More stories

  • in

    Land use and land cover changes influence the land surface temperature and vegetation in Penang Island, Peninsular Malaysia

    Study areaPenang is situated in the northern part of Peninsular Malaysia and lies within the latitudes 5°12’N to 5°30′ N and longitudes 100°09’E to 100°26’E (Fig. 7). Penang with a land area of 295 Km2, has an estimated population of 720,000 and is regarded as the most populated island in Malaysia. Penang shares the same border on the north and east with Kedah State and the south with Perak State. There are two main parts of Penang State: Penang Island and the mainland which is also regarded as Seberang Perai. These two parts of the State are connected by the two bridges. The eastern part of Penang Island is the most urbanized area comprising industries, commercial centres and residential buildings. However, the western part is less developed comprising mainly hilly terrain and forests22. This study is focused on the Island part of Penang. This island is endowed with a yearly equatorial climate (hot and humid). It has a mean annual temperature ranging between 27 and 30 °C while the mean annual relative humidity ranges between 70 and 90%. Also, the mean annual rainfall is about 267–624 cm.Figure 7The map of Penang State showing the Penang Island (created by the authors using ArcMap 10.8 software).Full size imageData acquisitionThe flow chart of the methodology is presented in Fig. 8. Landsat satellite images were used for the assessment of changes in land use covering a period of 2010–2021 (11 years).Figure 8The flow chart of the methodology.Full size imageThese images were gotten from the website of the United State Geological Survey (https://earthexplorer.usgs.gov). The Landsat images include the Landsat 5 TM (thematic mapper) and Landsat 8 OLI / TIRS (operational land imager / thermal infrared sensor). These were downloaded from the Landsat level 1 dataset (Table 6) with additional criteria which reduced the.Table 6 The characteristics of the satellite data used.Full size tableDetermination of LST and NDVI for Landsat 5 and 8Band 6 of Landsat 5 and band 10 of Landsat 8 were used for the determination of the land surface temperature (LST). The LST and normalized difference vegetation index were determined using the following steps:Conversion of top of atmosphere (TOA) radianceUsing the radiance rescaling factor, thermal infra-red digital numbers were converted to TOA spectral radiance using the equation below29: (frac{Red – NIR}{{Red + NIR}}) (frac{Red – NIR}{{Red + NIR}}) For Landsat 8,$$ {text{L}}lambda = left( {{text{ML}} times {text{ Qcal}}} right) + left( {{text{AL}} – {text{Oi}}} right) $$
    (1)
    For Landsat 5,$$ {text{L}}lambda = left( {{ }frac{{{text{LMax}}lambda – {text{LMin}}lambda }}{{{text{QcalMax}} – {text{QcalMin}}}}} right) times left( {left( {{text{Qcal }} – {text{QcalMin}}} right) + {text{LMin}}lambda } right) $$
    (2)
    where Lλ is TOA spectral radiance, ML is radiance multiplicative band Number, AL is radiance add band number, Qcal is quantized and calibrated standard product pixel values (DN for band 6 or band 10), Oi is the correction value for the respective bands, LMaxλ is spectral radiance scaled to QcalMax, LMinλ is spectral radiance scaled to QcalMin, QcalMax is maximum quantized calibrated pixel value, and QcalMin is minimum quantized calibrated pixel value.Conversion to TOA brightness temperature (BT)Spectral radiance data were converted to TOA brightness temperature using the thermal constant values in the Metadata file29.Kelvin (K) to Celcius (°C) degrees$$ BT = {raise0.5exhbox{$scriptstyle {K2}$} kern-0.1em/kern-0.15em lower0.25exhbox{$scriptstyle {ln left( {frac{K1}{{{text{L}}lambda { } + { }1}}} right)}$}} – 273.15 $$
    (3)
    where BT is the Top of atmosphere brightness temperature (°C), Lλ is TOA spectral radiance (W.m−2 .sr−1 .µm−1)), K1 is the K1 constant band number, and K2 is the K2 constant band number. For Landsat 5, K1 is 607.76, and K2 is 1260.56.Normalized difference vegetation index (NDVI)The Normalized Difference Vegetation Index (NDVI) is a standardized vegetation index which reveals the intensity of greenness and surface radiant temperature of the area30,31. The index value of NDVI usually ranges from − 1 to 1. The higher NDVI value indicates that the vegetation of the area is denser and healthier. This shows that the NDVI values of normal healthy vegetation range from 0.1– 0.75, while it is almost zero for rock and soil, and negative value for water bodies24. The NDVI is calculated using the followings:$$ {text{NDVI }} = frac{{left( {{text{NIR }}{-}{text{ RED}}} right){ }}}{{left( {{text{NIR }} + {text{ RED}}} right)}} $$
    (4)
    In Landsat 4–7$$ {text{NDVI }} = , left( {{text{Band 4 }}{-}{text{ Band 3}}} right) , / , left( {{text{Band 4 }} + {text{ Band 3}}} right) $$In Landsat 8$$ {text{NDVI }} = , left( {{text{Band 5 }}{-}{text{ Band 4}}} right) , / , left( {{text{Band 5 }} + {text{ Band 4}}} right) $$where: RED = DN values from the RED band, and NIR = DN values from the Near Infra-red band.Land Surface Emissivity (LSE)Land Surface Emissivity is the average emissivity of an element on the surface of the earth calculated from NDVI values.$$ {text{PV }} = left{ {frac{{left( {{text{NDVI }} – {text{ NDVImin}}} right)}}{{left( {{text{NDVImax }} – {text{ NDVImin}}} right)}}} right}^{2} $$
    (5)
    where PV is the Proportion of vegetation, NDVI is the DN value from the NDVI image, NDVImin is the minimum DN value from the NDVI image, and NDVImax is the maximum DN value from the NDVI image.$$ {text{E }} = left( {0.004{ } times {text{PV}}} right) + 0.986 $$
    (6)
    where E is land surface emissivity, PV is the Proportion of vegetation, 0.986 corresponds to a correction value of the equation.Land Surface Temperature (LST)Land Surface Temperature (LST) is the radiative temperature which is calculated using top of atmosphere brightness temperature, the wavelength of emitted radiance and land surface emissivity.$$ {text{LST}} = {raise0.5exhbox{$scriptstyle {BT}$} kern-0.1em/kern-0.15em lower0.25exhbox{$scriptstyle {left( {1 + left( {{lambda } times { }frac{{{text{BT}}}}{{{text{c}}2}}} right) times {text{ln}}left( {text{E}} right)} right)}$}} $$
    (7)
    Here c2 is 14388. The value of λ for Landsat 5 (Band 6) is 11.5 µm and Landsat 8 (Band 10) is 10.8 µm.Where BT is the top of atmosphere brightness temperature, λ is the wavelength of emitted radiance, and E is land surface emissivity.c2 = h*c/s (1.4388*10–2 mK = 14388 mK), h is Planck’s constant (6.626*1034 Js), s is Boltzmann constant (1.38*1023 JK), c is velocity of light (2.998*108 m/s).Determination of land use and land cover (LULC) of the study areaThe Landsat images were pre-screened and subjected to clipping and classification32. The boundary shape file of Penang was used to clip out the area of study.Image classificationThe unsupervised method involving a random assignment of sample training points and supervised methods of satellite image classification was employed in this study for determining the LULC types. This mixture of image classification methods has been reported as vital in achieving a high accuracy level33. Bands 5, 4 and 3 were used to classify Landsat 8 while bands 4, 3 and 2 were used for classifying Landsat 5. We used the extraction by mask in the spatial analyst tool of ArcMap 10.2.1 software to extract the study area from the selected bands of the Landsat satellite images. A widely used supervised image classification method was adopted for classifying the Landsat bands in this study32,34. The principle of operation of this method involves the identification of known sample training points which are then used to classify other unknown points with related spectral signatures35. The three monochromatic satellite bands were combined to produce the false colour composite (FCC) using the data management tool36. This involves drawing polygons on the LULC type to select the training points. The LULC types adopted for this study include urbanized areas, agricultural land, rocks, forests, bare surfaces, and water bodies. These were modified LULC types from IPOC Good Practice Guidance37. To achieve this, a minimum of 40 sample points were selected randomly for each category of LULC type36. Having prior knowledge of the study area assisted in the selection of the training points38.The multivariate maximum likelihood classification (MLC) technique was used for transforming the images. Other image transformation techniques have been used by researchers. These include the fuzzy set classifier, neural networks (NN) classifier, extraction and classification of homogenous objects (ECHO) classifier, per-field classifier, sub-pixel classifier, decision trees (DTs), support vector machines (SVMs), minimum distance classifier (MDC) and so-on39. The adoption of any of these techniques is dependent on the knowledge of the area of study, band selection, accessibility of data, the complexity of the landscape, the classification algorithm, and the proficiency of the analyst39. We preferred MLC to other techniques in this study due to its reported high level of accuracy in tropical regions32,34. Another reason for choosing MLC is that it is readily incorporated in many widely used GIS software packages. This MLC algorithm operates based on assigning pixels to the highest probability class and establishing the class ownership of such pixels. It is also regarded as a parametric classifier whose data follows almost a normal distribution39. We ensured the accuracy of this classifier by assigning a large number of training sample points using our prior knowledge of the study area.Description of the LULC categoriesThe urbanized area is the developed part of the study area. This includes houses, roads, railways, and industries. This is also known to be a settlement in other literature40. Agricultural land is the part of the study area dominated by agricultural activities and herbaceous plants and grasses. Agricultural land is generally a product of deforestation36. Rocks are part of the study area comprising solid mineral materials (rocks). Bare land is the bare soil which is either made open by natural or human activities.Forests are parts of the study area dominated by trees. They can be primary or secondary forests depending on the rate of disturbances. According to41, forest land is an area having more than 0.5 ha of flora comprising trees (height is above 5 m) with a canopy greater than 10%. The forests in Penang are generally both primary and secondary42. Water bodies are parts of the study area covered by water seasonally or permanently. These include seas, rivers, lakes, ponds, streams, or reservoirs40.Determination of change in the LULCThe rate and extent of change in the LULC of Penang within the periods under consideration were determined following the formula below43:$$ {text{Changed area }}left( {{text{C}}_{{text{a}}} } right) , = {text{ T}}_{{text{a}}} left( {text{year 2}} right) , {-}{text{ T}}_{{text{a}}} left( {text{year 1}} right) $$
    (8)
    $$ {text{Changed extent }}left( {{text{C}}_{{text{e}}} } right) , = {text{ C}}_{{text{a}}} /{text{ T}}_{{text{a}}} left( {text{year 1}} right) $$
    (9)
    $$ {text{Percentage of change }} = {text{ C}}_{{text{e}}} {text{x 1}}00 $$
    (10)
    where Ta means the total area.Determination of relationship between LST and NDVIThe values of LST and NDVI at 20 random points of each LULC class were used. The relationship between the LST and NDVI across all the LULC classes in each year was determined using the bivariate linear regression analysis. This was done in Paleontological Statistical (PAST) package 3.0.Classification accuracy assessmentThe classification accuracy was assessed by taking ground truth coordinate data of the LULC of the study area using a geographical positioning system (GPS) device (Garmin Etrex 10). These data were compared with the LULC classified in this study32. Consequently, an error matrix was generated. This normally uses ground truth data to explain the accuracy of the classified LULC. The error matrix comprises the user’s accuracy, the producer’s accuracy, overall accuracy and the Kappa index32.The producer’s accuracy (omission error) represents the probability of the correctly classified reference pixel and it is determined using this formula below:$${text{Producer’s accuracy }}left( % right) , = { 1}00% , – {text{ error of omission}} $$
    (11)
    Also, the user’s accuracy (commission error) represents the probability that the classified pixel matches the one on the ground36 and it is determined using the formula below:$$ {text{User’s accuracy }}left( % right) , = { 1}00% , – {text{ error of commission}} $$
    (12)
    The statistical accuracy of the matrix was determined using the Kappa coefficient44. This Kappa coefficient ranges from − 1 to + 145. Therefore, the overall accuracy of the classification was determined by dividing the total number of correctly classified pixels by the total number of sampled ground truth data40. More

