More stories

  • in

    Vitality as a measure of animal welfare during purse seine pumping related crowding of Atlantic mackerel (Scomber scrombrus)

    Huntingford, F. A. et al. Current issues in fish welfare. J. Fish Biol. 68, 332–372 (2006).Article 

    Google Scholar 
    Kaiser, M. J. & Huntingford, F. A. Introduction to papers on fish welfare in commercial fisheries. J. Fish Biol. 75, 2852–2854 (2009).Article 
    CAS 

    Google Scholar 
    Veldhuizen, L. J. L., Berentsen, P. B. M., de Boer, I. J. M., van de Vis, J. W. & Bokkers, E. A. M. Fish welfare in capture fisheries: A review of injuries and mortality. Fish. Res. 204, 41–48 (2018).Article 

    Google Scholar 
    Breen, M. et al. Catch welfare in commercial fisheries. In The Welfare of Fish (eds Kristiansen, T. S. et al.) 401–437 (Springer, 2020).Chapter 

    Google Scholar 
    Diggles, B. K., Cooke, S. J., Rose, J. D. & Sawynok, W. Ecology and welfare of aquatic animals in wild capture fisheries. Rev. Fish. Biol. Fish. 21, 739–765 (2011).Article 

    Google Scholar 
    Korte, S. M., Olivier, B. & Koolhaas, J. M. A new animal welfare concept based on allostasis. Physiol. Behav. 92, 422–428 (2007).Article 
    CAS 

    Google Scholar 
    Broom, D. M. The scientific assessment of animal welfare. Appl. Anim. Behav. Sci. 20, 5–19 (1988).Article 

    Google Scholar 
    Broom, D. M. Animal welfare: Concepts and measurement. J. Anim. Sci. 69, 4167–4175 (1991).Article 
    CAS 

    Google Scholar 
    Tveit, G. M., Anders, N., Bondø, M. S., Mathiassen, J. R. & Breen, M. Atlantic mackerel (Scomber scombrus) change skin colour in response to crowding stress. J. Fish Biol. 100, 738–747 (2022).Article 
    CAS 

    Google Scholar 
    Noble, C. et al. Welfare Indicators for Farmed Atlantic Salmon: Tools for Assessing Fish Welfare (Nofima, 2018).
    Google Scholar 
    Sopinka, N. M., Donaldson, M. R., O’Connor, C. M., Suski, C. D. & Cooke, S. J. Stress indicators in fish. In Fish Physiology vol 35 405–462 (Elsevier, 2016).
    Google Scholar 
    Lawrence, M. J. et al. Are 3 minutes good enough for obtaining baseline physiological samples from teleost fish?. Can. J. Zool. 96, 774–786 (2018).Article 
    CAS 

    Google Scholar 
    Lawrence, M. J. et al. Best practices for non-lethal blood sampling of fish via the caudal vasculature. J. Fish Biol. 97, 4–15 (2020).Article 

    Google Scholar 
    Clark, T. D. et al. The efficacy of field techniques for obtaining and storing blood samples from fishes. J. Fish Biol. 79, 1322–1333 (2011).Article 
    CAS 

    Google Scholar 
    Davis, M. W., Olla, B. L. & Schreck, C. B. Stress induced by hooking, net towing, elevated sea water temperature and air in sablefish: Lack of concordance between mortality and physiological measures of stress. J. Fish Biol. 58, 1–15 (2001).Article 

    Google Scholar 
    Rushen, J. Problems associated with the interpretation of physiological data in the assessment of animal welfare. Appl. Anim. Behav. Sci. 28, 381–386 (1991).Article 

    Google Scholar 
    Dawkins, M. Using behaviour to assess animal welfare. Anim. Welf. 13, 3–7 (2004).
    Google Scholar 
    Moberg, G. P. & Mench, J. A. The Biology of Animal Stress: Basic Principles and Implications for Animal Welfare (CABI, 2000).Book 

    Google Scholar 
    Wedemeyer, G. A. Effects of rearing conditions on the health and physiological quality of fish in intensive culture. In Fish Stress and Health in Aquaculture vol 278 (Cambridge University Press, 1997).
    Google Scholar 
    Botreau, R. et al. Aggregation of measures to produce an overall assessment of animal welfare. Part 1: A review of existing methods. Animal 1, 1179–1187 (2007).Article 
    CAS 

    Google Scholar 
    Turnbull, J., Bell, A., Adams, C., Bron, J. & Huntingford, F. Stocking density and welfare of cage farmed Atlantic salmon: Application of a multivariate analysis. Aquaculture 243, 121–132 (2005).Article 

