Press, 2005).
Google Scholar 
Wilson, D. E. & Mittermeier, R. A. Handbook of the Mammals of the World. Vol. 9. Bats (Lynx Editions, 2019).
Google Scholar 
R Core Team. R: A language and environment for statistical computing (2022).Kuhn, M. caret: Classification and Regression Training. R package version 6.0-86. https://CRAN.R-project.org/package=caret (2020).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).Book 
MATH 

Google Scholar 
Kuhn, M. & Johnson, K. Applied Predictive Modeling (Springer, 2013).Book 
MATH 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Hijmans, R. J. raster: Geographic Data Analysis and Modeling (2022).Barker, B. S., Rodríguez-Robles, J. A. & Cook, J. A. Climate as a driver of tropical insular diversity: Comparative phylogeography of two ecologically distinctive frogs in Puerto Rico. Ecography 38, 769–781 (2015).Article 

Google Scholar 
Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C. & Guisan, A. Selecting predictors to maximize the transferability of species distribution models: Lessons from cross-continental plant invasions. Glob. Ecol. Biogeogr. 26, 275–287 (2017).Article 

Google Scholar 
Akinwande, M. O., Dikko, H. G. & Samson, A. Variance inflation factor: As a condition for the inclusion of suppressor variable(s) in regression analysis. Open J. Stat. 05, 754–767 (2015).Article 

Google Scholar 
Izenman, A. J. Linear discriminant analysis. in Modern Multivariate Statistical Techniques 237–280 (2013).Lever, J., Krzywinski, M. & Altman, N. Points of significance: Principal component analysis. Nat. Methods 14, 641–642 (2017).Article 
CAS 

Google Scholar 
Guisan, A., Petitpierre, B., Broennimann, O., Daehler, C. & Kueffer, C. Unifying niche shift studies: Insights from biological invasions. Trends Ecol. Evol. 29, 260–269 (2014).Article 

Google Scholar 
Di Cola, V. et al. ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).Article 

Google Scholar 
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).Article 

Google Scholar 
Liu, C., Wolter, C., Xian, W. & Jeschke, J. M. Most invasive species largely conserve their climatic niche. Proc. Natl. Acad. Sci. 117, 23643–23651 (2020).Article 
ADS 
CAS 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).Article 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 33, 607–611 (2010).
Google Scholar  More