  • in

    Recent speciation associated with range expansion and a shift to self-fertilization in North American Arabidopsis

    Coyne, J. A. & Orr, H. A. Speciation 83–178 (Sinauer, 2004).Dieckmann, U., Doebeli, M., Metz, J. A. & Tautz, D. Adaptive Speciation (Cambridge University Press, 2004).Butlin, R. K., Galindo, J. & Grahame, J. W. Sympatric, parapatric or allopatric: the most important way to classify speciation? Philos. T. Roy. Soc. B 363, 2997–3007 (2008).Article 

    Google Scholar 
    Smadja, C. M. & Butlin, R. K. A framework for comparing processes of speciation in the presence of gene flow. Mol. Ecol. 20, 5123–5140 (2011).Article 

    Google Scholar 
    Seehausen, O. et al. Genomics and the origin of species. Nat. Rev. Genet. 15, 176–192 (2014).Article 
    CAS 

    Google Scholar 
    Kulmuni, J., Butlin, R. K., Lucek, K., Savolainen, V. & Westram, A. M. Towards the completion of speciation: the evolution of reproductive isolation beyond the first barriers. Philos. T. Roy. Soc. B 375, 20190528 (2020).Article 

    Google Scholar 
    Hofreiter, M. & Stewart, J. Ecological change, range fluctuations and population dynamics during the Pleistocene. Curr. Biol. 19, R584–R594 (2009).Article 
    CAS 

    Google Scholar 
    Longman, J., Mills, B. J. W., Manners, H. R., Gernon, T. M. & Palmer, M. R. Late Ordovician climate change and extinctions driven by elevated volcanic nutrient supply. Nat. Geosci. 14, 924–929 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Thomson, R. C., Spink, P. Q. & Shaffer, H. B. A global phylogeny of turtles reveals a burst of climate-associated diversification on continental margins. Proc. Natl Acad. Sci. USA 118, e2012215118 (2021).Article 
    CAS 

    Google Scholar 
    Chaboureau, A. C., Sepulchre, P., Donnadieu, Y. & Franc, A. Tectonic-driven climate change and the diversification of angiosperms. Proc. Natl Acad. Sci. USA 111, 14066–14070 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).Article 
    ADS 
    CAS 

    Google Scholar 
    Schmitt, T. Molecular biogeography of Europe: Pleistocene cycles and postglacial trends. Front. Zool. 4, 11 (2007).Article 

    Google Scholar 
    Haffer, J. Speciation in Amazonian forest birds. Science 165, 131–137 (1969).Article 
    ADS 
    CAS 

    Google Scholar 
    Ebdon, S. et al. The Pleistocene species pump past its prime: evidence from European butterfly sister species. Mol. Ecol. 30, 3575–3589 (2021).Article 

    Google Scholar 
    Excoffier, L., Foll, M. & Petit, R. J. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).Article 

    Google Scholar 
    Baker, H. G. Self-compatibility and establishment after ‘long-distance’ dispersal. Evolution 9, 347–349 (1955).
    Google Scholar 
    Fisher, R. The Genetical Theory of Natural Selection 125–129 (Oxford University Press, 1930).Endler, J. A. Geographic Variation, Speciation, and Clines. Monographs in Population Biology Vol. 10, 53–65, 142–150 (Princeton University Press, 1977).Doebeli, M. & Dieckmann, U. Speciation along environmental gradients. Nature 421, 259–264 (2003).Article 
    ADS 
    CAS 