    Google Scholar 
    North, B. P. et al. The impact of stocking density on the welfare of rainbow trout (Oncorhynchus mykiss). Aquaculture 255, 466–479 (2006).Article 

    Google Scholar 
    Spoolder, H., De Rosa, G., Hörning, B., Waiblinger, S. & Wemelsfelder, F. Integrating parameters to assess on-farm welfare. Anim. Welf. 12, 529–534 (2003).CAS 

    Google Scholar 
    Walker, J. K., Dale, A. R., D’Eath, R. B. & Wemelsfelder, F. Qualitative Behaviour Assessment of dogs in the shelter and home environment and relationship with quantitative behaviour assessment and physiological responses. Appl. Anim. Behav. Sci. 184, 97–108 (2016).Article 

    Google Scholar 
    Brscic, M. et al. Welfare assessment: Correlations and integration between a Qualitative Behavioural Assessment and a clinical health protocol applied in veal calves farms. Ital. J. Anim. Sci. 8, 601–603 (2009).Article 

    Google Scholar 
    Andreasen, S. N., Wemelsfelder, F., Sandøe, P. & Forkman, B. The correlation of Qualitative Behavior Assessments with Welfare Quality® protocol outcomes in on-farm welfare assessment of dairy cattle. Appl. Anim. Behav. Sci. 143, 9–17 (2013).Article 

    Google Scholar 
    Phythian, C. J., Michalopoulou, E., Cripps, P. J., Duncan, J. S. & Wemelsfelder, F. On-farm qualitative behaviour assessment in sheep: Repeated measurements across time, and association with physical indicators of flock health and welfare. Appl. Anim. Behav. Sci. 175, 23–31 (2016).Article 

    Google Scholar 
    Davis, M. W., Benoît, H. P., Breen, M., Kopp, D. & Depestele, J. Vitality Assessments. In ICES guidelines for estimating discard survival, ICES Cooperative Research Reports No. 351. 219 (International Council for the Exploration of the Sea, 2021). https://doi.org/10.17895/ices.pub.8006.Stoner, A. W. Assessing stress and predicting mortality in economically significant crustaceans. Rev. Fish. Sci. 20, 111–135 (2012).Article 

    Google Scholar 
    Humborstad, O.-B., Davis, M. W. & Løkkeborg, S. Reflex impairment as a measure of vitality and survival potential of Atlantic cod (Gadus morhua). Fish. Bull. 107, 395–402 (2009).
    Google Scholar 
    Campbell, M. D., Tolan, J., Strauss, R. & Diamond, S. L. Relating angling-dependent fish impairment to immediate release mortality of red snapper (Lutjanus campechanus). Fish. Res. 106, 64–70 (2010).Article 

    Google Scholar 
    Davis, M. W. Fish stress and mortality can be predicted using reflex impairment. Fish Fish. 11, 1–11 (2010).Article 

    Google Scholar 
    Barkley, A. S. & Cadrin, S. X. Discard mortality estimation of yellowtail flounder using reflex action mortality predictors. Trans. Am. Fish. Soc. 141, 638–644 (2012).Article 

    Google Scholar 
    Raby, G. D. et al. Validation of reflex indicators for measuring vitality and predicting the delayed mortality of wild coho salmon bycatch released from fishing gears. J. Appl. Ecol. 49, 90–98 (2012).Article 

    Google Scholar 
    LeDain, M. R. K. et al. Assisted recovery following prolonged submergence in fishing nets can be beneficial to turtles: An assessment with blood physiology and reflex impairment. Chelonian Conserv. Biol. 12, 172–177 (2013).Article 

    Google Scholar 
    Watson, R. A. & Tidd, A. Mapping nearly a century and a half of global marine fishing: 1869–2015. Mar. Policy 93, 171–177 (2018).Article 

    Google Scholar 
    Ben-Yami, M. Purse seining manual. (1994).Marçalo, A. et al. Mitigating slipping-related mortality from purse seine fisheries for small pelagic fish: Case studies from European Atlantic Waters. In The European Landing Obligation 297–318 (Springer, 2019).Chapter 

    Google Scholar 
    Digre, H., Tveit, G. M., Solvang-Garten, T., Eilertsen, A. & Aursand, I. G. Pumping of mackerel (Scomber scombrus) onboard purse seiners, the effect on mortality, catch damage and fillet quality. Fish. Res. 176, 65–75 (2016).Article 