    Google Scholar 
    Ispolatov, J. & Doebeli, M. Diversification along environmental gradients in spatially structured populations. Evol. Ecol. Res. 11, 295–304 (2009).
    Google Scholar 
    Rettelbach, A., Servedio, M. R. & Hermisson, J. Speciation in peripheral populations: effects of drift load and mating systems. J. Evol. Biol. 29, 1073–1090 (2016).Article 
    CAS 

    Google Scholar 
    Wright, S. I., Kalisz, S. & Slotte, T. Evolutionary consequences of self-fertilization in plants. Proc. R. Soc. Lond. Ser. B 280, 20130133 (2013).
    Google Scholar 
    Hu, X.-S. Mating system as a barrier to gene flow. Evolution 69, 1158–1177 (2015).Article 
    CAS 

    Google Scholar 
    Glémin, S. How are deleterious mutations purged? Drift versus nonrandom mating. Evolution 57, 2678–2687 (2003).
    Google Scholar 
    Warwick, S. I., Francis, A. & Al-Shehbaz, I. A. Brassicaceae: species checklist and database on CD-Rom. Plant Syst. Evol. 259, 249–258 (2006).Article 

    Google Scholar 
    Warwick, S. I., Al-Shehbaz, I. A. & Sauder, C. A. Phylogenetic position of Arabis arenicola and generic limits of Aphragmus and Eutrema (Brassicaceae) based on sequences of nuclear ribosomal DNA. Can. J. Bot. 84, 269–281 (2006).Article 
    CAS 

    Google Scholar 
    Hohmann, N. et al. Taming the wild: resolving the gene pools of non-model Arabidopsis lineages. BMC Evol. Biol. 14, e224 (2014).Article 

    Google Scholar 
    Novikova, P. Y. et al. Sequencing of the genus Arabidopsis identifies a complex history of nonbifurcating speciation and abundant trans-specific polymorphism. Nat. Genet. 48, 1077–1082 (2016).Article 
    CAS 

    Google Scholar 
    Perrier, A. & Willi, Y. Intraspecific variation in reproductive barriers between two recently-diverged, allopatric Arabidopsis species. J. Evol. Biol. https://doi.org/10.1111/jeb.14122 (2022). (in press).Griffin, P. C. & Willi, Y. Evolutionary shifts to self-fertilisation restricted to geographic range margins in North American Arabidopsis lyrata. Ecol. Lett. 17, 484–490 (2014).Article 
    CAS 

    Google Scholar 
    Willi, Y., Fracassetti, M., Zoller, S. & Van Buskirk, J. Accumulation of mutational load at the edges of a species range. Mol. Biol. Evol. 35, 781–791 (2018).Article 
    CAS 

    Google Scholar 
    Schmickl, R., Jørgensen, M. H., Brysting, A. K. & Koch, M. A. The evolutionary history of the Arabidopsis lyrata complex: a hybrid in the Amphi-Beringian area closes a large distribution gap and builds up a genetic barrier. BMC Evol. Biol. 10, e98 (2010).Article 

    Google Scholar 
    Pyhäjärvi, T., Aalto, E. & Savolainen, O. Time scales of divergence and speciation among natural populations and subspecies of Arabidopsis lyrata (Brassicaceae). Am. J. Bot. 99, 1314–1322 (2012).Article 

    Google Scholar 
    Dyke, A. S. in Quaternary Glaciations – Extent and Chronology, Part II: North America (Elsevier, Amsterdam, 2004).Kirkpatrick, M. & Ravigné, V. Speciation by natural and sexual selection: models and experiments. Am. Nat. 159, S22–S35 (2002).Article 

    Google Scholar 
    Igic, B., Lande, R. & Kohn, J. R. Loss of self‐incompatibility and its evolutionary consequences. Int. J. Plant Sci. 169, 93–104 (2008).Article 

    Google Scholar 
    Willi, Y. & Määttänen, K. Evolutionary dynamics of mating system shifts in Arabidopsis lyrata. J. Evol. Biol. 23, 2123–2131 (2010).Article 
    CAS 

    Google Scholar 
    Lucek, K. & Willi, Y. Drivers of linkage disequilibrium across a species’ geographic range. PLoS Genet. 17, e1009477 (2021).Article 
    CAS 

    Google Scholar 
    Pironon, S. et al. Geographic variation in genetic and demographic performance: new insights from an old biogeographical paradigm: the centre-periphery hypothesis. Biol. Rev. 92, 1877–1909 (2017).Article 

    Google Scholar 
    Encinas-Viso, F., Young, A. G. & Pannell, J. R. The loss of self-incompatibility in a range expansion. J. Evol. Biol. 33, 1235–1244 (2020).Article 

    Google Scholar 
    Jarne, P. & Auld, J. R. Animals mix it up too: the distribution of self-fertilization among hermaphroditic animals. Evolution 60, 1816–1824 (2006).
    Google Scholar 
    Foxe, J. P. et al. Reconstructing origins of loss of self-incompatibility and selfing in North American Arabidopsis lyrata: a population genetic context. Evolution 64, 3495–3510 (2010).Article 

    Google Scholar 
    Koski, M. H., Layman, N. C., Prior, C. J., Busch, J. W. & Galloway, L. F. Selfing ability and drift load evolve with range expansion. Evol. Lett. 3, 500–512 (2019).Article 

    Google Scholar 
    Prior, C. J. & Busch, J. W. Selfing rate variation within species is unrelated to life‐history traits or geographic range position. Am. J. Bot. 108, 2294–2308 (2021).Article 

    Google Scholar 
    Skeels, A. & Cardillo, M. Reconstructing the geography of speciation from contemporary biodiversity data. Am. Nat. 193, 240–254 (2019).Article 

    Google Scholar 
    Sánchez-Castro, D., Perrier, A. & Willi, Y. Reduced climate adaptation at range edges in North American Arabidopsis lyrata. Glob. Ecol. Biogeogr. 31, 1066–1077 (2022).Article 

    Google Scholar 
    Roessler, K. et al. The genome-wide dynamics of purging during selfing in maize. Nat. Plants 5, 980–990 (2019).Article 
    CAS 

    Google Scholar 
    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).Hu, T. T. et al. The Arabidopsis lyrata genome sequence and the basis of rapid genome size change. Nat. Genet. 43, 476–481 (2011).Article 

    Google Scholar 
    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).Article 

    Google Scholar 
    McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).Article 
    CAS 

    Google Scholar 
    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).Article 
    CAS 

    Google Scholar 
    Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).Article 
    CAS 

    Google Scholar 
    Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).Article 
    CAS 

    Google Scholar 
    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).Article 
    CAS 

    Google Scholar 
    Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).Article 
    CAS 

    Google Scholar 
    Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).Article 

    Google Scholar 
    Marchi, N. et al. The genomic origins of the world’s first farmers. Cell 185, 1842–1859 (2022).Article 
    CAS 

    Google Scholar 
    Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).Article 
    CAS 

    Google Scholar 
    Genete, M., Castric, V. & Vekemans, X. Genotyping and de novo discovery of allelic variants at the Brassicaceae self-incompatibility locus from short-read sequencing data. Mol. Biol. Evol. 7, 1193–1201 (2020).Article 

    Google Scholar 
    Lynch, M. et al. Genome-wide linkage-disequilibrium profiles from single individuals. Genetics 198, 269–281 (2014).Article 

    Google Scholar 
    R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2021).Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).Article 
    CAS 

    Google Scholar 
    Nychka, D., Furrer, R., Paige, J. & Sain, S. fields: tools for spatial data. R package version 14.1 https://github.com/dnychka/fieldsRPackage (2021).Asquith, W. lmomco—L-moments, censored L-moments, trimmed L-moments, L-comoments, and many distributions. R package version 2.4.7 (2022).Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

    Google Scholar 
    Lemon, J. Plotrix: a package in the red light district of R. R. N. 6, 8–12 (2006).
    Google Scholar 
    Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R. N. 5, 9–13 (2005).
    Google Scholar 
    Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R Second edition (Springer, 2013). More

  • in

    Differences in fish herbivory among tropical and temperate seaweeds and annual patterns in kelp consumption influence the tropicalisation of temperate reefs

    Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4(8), 1044–1059 (2020).Article 

    Google Scholar 
    Hobbs, R. J., Valentine, L. E., Standish, R. J. & Jackson, S. T. Movers and stayers: Novel assemblages in changing environments. Trends Ecol. Evol. 33, 116–128 (2017).Article 

    Google Scholar 
    Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).Article 

    Google Scholar 
    Ockendon, N. et al. Mechanisms underpinning climatic impacts on natural populations: Altered species interactions are more important than direct effects. Glob. Change Biol. 20, 2221–2229 (2014).Article 
    ADS 

    Google Scholar 
    Gómez-Aparicio, L., García-Valdés, R., Ruíz-Benito, P. & Zavala, M. A. Disentangling the relative importance of climate, size and competition on tree growth in Iberian forests: Implications for forest management under global change. Glob. Change Biol. 17, 2400–2414 (2011).Article 
    ADS 

    Google Scholar 
    Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science https://doi.org/10.1126/science.aai9214 (2017).Article 

    Google Scholar 
    Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671. https://doi.org/10.1126/science.aaf7671 (2016).Article 
    CAS 

    Google Scholar 
    Vergés, A. et al. The tropicalization of temperate marine ecosystems: Climate-mediated changes in herbivory and community phase shifts. Proc. R. Soc. B-Biol. Sci. 281, 20140846. https://doi.org/10.1098/rspb.2014.0846 (2014).Article 

    Google Scholar 
    Poore, A. G. B. et al. Global patterns in the impact of marine herbivores on benthic primary producers. Ecol. Lett. 15, 912–922. https://doi.org/10.1111/j.1461-0248.2012.01804.x (2012).Article 

    Google Scholar 
    Bennett, S., Wernberg, T., Harvey, E. S., Santana-Garcon, J. & Saunders, B. J. Tropical herbivores provide resilience to a climate-mediated phase shift on temperate reefs. Ecol. Lett. 18, 714–723 (2015).Article 

    Google Scholar 
    Vergés, A. et al. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory and loss of kelp. Proc. Natl. Acad. Sci. 113(48), 13791–13796 (2016).Article 
    ADS 

    Google Scholar 
    Vergés, A. et al. Tropical rabbitfish and the deforestation of a warming temperate sea. J. Ecol. 102, 1518–1527. https://doi.org/10.1111/1365-2745.12324 (2014).Article 

    Google Scholar 
    Kumagai, N. H. et al. Ocean currents and herbivory drive macroalgae-to-coral community shift under climate warming. Proc. Natl. Acad. Sci. 115, 8990–8995 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Demko, A. M. et al. Declines in plant palatability from polar to tropical latitudes depend on herbivore and plant identity. Ecology 98, 2312–2321. https://doi.org/10.1002/ecy.1918 (2017).Article 

    Google Scholar 
    Floeter, S. R., Behrens, M. D., Ferreira, C. E. L., Paddack, M. J. & Horn, M. H. Geographical gradients of marine herbivorous fishes: Patterns and processes. Mar Biol 147, 1435–1447 (2005).Article 

    Google Scholar 
    Longo, G. O., Hay, M. E., Ferreira, C. E. L. & Floeter, S. R. Trophic interactions across 61 degrees of latitude in the Western Atlantic. Glob. Ecol. Biogeogr. 28, 107–117. https://doi.org/10.1111/geb.12806 (2019).Article 

    Google Scholar 
    Bolser, R. & Hay, M. Are tropical plants better defended? Palatability and defenses of temperate versus tropical seaweeds. Ecology 77, 2269–2286 (1996).Article 

    Google Scholar 
    Borer, E. T. et al. Global biogeography of autotroph chemistry: is insolation a driving force?. Oikos 122, 1121–1130. https://doi.org/10.1111/j.1600-0706.2013.00465.x (2013).Article 
    CAS 

    Google Scholar 
    Miranda, T. et al. Convictfish on the move: Variation in growth and trophic niche space along a latitudinal gradient. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsz098%JICESJournalofMarineScience (2019).Article 

    Google Scholar 
    Linton, S. M. The structure and function of cellulase (endo-β-1, 4-glucanase) and hemicellulase (β-1, 3-glucanase and endo-β-1, 4-mannase) enzymes in invertebrates that consume materials ranging from microbes, algae to leaf litter. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 240, 110354 (2020).Article 
    CAS 

    Google Scholar 
    Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925. https://doi.org/10.1038/nclimate1958 (2013).Article 
    ADS 

    Google Scholar 
    Nakamura, Y., Feary, D. A., Kanda, M. & Yamaoka, K. Tropical fishes dominate temperate reef fish communities within western Japan. PLoS ONE 8, e81107 (2013).Article 
    ADS 

    Google Scholar 
    Tanaka, K., Taino, S., Haraguchi, H., Prendergast, G. & Hiraoka, M. Warming off southwestern Japan linked to distributional shifts of subtidal canopy-forming seaweeds. Ecol. Evol. 2, 2854–2865. https://doi.org/10.1002/ece3.391 (2012).Article 

    Google Scholar 
    Pessarrodona, A. et al. Homogenization and miniaturization of habitat structure in temperate marine forests. Glob. Change Biol. 27, 5262–5275 (2021).Article 
    CAS 

    Google Scholar 
    Yamano, H., Sugihara, K. & Nomura, K. Rapid poleward range expansion of tropical reef corals in response to rising sea surface temperatures. Geophys. Res. Lett. 38, L04601. https://doi.org/10.1029/2010gl046474 (2011).Article 
    ADS 

    Google Scholar 
    Mezaki, T. & Kubota, S. Changes of hermatypic coral community in coastal sea area of Kochi, high-latitude Japan. Aquabiology 201, 332–337 (2012).
    Google Scholar 
    Serisawa, Y., Imoto, Z., Ishikawa, T. & Ohno, M. Decline of the Ecklonia cava population associated with increased seawater temperatures in Tosa Bay, southern Japan. Fish Sci 70, 189–191. https://doi.org/10.1111/j.0919-9268.2004.00788.x (2004).Article 
    CAS 

    Google Scholar 
    Kiriyama, T., Mitsunaga, N., Yasumoto, S., Fujii, A. & Yotsui, T. Undergrown phenomenon of brown alga, Hizikia fusiformis, thought to be caused by grazing of herbivores at Tsutsuura, Tsushima Islands [Japan]. Bulletin of Nagasaki Prefectural Institute of Fisheries (Japan) (1999).Kiriyama, T., Fujii, A. & Fujita, Y. Feeding and characteristic bite marks on Sargassum fusiforme by several herbivorous fishes. Aquac. Sci. 53, 355–365 (2005).
    Google Scholar 
    Yatsuya, K., Kiriyama, T., Kiyomoto, S., Taneda, T. & Yoshimura, T. On the deterioration process of Ecklonia and Eisenia beds observed in 2013 at Gounoura, Iki Island, Nagasaki Prefecture, Japan.-Initiation of the bed degradation due to high water temperature in summer and subsequent cascading effect by the grazing of herbivorous fish in autumn. Algal Resour. 7, 79–94 (2014).
    Google Scholar 
    Noda, M., Ohara, H., Murase, N., Ikeda, I. & Yamamoto, K. The grazing of Eisenia bicyclis and several species of Sargassaceous and Cystoseiraceous seaweeds by Siganus fuscescens in relation to the differences of species composition of their seaweed beds. Nippon Suisan Gakkaishi 80, 201–213 (2014).Article 