    Google Scholar 
    Tenningen, M., Vold, A. & Olsen, R. E. The response of herring to high crowding densities in purse-seines: Survival and stress reaction. ICES J. Mar. Sci. 69, 1523–1531 (2012).Article 

    Google Scholar 
    Anders, N., Roth, B. & Breen, M. Physiological response and survival of Atlantic mackerel exposed to simulated purse seine crowding and release. Conserv. Physiol. 9, 25 (2021).Article 

    Google Scholar 
    Anders, N. et al. Effects on individual level behaviour in mackerel (Scomber scombrus) of sub-lethal capture related stressors: Crowding and hypoxia. PLoS One 14, e0213709 (2019).Article 
    CAS 

    Google Scholar 
    Marçalo, A. et al. Behavioural responses of sardines Sardina pilchardus to simulated purse-seine capture and slipping. J. Fish Biol. 83, 480–500 (2013).Article 

    Google Scholar 
    Anders, N., Eide, I., Lerfall, J., Roth, B. & Breen, M. Physiological and flesh quality consequences of pre-mortem crowding stress in Atlantic mackerel (Scomber scombrus). PLoS One 15, e0228454 (2020).Article 
    CAS 

    Google Scholar 
    Olsen, R. E., Oppedal, F., Tenningen, M. & Vold, A. Physiological response and mortality caused by scale loss in Atlantic herring. Fish. Res. 129–130, 21–27 (2012).Article 

    Google Scholar 
    Marçalo, A. et al. Fishing simulation experiments for predicting the effects of purse-seine capture on sardine (Sardina pilchardus). ICES J. Mar. Sci. 67, 334–344 (2010).Article 

    Google Scholar 
    Roth, B. & Skåra, T. Pre mortem capturing stress of Atlantic herring (Clupea harengus) in purse seine and subsequent effect on welfare and flesh quality. Fish. Res. 244, 106124 (2021).Article 

    Google Scholar 
    Marçalo, A. et al. Sardine (Sardina pilchardus) stress reactions to purse seine fishing. Mar. Biol. 149, 1509–1518 (2006).Article 

    Google Scholar 
    ICES. Working Group on Widely Distributed Stocks (WGWIDE). 1019 https://doi.org/10.17895/ices.pub.7475 (2020).Lockwood, S. J., Pawson, M. G. & Eaton, D. R. The effects of crowding on mackerel (Scomber scombrus L)— physical condition and mortality. Fish. Res. 2, 129–147 (1983).Article 

    Google Scholar 
    Huse, I. & Vold, A. Mortality of mackerel (Scomber scombrus L.) after pursing and slipping from a purse seine. Fish. Res. 20, 54–59 (2010).Article 

    Google Scholar 
    Sone, I., Skåra, T. & Olsen, S. H. Factors influencing post-mortem quality, safety and storage stability of mackerel species: A review. Eur. Food Res. Technol. 245, 775–791 (2019).Article 
    CAS 

    Google Scholar 
    Handegard, N. O. et al. Effects on schooling function in mackerel of sub-lethal capture related stressors: Crowding and hypoxia. PLoS One 12, e0190259 (2017).Article 

    Google Scholar 
    Percie du Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. J. Cereb. Blood Flow Metab. 40, 1769–1777 (2020).Article 

    Google Scholar 
    Koolhaas, J. M. et al. Stress revisited: A critical evaluation of the stress concept. Neurosci. Biobehav. Rev. 35, 1291–1301 (2011).Article 
    CAS 

    Google Scholar 
    Tenningen, M., Pobitzer, A., Handegard, N. O. & de Jong, K. Estimating purse seine volume during capture: Implications for fish densities and survival of released unwanted catches. ICES J. Mar. Sci. 76, 2481–2488 (2019).Article 

    Google Scholar 
    Fulton, T. W. The Rate of Growth of Fishes. 141–241 (1904).Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).Book 
    MATH 

    Google Scholar 
    Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).Article 

    Google Scholar 
    Dray, S. & Dufour, A.-B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

    Google Scholar 
    Tenningen, M., Peña, H. & Macaulay, G. J. Estimates of net volume available for fish shoals during commercial mackerel (Scomber scombrus) purse seining. Fish. Res. 161, 244–251 (2015).Article 

    Google Scholar 
    Johnston, R., Jones, K. & Manley, D. Confounding and collinearity in regression analysis: A cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour. Qual. Quant. 52, 1957–1976 (2018).Article 

    Google Scholar 
    Burnham, K. & Anderson, D. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

    Google Scholar 
    Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: Challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).Article 
    CAS 

    Google Scholar 
    Hartig, F. & Lohse, L. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. (2022).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 
    Speers-Roesch, B., Mandic, M., Groom, D. J. E. & Richards, J. G. Critical oxygen tensions as predictors of hypoxia tolerance and tissue metabolic responses during hypoxia exposure in fishes. J. Exp. Mar. Biol. Ecol. 449, 239–249 (2013).Article 
    CAS 

    Google Scholar 
    Rogers, N. J., Urbina, M. A., Reardon, E. E., McKenzie, D. J. & Wilson, R. W. A new analysis of hypoxia tolerance in fishes using a database of critical oxygen level (Pcrit). Conserv. Physiol. 4, cow012 (2016).Article 

    Google Scholar 
    Domenici, P., Herbert, N. A., Lefrançois, C., Steffensen, J. F. & McKenzie, D. J. The Effect of Hypoxia on Fish Swimming Performance and Behaviour. In Swimming Physiology of Fish: Towards Using Exercise to Farm a Fit Fish in Sustainable Aquaculture (eds Palstra, A. P. & Planas, J. V.) 129–159 (Springer, 2013).Chapter 

    Google Scholar 
    Johnstone, A. D. F., Wardle, C. S. & Almatar, S. M. Routine respiration rates of Atlantic mackerel, Scomber scombrus L., and herring, Clupea harengus L., at low activity levels. J. Fish Biol. 42, 149–151 (1993).Article 

    Google Scholar 
    Peña, H., Macaulay, G. J., Ona, E., Vatnehol, S. & Holmin, A. J. Estimating individual fish school biomass using digital omnidirectional sonars, applied to mackerel and herring. ICES J. Mar. Sci. 78, 940–951 (2021).Article 

    Google Scholar 
    Kieffer, J. D. Limits to exhaustive exercise in fish. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 126, 161–179 (2000).Article 
    CAS 

    Google Scholar 
    Wardle, C. S. & He, P. Burst swimming speeds of mackerel, Scomber scombrus L. J. Fish Biol. 32, 471–478 (1988).Article 

    Google Scholar 
    Anders, N., Breen, M., Skåra, T., Roth, B. & Sone, I. Effects of capture-related stress and pre-freezing holding in refrigerated sea water (RSW) on the muscle quality and storage stability of Atlantic mackerel (Scomber scombrus) during subsequent frozen storage. Food Chem. https://doi.org/10.1016/j.foodchem.2022.134819 (2022).Article 

    Google Scholar 
    Sogn-Grundvåg, G., Zhang, D. & Iversen, A. Large buyers at a fish auction: The case of the Norwegian pelagic auction. Mar. Policy 104, 232–238 (2019).Article 

    Google Scholar 
    Breen, M. et al. Behaviour & Welfare of Mackerel & Herring During Capture in Purse Seine. 134 https://www.fhf.no/prosjekter/prosjektbasen/901350/ (2021). More

  • in

    Out-of-date datasets hamper conservation of species close to extinction

    Scheffers, B. R., Yong, D. L., Harris, J. B. C., Giam, X. & Sodhi, N. S. The world’s rediscovered species: back from the brink? PloS ONE 6, e22531 (2011).Article 
    CAS 

    Google Scholar 
    Abeli, T., Albani Rocchetti, G., Barina, Z., Bazos, I. & Draper, D. et al. Seventeen ‘extinct’ plant species back to conservation attention in Europe. Nat. Plants 7, 282–286 (2021).Article 

    Google Scholar 
    Guidelines for Using the IUCN Red List Categories and Criteria Version 14 (IUCN Standards and Petitions Committee, 2019); http://www.iucnredlist.org/documents/RedListGuidelines.pdfDalrymple, S. E. & Abeli, T. Ex situ seed banks and the IUCN Red List. Nat. Plants 5, 122–123 (2019).Article 

    Google Scholar 
    Albani Rocchetti, G. et al. Selecting the best candidates for resurrecting extinct-in-the-wild plants from herbaria. Nat. Plants. https://doi.org/10.1038/s41477-022-01296-7 (2022).The IUCN Red List of Threatened Species Version 2022-1 (IUCN, accessed 264 October 2022); https://www.iucnredlist.orgHumphreys, A. M., Govaerts, R., Ficinski, S. Z., Lughadha, E. N. & Vorontsova, M. S. Global dataset shows geography and life form predict modern plant extinction and rediscovery. Nat. Ecol. Evol. 3, 1043–1047 (2019).Article 