    Google Scholar 
    Noda, M., Kinoshita, J., Tanada, N. & Murase, N. Characteristics of bite scars observed in kelp forests of Lessoniaceae denuded by short-term foraging damages of the herbivorous fish Siganus fuscecens. J. Natl. Fish. Univ. 66, 111–122 (2018).
    Google Scholar 
    Wernberg, T. et al. Seaweed communities in retreat from ocean warming. Curr. Biol. 21, 1828–1832. https://doi.org/10.1016/j.cub.2011.09.028 (2011).Article 
    CAS 

    Google Scholar 
    Terazono, Y., Nakamura, Y., Imoto, Z. & Hiraoka, M. Fish response to expanding tropical Sargassum beds on the temperate coasts of Japan. Mar. Ecol. Prog. Ser. 464, 209–220. https://doi.org/10.3354/meps09873 (2012).Article 
    ADS 

    Google Scholar 
    Duffy, J. E. & Hay, M. E. Seaweed adaptations to herbivory – chemical, structural, and morphological defenses are often adjusted to spatial or temporal patterns of attack. Bioscience 40, 368–375 (1990).Article 

    Google Scholar 
    Endo, H., Suehiro, K., Kinoshita, J. & Agatsuma, Y. Combined effects of temperature and nutrient enrichment on palatability of the brown alga Sargassum yezoense (Yamada) Yoshida & T. Konno. Am. J. Plant Sci. 6, 275 (2015).Article 
    CAS 

    Google Scholar 
    Clements, K. D., German, D. P., Piché, J., Tribollet, A. & Choat, J. H. Integrating ecological roles and trophic diversification on coral reefs: Multiple lines of evidence identify parrotfishes as microphages. Biol. J. Linn. Soc. 120, 729–751. https://doi.org/10.1111/bij.12914 (2017).Article 

    Google Scholar 
    Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund–an R package for model-based analysis of multivariate abundance data. Methods Ecol. Evol. 3, 471–474 (2012).Article 

    Google Scholar 
    Wilson, S. K., Bellwood, D. R., Choat, J. H. & Furnas, M. J. Detritus in the epilithic algal matrix and its use by coral reef fishes. Oceanogr. Mar. Biol. Annu. Rev. 41, 279–309 (2003).
    Google Scholar 
    Helfman, G. S. in The Behaviour of Teleost Fishes 366–387 (Springer, 1986).Prince, J., LeBlanc, W. & Maciá, S. Design and analysis of multiple choice feeding preference data. Oecologia 138, 1–4 (2004).Article 
    ADS 

    Google Scholar 
    Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.3 3 (2020).Ohno, M. & Ishikawa, M. Physiological ecology of brown alga, Ecklonia on coast of Tosa Bay, southern Japan. I. Seasonal variation of Ecklonia bed. Rep. USA Marine Biol. Inst. Kochi Univ. 4, 59–73 (1982).
    Google Scholar 
    Agostini, S. et al. Simplification, not “tropicalization”, of temperate marine ecosystems under ocean warming and acidification. Glob. Change Biol. 27, 4771–4784 (2021).Article 
    CAS 

    Google Scholar 
    Clements, K. & Choat, J. Influence of season, ontogeny and tide on the diet of the temperate marine herbivorous fish Odax pullus (Odacidae). Mar. Biol. 117, 213–220 (1993).Article 

    Google Scholar 
    Mizuta, H., Hayasaki, J. & Yamamoto, H. Relationship between nitrogen content and sorus formation in the brown alga Laminaria japonica cultivated in southern Hokkaido, Japan. Fish. Sci. 64, 909–913 (1998).Article 
    CAS 

    Google Scholar 
    Kumura, T., Yasui, H. & Mizuta, H. Nutrient requirement for zoospore formation in two alariaceous plants Undaria pinnatifida (Harvey) Suringar and Alaria crassifolia Kjellman (Phaeophyceae: Laminariales). Fish. Sci. 72, 860–869 (2006).Article 
    CAS 

    Google Scholar 
    Qiu, Z. et al. Future climate change is predicted to affect the microbiome and condition of habitat-forming kelp. Proc. R. Soc. B 286, 20181887 (2019).Article 

    Google Scholar 
    Hoey, A. S. & Bellwood, D. R. Limited functional redundancy in a high diversity system: Single species dominates key ecological process on coral reefs. Ecosystems 12, 1316–1328. https://doi.org/10.1007/s10021-009-9291-z (2009).Article 

    Google Scholar 
    Streit, R. P., Hoey, A. S. & Bellwood, D. R. Feeding characteristics reveal functional distinctions among browsing herbivorous fishes on coral reefs. Coral Reefs 34, 1037–1047 (2015).Article 
    ADS 

    Google Scholar 
    Van Alstyne, K. L. & Paul, V. J. The biogeography of polyphenolic compounds in marine macroalgae – Temperate brown algal defenses deter feeding by tropical herbivorous fishes. Oecologia 84, 158–163 (1990).Article 
    ADS 

    Google Scholar 
    Targett, N. M., Boettcher, A. A., Targett, T. E. & Vrolijk, N. H. Tropical marine herbivore assimilation of phenolic-rich plants. Oecologia 103, 170–179 (1995).Article 
    ADS 

    Google Scholar 
    Prado, P. & Heck, K. L. Seagrass selection by omnivorous and herbivorous consumers: Determining factors. Mar. Ecol. Prog. Ser. 429, 45–55. https://doi.org/10.3354/meps09076 (2011).Article 
    ADS 

    Google Scholar 
    Montgomery, W. L. & Gerking, S. D. Marine macroalgae as foods for fishes: an evaluation of potential food quality. Environ. Biol. Fish. 5, 143–153 (1980).Article 

    Google Scholar 
    Duffy, J. & Paul & V.J.,. Prey nutritional quality and the effectiveness of chemical defenses against tropical reef fishes. Oecologia 90, 333–339 (1992).Article 
    ADS 
    CAS 

    Google Scholar 
    Michael, P. J., Hyndes, G. A., Vanderklift, M. A. & Vergés, A. Identity and behaviour of herbivorous fish influence large-scale spatial patterns of macroalgal herbivory in a coral reef. Mar. Ecol. Prog. Ser. 482, 227–240 (2013).Article 
    ADS 

    Google Scholar 
    Bennett, S. & Bellwood, D. R. Latitudinal variation in macroalgal consumption by fishes on the Great Barrier Reef. Mar. Ecol. Prog. Ser. 426, 241–252 (2011).Article 
    ADS 

    Google Scholar 
    Zarco-Perello, S., Wernberg, T., Langlois, T. J. & Vanderklift, M. A. Tropicalization strengthens consumer pressure on habitat-forming seaweeds. Sci. Rep. 7, 820. https://doi.org/10.1038/s41598-017-00991-2 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Smith, S. M. et al. Tropicalisation and kelp loss shift trophic composition and lead to more winners than losers in fish communities. Glob. Change Biol. 27(11), 2537–2548 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Zarco-Perello, S. et al. Range-extending tropical herbivores increase diversity, intensity and extent of herbivory functions in temperate marine ecosystems. Funct. Ecol. 34, 2411–2421. https://doi.org/10.1111/1365-2435.13662 (2020).Article 

    Google Scholar  More

  • in

    Seasonal peak photosynthesis is hindered by late canopy development in northern ecosystems

    Piao, S., Friedlingstein, P., Ciais, P., Viovy, N. & Demarty, J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, GB3018 (2007).Article 

    Google Scholar 
    Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).Article 

    Google Scholar 
    Xia, J., Niu, S., Ciais, P. & Janssens, I. A. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl Acad. Sci. USA 112, 2788–2793 (2015).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Yang, J. et al. Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China. Remote Sens. Environ. 233, 111395 (2019).Article 