    Google Scholar 
    Knapp, W. M., Frances, A., Noss, R., Naczi, R. F. & Weakley, A. et al. Vascular plant extinction in the continental United States and Canada. Conserv. Biol. 35, 360–368 (2021).Article 

    Google Scholar 
    Sasidharan, N. Cynometra beddomei. The IUCN Red List of Threatened Species 2020 (IUCN, accessed 27 October 2021); https://www.iucnredlist.org/species/31184/115932185Cronk, Q. C. B. A new species and hybrid in the St Helena endemic genus Trochetiopsis. Edinb. J. Bot. 52, 205–213 (1995).Article 

    Google Scholar 
    Loizeau, P. A. & Jackson, P. W. World Flora Online mid-term update. Ann. Missouri Bot. Gard. 102, 341–346 (2017).Article 

    Google Scholar 
    Edwards, C., Bassüner, B., Birkinshaw, C., Camara, C. & Lehavana, A. et al. A botanical mystery solved by phylogenetic analysis of botanical garden collections: the rediscovery of the presumed-extinct Dracaena umbraculifera. Oryx 52, 427–436 (2018).Article 

    Google Scholar 
    MosaChristas, K., Karthick, R., Kowsalya, E. & Jaquline, C. R. I. Musa kattuvazhana (Musaceae): rediscovery and additional notes on a critically endangered species from Western Ghats of Tamil Nadu, India. Feddes Repert. 132, 263–268 (2021).Article 

    Google Scholar 
    Van Hoi, Q. U. A. C. H., Doudkin, R. V., Cuong, T. Q., Le Van, S. O. N. & Van Dung, L. U. O. N. G. et al. Rediscovery of Camellia langbianensis (Theaceae) in Vietnam. Phytotaxa 480, 85–90 (2021).Article 

    Google Scholar 
    Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G. & Axton, M. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).Costello, M. J. & Wieczorek, J. Best practice for biodiversity data management and publication. Biol. Conserv. 173, 68–73 (2014).Article 

    Google Scholar 
    Wieczorek, J., Bloom, D., Guralnick, R., Blum, S., Döring, M., Giovanni, R., Robertson, T. & Vieglais, D. Darwin Core: an evolving community-developed biodiversity data standard. PloS ONE 7, e29715 (2012).Article 
    CAS 

    Google Scholar 
    de Lange, P.J. Lepidium obtusatum Fact Sheet (content continuously updated) (New Zealand Plant Conservation Network, accessed 16 December 2021); https://www.nzpcn.org.nz/flora/species/lepidium-obtusatum/Knapp, W.M., Poindexter, D.B. & Weakley, A.S. The true identity of Marshallia grandiflora an extinct species and the description of Marshallia pulchra (Asteraceae Helenieae Marshalliinae). Phytotaxa 447, 1–15 (2020).Article 

    Google Scholar  More

  • in

    Wood structure explained by complex spatial source-sink interactions

    Overall frameworkCells in our model are arranged along independent radial files, with each cell in one of either the proliferation, enlargement-only, wall thickening, or mature zones, depending on the distance of the cell’s centre from the inside edge of the phloem and the time of year. Only cells that contribute to the formation of xylem tracheids are treated explicitly. A daily timestep is used, on which cells in the proliferation and enlargement-only zones can enlarge in the radial direction if these zones are non-dormant, and on which secondary-wall thickening can occur in the wall thickening zone. Cells in the proliferation zone divide periclinally if they reach a threshold radial length. Cell-size control at division is intermediate between a critical size and a critical increment22. Mother cells divide asymmetrically, with the subsequent relative growth rates of the daughters inversely proportional to their relative sizes. Size at division and asymmetry of division are computed with added statistical noise22, and therefore the model is run for an ensemble of independent radial files with perturbed initial conditions.Equations and parametersCell enlargement and divisionCells in the proliferation and enlargement-only zones, when not dormant, enlarge in the radial direction at a rate dependent on temperature and relative sibling birth size. A Boltzmann-Arrhenius approach is used for the temperature dependence30:$$mu={mu }_{0}{e}^{frac{{E}_{a}}{k}left(frac{1}{{T}_{0}}-frac{1}{T}right)}$$
    (1)
    where μ is the relative rate of radial cell growth at temperature T (μm μm−1 day−1), μ0 is μ at temperature T0 (=283.15 K), Ea is the effective activation energy for cell enlargement, k is the Boltzmann constant (i.e. 8.617 x 10−5 eV K−1), and T is temperature (K). μ0 was calibrated to an observed mean radial file length at the end of the elongation period dataset23 (Table 1; see “Observations”), and Ea was calibrated to an observed temperature dependence of annual ring width dataset31 (Table 1; Supplementary Fig. 4; see “Observations”).Table 1 Model parameters calibrated to observationsFull size tableRadial length of an individual cell then increases according to:$${{Delta }}{L}_{r}={L}_{r}({e}^{epsilon mu }-1)$$
    (2)
    where ΔLr is the radial increment of the cell (μm day−1), Lr is the radial length of the cell (μm), and ϵ is the cell’s growth dependence on relative birth size, given by22:$$epsilon=1-{g}_{asym}{alpha }_{b}$$
    (3)
    where gasym is the strength of the dependence of relative growth rate on asymmetric division (Table 2; unitless), and αb is the degree of asymmetry relative to the cell’s sister22 (scalar):$${alpha }_{b}=frac{{L}_{r}{,}_{b}-{L}_{r}{,}_{b}^{sis}}{{L}_{r}{,}_{b}+{L}_{r}{,}_{b}^{sis}}$$
    (4)
    where Lr,b is the radial length of the cell at birth (μm) and ({L}_{r}{,}_{b}^{sis}) is the radial length of its sister at birth (μm), which are calculated stochastically22:$${L}_{r}{,}_{b}={L}_{r}{,}_{d}(0.5-{Z}_{a})$$
    (5)
    $${L}_{r}{,}_{b}^{sis}={L}_{r}{,}_{d}(0.5+{Z}_{a})$$
    (6)
    where Lr,d is the length of the mother cell when it divides (μm) and Za is Gaussian noise with mean zero and standard deviation σa (Table 2; −0.49 ≤Za≤ 0.49 for numerical stability).Table 2 Parameters used in the model that are taken directly from literatureFull size tableLength at division is derived as22:$${L}_{r}{,}_{d}=f{L}_{r}{,}_{b}+{chi }_{b}(2-f+Z)$$
    (7)
    where f is the mode of cell-size regulation (Table 2; unitless), χb is the mean cell birth size (Table 3; μm), and Z is Gaussian noise with mean zero and standard deviation σ (Table 2).Table 3 Parameters used in the model that are calculated from observationsFull size tableThe first cell in each radial file is an initial, which produces phloem mother cells outwards and xylem mother cells inwards. It grows and divides as other cells in the proliferation zone, but on division one of the daughters is stochastically assigned to phloem or xylem, the other remaining as the initial. The probability of the daughter being on the phloem side is fphloem (Table 3).Cell-wall growthBoth primary and secondary cell-wall growth are influenced by temperature, carbohydrate concentration, and lumen volume. A Michaelis-Menten equation is used to relate the rate of wall growth to the concentration of carbohydrates in the cytoplasm:$${{Delta }}M=frac{{{Delta }}{M}_{max}theta }{theta+{K}_{m}}$$
    (8)
    where ΔM is the rate of cell-wall growth (mg cell−1 day−1), ΔMmax is the carbohydrate-saturated rate of wall growth (mg cell−1 day−1), θ is the concentration of carbohydrates in the cell’s cytoplasm (mg ml−1), and Km is the effective Michaelis constant (mg ml−1; Table 1).The maximum rate of cell-wall growth, ΔMmax, is assumed to depend linearly on lumen volume (a proxy for the amount of machinery for wall growth), and on temperature as in Eq. (1):$${{Delta }}{M}_{max}=omega {V}_{l}{e}^{frac{{E}_{aw}}{k}left(frac{1}{{T}_{0}}-frac{1}{T}right)}$$
    (9)