    Google Scholar 
    Huang, K., Xia, J., Wang, Y. & Ahlstrom, A. Enhanced peak growth of global vegetation and its key mechanisms. Nat. Ecol. Evol. 2, 1897–1905 (2018).Article 
    PubMed 

    Google Scholar 
    Park, T., Chen, C. & Macias-Fauria, M. Changes in timing of seasonal peak photosynthetic activity in northern ecosystems. Glob. Change Biol. 25, 2382–2395 (2019).Article 

    Google Scholar 
    Medlyn, B. E. Physiological basis of the light use efficiency model. Tree Physiol. 18, 167 (1998).Article 
    PubMed 

    Google Scholar 
    Turner, D. P., Urbanski, S., Bremer, D., Wofsy, S. C. & Gregory, M. A cross-biome comparison of daily light use efficiency for gross primary production. Glob. Change Biol. 9, 383–395 (2003).Article 

    Google Scholar 
    Monteith, J. L. Solar radiation and productivity in tropical ecosystems. Appl. Ecol. 9, 747–766 (1972).Article 

    Google Scholar 
    Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).Article 
    PubMed 
    CAS 

    Google Scholar 
    Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).Article 
    CAS 

    Google Scholar 
    Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011).Article 

    Google Scholar 
    Yuan, H., Dai, Y., Xiao, Z., Ji, D. & Shangguan, W. Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling. Remote Sens. Environ. 115, 1171–1187 (2011).Article 

    Google Scholar 
    Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).Article 
    PubMed 
    CAS 

    Google Scholar 
    Wang, X. et al. Globally consistent patterns of asynchrony in vegetation phenology derived from optical, microwave, and fluorescence satellite data. J. Geophys. Res. Biogeosci. 125, e2020JG005732 (2020).Article 

    Google Scholar 
    Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).Article 
    PubMed 
    CAS 

    Google Scholar 
    Zhang, Y., Commane, R., Zhou, S., Williams, A. P. & Gentine, P. Light limitation regulates the response of autumn terrestrial carbon uptake to warming. Nat. Clim. Change 10, 739–743 (2020).Article 
    CAS 

    Google Scholar 
    Yuan, W. et al. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agric. For. Meteorol. 192-193, 108–120 (2014).Article 

    Google Scholar 
    Reich, P. B. et al. Temperature drives global patterns in forest biomass distribution in leaves, stems, and roots. Proc. Natl Acad. Sci. USA 111, 13721–13726 (2014).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Wright, I. J., Reich, P. B. & Westoby, M. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).Article 
    PubMed 
    CAS 

    Google Scholar 
    Reich, P. B., Oleksyn, J. & Wright, I. J. Leaf phosphorus influences the photosynthesis–nitrogen relation: a cross-biome analysis of 314 species. Oecologia 160, 207–212 (2009).Article 
    PubMed 

    Google Scholar 
    Chen, Y., Han, W., Tang, L., Tang, Z. & Fang, J. Leaf nitrogen and phosphorus concentrations of woody plants differ in responses to climate, soil and plant growth form. Ecography 36, 178–184 (2013).Article 

    Google Scholar 
    Jiang, M., Caldararu, S., Zaehle, S., Ellsworth, D. S. & Medlyn, B. E. Towards a more physiological representation of vegetation phosphorus processes in land surface models. New Phytol. 222, 1223–1229 (2019).Article 
    PubMed 

    Google Scholar 
    Kergoat, L., Lafont, S., Arneth, A., Le Dantec, V. & Saugier, B. Nitrogen controls plant canopy light-use efficiency in temperate and boreal ecosystems. J. Geophys. Res. Biogeosci. 113, G04017 (2008).Article 

    Google Scholar 
    Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).Article 
    CAS 

    Google Scholar 
    Cleveland, C. C. et al. Patterns of new versus recycled primary production in the terrestrial biosphere. Proc. Natl Acad. Sci. USA 110, 12733–12737 (2013).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Veneklaas, E. J. et al. Opportunities for improving phosphorus-use efficiency in crop plants. New Phytol. 195, 306–320 (2012).Article 
    PubMed 
    CAS 

    Google Scholar 
    Janssens, I. A. & Luyssaert, S. Nitrogen’s carbon bonus. Nat. Geosci. 2, 318–319 (2009).Article 
    CAS 

    Google Scholar 
    Luo, X. et al. Global variation in the fraction of leaf nitrogen allocated to photosynthesis. Nat. Commun. 12, 4866 (2021).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Lambers, H., Iii, F. & Pons, T. L. Plant Physiological Ecology (Springer, 2008).Vose, J. M. et al. Factors influencing the amount and distribution of leaf area of pine stands. Ecol. Bull. 43, 102−114 (1994).Carter, S. K., Saenz, D. & Rudolf, V. H. W. Shifts in phenological distributions reshape interaction potential in natural communities. Ecol. Lett. 21, 1143–1151 (2018).Article 
    PubMed 

    Google Scholar 
    Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).Article 

    Google Scholar 
    Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).Article 
    PubMed 
    CAS 

    Google Scholar 
    Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere–biosphere system. Glob. Biogeochem. Cycles 19, GB1015 (2005).Article 

    Google Scholar 
    Murray-Tortarolo, G. et al. Evaluation of land surface models in reproducing satellite-derived LAI over the high-latitude Northern Hemisphere. Part I: Uncoupled DGVMs. Remote Sens. 5, 4819–4838 (2013).Article 

    Google Scholar 
    Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).Article 

    Google Scholar 
    Goll, D. S., Winkler, A. J. & Raddatz, T. Carbon–nitrogen interactions in idealized simulations with JSBACH (version 3.10). Geosci. Model Dev. 10, 2009–2030 (2017).Article 
    CAS 

    Google Scholar 
    Goll, D. S., Vuichard, N. & Maignan, F. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. 10, 3745–3770 (2017).Article 
    CAS 

    Google Scholar 
    Sun, Y., Goll, D. S. & Chang, J. Global evaluation of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 (r5986). Geosci. Model Dev. 14, 1987–2010 (2021).Article 
    CAS 

    Google Scholar 
    Clark, D. B., Mercado, L. M. & Sitch, S. The Joint UK Land Environment Simulator (JULES), model description—Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).Article 

    Google Scholar 
    Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change 9, 684–689 (2019).Article 
    CAS 

    Google Scholar 
    Reyes-Fox, M. et al. Elevated CO2 further lengthens growing season under warming conditions. Nature 510, 259–262 (2014).Article 
    PubMed 
    CAS 

    Google Scholar 
    Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl Acad. Sci. USA 111, E1327–E1333 (2014).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358, eaam5747 (2017).Article 
    PubMed 

    Google Scholar 
    Joiner, J. et al. The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange. Remote Sens. Environ. 152, 375–391 (2014).Article 

    Google Scholar 
    Chu, D. et al. Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion. Remote Sens. Environ. 264, 112632 (2021).Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 6, 2803–2823 (2013).Article 

    Google Scholar 
    Zhang, Y., Joiner, J., Gentine, P. & Zhou, S. Reduced solar-induced chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset artifacts. Glob. Change Biol. 24, 2229–2230 (2018).Article 

    Google Scholar 
    Rodell, M., Houser, P. R. & Jambor, U. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).Article 

    Google Scholar 
    Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. 11, 1424–1439 (2005).Article 

    Google Scholar 
    LASSLOP, G. et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob. Change Biol. 16, 187–208 (2010).Article 

    Google Scholar 
    Vautard, R., Yiou, P. & Ghil, M. Singular-spectrum analysis: a toolkit for short, noisy chaotic signals. Phys. D. 58, 95–126 (1992).Article 

    Google Scholar 
    Zhou, S. et al. Dominant role of plant physiology in trend and variability of gross primary productivity in North America. Sci. Rep. 7, 41366 (2017).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Butler, E. E., Datta, A. & Flores-Moreno Mapping local and global variability in plant trait distributions. Proc. Natl Acad. Sci. USA 114, E10937–E10946 (2017).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).Article 

    Google Scholar 
    Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the anthropocene. PLoS ONE 7, e30535 (2012).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Kier, G., Mutke, J., Dinerstein, E., Ricketts, T. H. & Barthlott, W. Global patterns of plant diversity and floristic knowledge. J. Biogeogr. 32, 1107–1116 (2005).Article 

    Google Scholar 
    Boles, S. H. et al. Land cover characterization of temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sens. Environ. 90, 477–489 (2004).Article 

    Google Scholar  More

  • in

    Can the world save a million species from extinction?