    where ω is the normalised rate of cell-wall mass growth (i.e. the rate at T0; Table 1; mg ml−1 day−1), Vl is the cell lumen volume (ml cell−1), and Eaw is the effective activation energy for wall building (eV; Table 1). ω and Km were calibrated to an observed distribution of carbohydrates23 (see next section). Eaw was calibrated to an observed temperature dependence of maximum density31 (Table 1; see “Observations”).Lumen volume is given by:$${V}_{l}={V}_{c}-{V}_{w}$$
    (10)
    where Vc is total cell volume (ml cell−1) and Vw is total wall volume (ml cell−1). Cells are assumed cuboid and therefore Vc is given by:$${V}_{c}={L}_{a}{L}_{t}{L}_{r}/1{0}^{12}$$
    (11)
    where La is axial length (μm; Table 2) and Lt is tangential length (μm; Table 3). Vw is given by:$${V}_{w}=M/rho$$
    (12)
    where M is wall mass (mg cell−1) and ρ is wall-mass density (Table 2; mg[DM] ml−1).Cells in the proliferation and enlargement-only zones only have primary cell walls. ΔMmax (Eq. (9)) is therefore given the following limit:$${{Delta }}{M}_{max}=min ({{Delta }}{M}_{max},rho {V}_{{w}_{p}}-M)$$
    (13)
    where ({V}_{{w}_{p}}) is the required primary wall volume:$${V}_{{w}_{p}}={V}_{c}-({L}_{a}-2{W}_{p})({L}_{t}-2{W}_{p})({L}_{r}-2{W}_{p})/1{0}^{12}$$
    (14)
    where Wp is primary cell-wall thickness (Table 3; μm).Carbohydrate distributionThe distribution of carbohydrates across each radial file is calculated independently from the balance of diffusion from the phloem and the uptake into primary and secondary cell walls. The carbohydrate concentration in the phloem is prescribed at the mean value observed across the three observational dates in23, as described below in “Observations”, and the resulting concentration in the cytoplasm of the furthest living cell from the phloem is solved numerically. The inside wall of this cell is assumed to be impermeable to carbohydrates and therefore provides the inner boundary to the solution. It is assumed that the rate of diffusion across each file is rapid relative to the rate of cell-wall building, and therefore concentrations are assumed to be in equilibrium on each day. Carbohydrate diffusion between living cells is assumed to be proportional to the concentration gradient:$${q}_{i}=({theta }_{i-1}-{theta }_{i})/eta$$
    (15)
    where qi is the rate of carbohydrate diffusion from cell i − 1 to cell i (mg day−1) and η is the resistance to flow between cells (calibrated to the observed distribution of carbohydrates23, see next section; Table 1; day ml−1).As it is assumed that carbohydrates cannot diffuse between radial files, at equilibrium the flux into a given cell must equal the rate of wall growth in that cell plus the wall growth in all cells further along the radial file away from the phloem. From this it can be shown that the equilibrium carbohydrate concentration in the furthest living cell from the phloem in each radial file is given by:$${theta }_{n}={theta }_{p}-eta mathop{sum }limits_{i=1}^{n}mathop{sum }limits_{j=i}^{n}{{Delta }}{M}_{j}$$
    (16)
    where θp is the concentration of carbohydrates in the phloem (Table 3; mg ml−1) and n is the number of living cells in the file (phloem mother cells are ignored for simplicity). The rate of wall growth in each cell depends on the concentration of carbohydrates (Eq. (8)), and therefore θn must be found that results in an equilibrium flow across the radial file. This is done using Brent’s method41 as implemented in the “ZBRENT” function42.Zone widthsThe widths of the proliferation, enlargement-only, and secondary wall thickening zones vary through the year, and are fitted to observations on three dates23 (see Supplementary Fig. 2 and “Observations”). Linear responses to daylength were found, which are therefore used to determine widths for the observational period and later days:$${z}_{k}={a}_{k}+{b}_{k}{{{{{{{rm{dl}}}}}}}};{{mathrm{DOY}}}ge 185$$
    (17)
    where zk is the distance of the inner edge of the zone from the inner edge of the phloem (μm), k is proliferation (p), secondary wall thickening (t), or enlargement-only (e), ak and bk are constants (Table 3), dl is daylength (s), and DOY is day-of-year. The proliferation zone width on earlier days when non-dormant was fixed at its DOY 185 width (assuming this to be its maximum, and that it would reach its maximum very soon after cambial dormancy is broken in the spring). During dormancy, the proliferation zone width is fixed at its value on DOY 231 (the first day of dormancy23). The enlargement-only zone width prior to DOY 185, the first observational day, is assumed to be a linear extension of the rate of change after DOY 185. The wall-thickening zone width plays little role prior to DOY 185 at the focal site, and so was set to its Eq. (17) value each earlier day. On all days the condition zt≥ze≥zp is imposed, and zone widths cannot exceed their values at 24 h daylength (necessary for sites north of the Arctic circle). Supplementary Figure 2 shows the resulting progression of zone widths through the year, together with the observed values.DormancyProliferation was observed to be finished by DOY 23123, and so the proliferation and enlargement-only zones are assumed to enter dormancy then. Release from dormancy in the spring is calculated using an empirical thermal time/chilling model33. It was necessary to adjust the asymptote and temperature threshold of the published model because the heat sum on the day of release calculated from observations in Sweden (see “Observations”) was much lower than reported for Sitka spruce buds in Britain in the original work:$${{{{{{{{rm{dd}}}}}}}}}_{req}=15+4401.8{e}^{-0.042{{{{{{{rm{cd}}}}}}}}}$$
    (18)
    where ddreq is the required sum of degree-days (°C) from DOY 32 for dormancy release and cd is the chill-day sum from DOY 306. The degree-day sum is the sum of daily mean temperatures above 0 °C, and the chill-day sum is the number of days with mean temperatures below 0 °C. Dormancy can only be released during the first half of the year.Simulation protocolsEach simulation consisted of an ensemble of 100 independent radial files. Each radial file was initialised by producing a file of 100 cells with radial lengths χb(1+Za), allowing these to divide once, ignoring the second daughter from each division, and then limiting the remaining daughters to only those falling inside the proliferation zone on DOY 1. Values for ϵ (the relative growth of daughter cells) and Lr,d (the radial length at division) were derived for each cell. The main simulations were conducted at the observation site in boreal Sweden (64.35°N, 19.77°E) over 1951–1995 to capture the growth period of the observed trees23. Results are mostly presented for 1995 when the observations were made. Simulations for calibration of the effective activation energies (i.e. Ea and Eaw) were performed at 68.26°N, 19.63°E in Arctic Sweden over 1901–200431. Daily mean temperatures for both sites were derived from the appropriate gridbox in a 6 h 1/2 degree global-gridded dataset43.ObservationsObservations of cellular characteristics and carbohydrate concentrations23 were used to derive a number of model parameters, and to test model output (model calibration and testing were performed using different outputs). According to the published study we used, samples were cut from three 44 yr old Scots pine trees growing in Sweden (64°21’ N; 19°46’ E) at different times during the growing season. 30 μm thick longitudinal tangential sections of the cambial region were made, and the radial distributions of soluble carbohydrates measured using microanalytical techniques23. Cell sizes, wall thicknesses, and positions in their Fig. 123, an image of transverse sections on three sampling dates, were digitised using “WebPlotDigitizer”44. These, together with the numbers of cells in each zone and their sizes given in the text of that paper, were used to estimate zone widths, which were then regressed against daylength to give the parameters for Eq. (17) (Table 3), mean cell size in the proliferation zone on the first sampling date (used to derive χb; Table 3), mean cell tangential length (Table 3), and final ring width (used to calibrate μ0; Table 1). The thickness of the primary cell wall (Table 3) was derived by plotting cell-wall thickness against time and taking the low asymptote.The distributions of carbohydrates along the radial files on the last sampling date for “Tree 1” and “Tree 3” (results for “Tree 2” were not shown for this date) shown in Fig. 2 of the observational paper23 were calculated. The masses for each of sucrose, glucose, and fructose in each 30 μm section were digitised using the same method as for cell properties and then summed and converted to concentrations, with the results shown in Supplementary Figure 5. Mean observed carbohydrate concentrations and cell masses at four points were used to calibrate values for the η, ω, and Km parameters in Table 1. Calibration was performed by minimising the summed relative error across the observations.The calibration target for the effective activation energy for wall deposition (i.e. Eaw) was the observed relationship between maximum density and mean June-July-August temperature over 1901-2004 in northern Sweden31 (Supplementary Fig. 3), and for the effective activation energy for cell enlargement the relationship between ring width and temperature (i.e. Ea) target was the same study (Supplementary Fig. 4). These observations were made on living and subfossil Scots pine sample material from the Lake Tornesträsk area (68.21–68.31°N; 19.45–19.80°E; 350–450 m a.s.l.) using X-ray densitometry for maximum density, and standardised to remove non-climatic information31.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

  • in

    Nations forge historic deal to save species: what’s in it and what’s missing

    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

  • in

    Water motion and pH jointly impact the availability of dissolved inorganic carbon to macroalgae

    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

  • in

    Neolithic dental calculi provide evidence for environmental proxies and consumption of wild edible fruits and herbs in central Apennines

    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

  • in

    Climate warming has compounded plant responses to habitat conversion in northern Europe

    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

  • in

    Environmentally driven phenotypic convergence and niche conservatism accompany speciation in hoary bats

    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