    Indonesia’s bleeding toad (Leptophryne cruentata) is critically endangered.Credit: Pepew Fegley/Shutterstock

    One-quarter of all plant and animal species are threatened with extinction owing to factors such as climate change and pollution. Starting this week, negotiators and ministers from more than 190 countries are meeting at a United Nations biodiversity summit called COP15 in Montreal, Canada, to address the emergency.
    10 startling images of nature in crisis — and the struggle to save it
    From 7 to 19 December, they will be trying to seal a new deal to save Earth’s biodiversity. The treaty, known as the post-2020 Global Biodiversity Framework, is intended to establish precise targets for countries to protect and restore nature, including conserving 30% of the planet by 2030 and cutting nutrient pollution, such as reducing nitrogen fertilizer loss from farmland.Time is running out. “We’re driving species to extinction at a rate about 1,000 times faster than they are created through evolution,” says Stuart Pimm, an ecologist at Duke University in Durham, North Carolina, and head of Saving Nature, a non-profit conservation organization.As COP15 kicks off, researchers and policy experts are concerned that countries still disagree on too many issues to secure a deal that will protect species and ecosystems effectively. Here, Nature looks at the extent of the crisis, and what scientists say countries must do to succeed.Which species are most at risk, and what’s threatening them?Among the most at-risk groups are amphibians and reef-forming corals. A global assessment shows that more than 40% of amphibians are threatened with extinction1, including the critically endangered bleeding toad (Leptophryne cruentata), which lives in Mount Gede Pangrango National Park in Java, Indonesia.These toads were thought to be extinct until the year 2000, when some were spotted by a team led by Mirza Kusrini, a herpetologist at Bogor Agricultural University in Indonesia. But the researchers found that the amphibians were infected with chytrid (Chytridiomycota sp.), a fungus that has devastated global amphibian populations. Kusrini says that climate change is probably making life hard for the tiny toad, which got its common name from the crimson, splatter-like spots covering its body. Warm weather can stimulate fungal outbreaks and shift the timing of behaviours, such as the toads’ breeding season, making the amphibians vulnerable.

    Source: Red List Index/IUCN

    Global warming, which has been raising sea temperatures, is also responsible for harming coral reefs around the globe (see ‘Threat assessment’). Over a period of 9 years, up to 2018, 14% of the world’s coral died out — a massive problem, because today, coral reefs support one-quarter of all marine species.Research shows that climate change is quickly becoming a large threat to biodiversity2. But still, the most-destructive forces are the conversion of land and seas for agricultural uses and people exploiting natural resources through fishing, logging, hunting and the wildlife trade. About 75% of land and 66% of ocean areas have been significantly altered, usually for producing food.What might happen if species disappear?It’s difficult to predict, because doing so requires knowledge of which species are present in a particular ecosystem, such as a rainforest, and what functions they have, says Shahid Naeem, an ecologist at Columbia University in New York City. Much of that information is often unknown. However, scientists have shown3 that ecosystems with less biodiversity are not as good at capturing and converting resources into biomass, such as happens when plants capture nutrients or sunlight used for growth.
    Why deforestation and extinctions make pandemics more likely
    Neither are less-diverse ecosystems as good at decomposing and recycling biological materials and nutrients. For example, studies show that dead organisms are broken down, and their nutrients recycled, more quickly when a high variety of plant litter covers the forest floor4. Ecosystems with low biodiversity also have low resilience — they are not as able to bounce back after a perturbation or shock, such as a fire, as more-diverse systems are, Naeem says.“If we lose parts of our system, it simply won’t function very efficiently, and it won’t be very robust,” he adds. “The science behind that is rock solid.”Ecosystems also provide clean water and can sometimes prevent diseases from spreading to humans. When species are lost, these services deteriorate, Kusrini says. For example, most amphibians eat insects, many of which are considered pests, such as cockroaches, termites and mosquitoes. Studies have shown a rise in cases of malaria — spread by mosquitoes — in areas in Central America where amphibian populations have collapsed5. “You know when they disappear”, Kusrini says, because insect numbers rise and people start using more pesticides to kill them.What solutions do researchers say are needed to protect biodiversity?Protecting and conserving habitats is central to saving species. This idea is captured in the framework being negotiated at COP15. The draft includes the goal of conserving at least 30% of the world’s land and sea by 2030. But for protections to be most effective, they must include regions that are rich in biodiversity, such as tropical forests, Pimm says. Despite an increase in protected areas worldwide over the past ten years, species numbers have still declined, because these safeguards were not in the right places, studies show6.

    Delegates at COP15 in Montreal show their support for a new agreement among nations to protect Earth’s biodiversity.Credit: UN Convention on Biological Diversity (CC BY 2.0)

    “What we’re going to be looking for at COP15 is more quality, not just more quantity,” Pimm says.Eradicating invasive species is another important conservation strategy, and the framework’s draft currently calls for cutting the introduction of such species in half. Some estimates suggest that invasive predators, such as cats and rats, are responsible for more than half of all extinctions of birds, mammals and reptiles7.It’s important that nations agree on a framework with at least some quantifiable targets, so that progress can be measured, and so that countries can be held accountable if they fail to meet their targets, researchers say. “I’m afraid what will happen is, they will produce a long list of ‘waffle’,” Pimm says. “We need quantification.”Will nations manage to agree on a new deal to protect nature?As COP15 begins, the outlook is not good. The text of the draft is still littered with unresolved issues. At a press conference on 6 December, Elizabeth Mrema, executive secretary of the Convention on Biological Diversity — the global treaty that underpins the new biodiversity deal — said that national negotiators had made insufficient progress in a final round of discussions before the start of the summit. She urged countries to compromise, otherwise they will fail to reach a deal. “The state of the planet is in crisis,” Mrema said. “This is our last chance to act.”
    Troubled biodiversity plan gets billion-dollar funding boost
    One key contentious issue is how to finance biodiversity conservation, particularly in low- and middle-income countries, which are home to much of the world’s biodiversity. These nations, including Brazil and Gabon, would like a new fund to be established with US$100 billion added per year in aid. So far, that proposal has not gained traction with wealthier countries. “They really need to have the financial commitments, because things don’t get done without the money,” Naeem says.Despite the pessimism, Naeem is certain that scientists and advocates will keep pushing for a deal. “There would be real change” if countries were able to achieve a universal decrease in biodiversity loss, he says. More

  • in

    Biomechanical traits of salt marsh vegetation are insensitive to future climate scenarios

    Narayan, S. et al. The effectiveness, costs and coastal protection benefits of natural and nature-based defences. PLoS ONE 11, e0154735 (2016).Article 

    Google Scholar 
    Schürch, M., Rapaglia, J., Liebetrau, V., Vafeidis, A. T. & Reise, K. Salt marsh accretion and storm tide variation: An example from a barrier island in the North Sea. ESCO 35, 486–500 (2012).
    Google Scholar 
    de Groot, A. V., Veeneklaas, R. M., Kuijper, D. P. & Bakker, J. P. Spatial patterns in accretion on barrier-island salt marshes. Geomorphology 134, 280–296 (2011).Article 
    ADS 

    Google Scholar 
    Temmerman, S. et al. Ecosystem-based coastal defence in the face of global change. Nature 504, 79–83 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Barbier, E. B. et al. Coastal ecosystem: Based management with nonlinear ecologial functions and values. Science 319, 321–323 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Schoonees, T. et al. Hard structures for coastal protection, towards greener designs. Estuaries Coasts 21, 755 (2019).
    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 (2021).Lenssen, G. M., Lamers, J., Stroetenga, M. & Rozema, J. CO2 and biosphere 379–390 (Kluwer Academic Publishers, 1993).Book 

    Google Scholar 
    Cherry, J. A., McKee, K. L. & Grace, J. B. Elevated CO2 enhances biological contributions to elevation change in coastal wetlands by offsetting stressors associated with sea-level rise. J. Ecol. 97, 67–77 (2009).Article 

    Google Scholar 
    Arp, W. J., Drake, B. G., Pockman, W. T., Curtis, P. S. & Whigham, D. F. CO2 and Biosphere 133–143 (Kluwer Academic Publishers, 1993).Book 

    Google Scholar 
    Cao, H. et al. Wave effects on seedling establishment of three pioneer marsh species: survival, morphology and biomechanics. Ann. Bot. 125, 345–352 (2020).Article 

    Google Scholar 
    Puijalon, S. et al. Plant resistance to mechanical stress: Evidence of an avoidance-tolerance trade-off. New Phytol. 191, 1141–1149 (2011).Article 
    CAS 

    Google Scholar 
    Niklas, K. Plant Biomechanics: An Engineering Approach to Plant Form and Function (University of Chicago Press, 1992).
    Google Scholar 
    Silinski, A. et al. Effects of wind waves versus ship waves on tidal marsh plants: A flume study on different life stages of Scirpus maritimus. PLoS ONE 10, e0118687 (2015).Article 

    Google Scholar 
    Rupprecht, F., Möller, I., Evans, B. R., Spencer, T. & Jensen, K. Biophysical properties of salt marsh canopies: Quantifying plant stem flexibility and above ground biomass. Coast. Eng. 100, 48–57 (2015).Article 

    Google Scholar 
    Paul, M. & de los Santos, C. B. Variation in flexural, morphological, and biochemical leaf properties of eelgrass (Zostera marina) along the European Atlantic climate regions. Mar. Biol. 166, 2187 (2019).Article 

    Google Scholar 
    Carus, J., Paul, M. & Schröder, B. Vegetation as self-adaptive coastal protection: Reduction of current velocity and morphologic plasticity of a brackish marsh pioneer. Ecol. Evol. 6, 1579–1589 (2016).Article 

    Google Scholar 
    Callaghan, F. M. et al. A submersible device for measuring drag forces on aquatic plants and other organisms. NZ J. Mar. Freshw. Res. 41, 119–127 (2007).Article 

    Google Scholar 
    Paul, M., Bouma, T. J. & Amos, C. L. Wave attenuation by submerged vegetation: combining the effect of organism traits and tidal current. Mar. Ecol. Prog. Ser. 444, 31–41 (2012).Article 
    ADS 

    Google Scholar 
    Taphorn, M., Villanueva, R., Paul, M., Visscher, J. H. & Schlurmann, T. Flow field and wake structure characteristics imposed by single seagrass blade surrogates. J. Ecohydraul. 1, 1–13 (2021).
    Google Scholar 
    Lightbody, A. F. & Nepf, H. M. Prediction of velocity profiles and longitudinal dispersion in emergent salt marsh vegetation. Limnol. Oceangr 51, 218–228 (2006).Article 
    ADS 

    Google Scholar 
    Kobayashi, N., Raichle, A. W. & Asano, T. Wave attenuation by vegetation. J. Waterway Port Coastal Ocean Eng. 119, 30–48 (1993).Article 

    Google Scholar 
    Villanueva, R., Thom, M., Visscher, J. H., Paul, M. & Schlurmann, T. Wake length of an artificial seagrass meadow: A study of shelter and its feasibility for restoration. J. Ecohydraul. 1, 1–15 (2021).
    Google Scholar 
    Paul, M. & Amos, C. L. Spatial and seasonal variation in wave attenuation over Zostera noltii. J. Geophys. Res. 116, C08019 (2011).ADS 

    Google Scholar 
    Marjoribanks, T. I. & Paul, M. Modelling flow-induced reconfiguration of variable rigidity aquatic vegetation. J. Hydraul. Res. 1, 1–16 (2021).
    Google Scholar 
    Schulze, D., Rupprecht, F., Nolte, S. & Jensen, K. Seasonal and spatial within-marsh differences of biophysical plant properties: Implications for wave attenuation capacity of salt marshes. Aquat. Sci. 81, 82 (2019).Article 

    Google Scholar 
    Gillis, L. G. et al. Living on the edge: How traits of ecosystem engineers drive bio-physical interactions at coastal wetland edges. Adv. Water Resour. 166, 104257 (2022).Article 

    Google Scholar 
    Zhao, H. & Chen, Q. Modeling attenuation of storm surge over deformable vegetation: methodology and verification. J. Eng. Mech. 140, 4014090 (2014).
    Google Scholar 
    Möller, I. et al. Wave attenuation over coastal salt marshes under storm surge conditions. Nat. Geosci 7, 727–731 (2014).Article 
    ADS 

    Google Scholar 
    Maza, M. et al. Large-scale 3-D experiments of wave and current interaction with real vegetation. Part 2. Experimental analysis. Coast. Eng. 106, 73–86 (2015).Article 

    Google Scholar 
    Gray, A. J. & Mogg, R. J. Climate impacts on pioneer saltmarsh plants. Clim. Res. 18, 105–112 (2001).Article 

    Google Scholar 
    Novaes, E., Kirst, M., Chiang, V., Winter-Sederoff, H. & Sederoff, R. Lignin and biomass: A negative correlation for wood formation and lignin content in trees. Plant Physiol. 154, 555–561 (2010).Article 
    CAS 

    Google Scholar 
    Redfield, A. C. Development of a New England salt marsh. Ecol. Monogr. 42, 201–237 (1972).Article 

    Google Scholar 
    Kirwan, M. L. et al. Limits on the adaptability of coastal marshes to rising sea level. Geophys. Res. Lett. 37, 1–10 (2010).Article 

    Google Scholar 
    Idier, D., Dumas, F. & Muller, H. Tide-surge interaction in the English Channel. Nat. Hazards Earth Syst. Sci. 12, 3709–3718 (2012).Article 
    ADS 

    Google Scholar 
    Weisse, R., von Storch, H., Niemeyer, H. D. & Knaack, H. Changing North Sea storm surge climate: An increasing hazard?. Ocean Coast. Manag. 68, 58–68 (2012).Article 

    Google Scholar 
    Idier, D., Paris, F., Le Cozannet, G., Boulahya, F. & Dumas, F. Sea-level rise impacts on the tides of the European Shelf. Cont. Shelf Res. 137, 56–71 (2017).Article 
    ADS 

    Google Scholar 
    Marcos, M., Calafat, F. M., Berihuete, Á. & Dangendorf, S. Long-term variations in global sea level extremes. J. Geophys. Res. Oceans 120, 8115–8134 (2015).Article 
    ADS 

    Google Scholar 
    Dangendorf, S., Mudersbach, C., Jensen, J., Anette, G. & Heinrich, H. Seasonal to decadal forcing of high water level percentiles in the German Bight throughout the last century. Ocean Dyn. 46, 277 (2013).
    Google Scholar 
    de Winter, R. C., Sterl, A. & Ruessink, B. G. Wind extremes in the North Sea Basin under climate change: An ensemble study of 12 CMIP5 GCMs. J. Geophys. Res. Atmos. 118, 1601–1612 (2013).Article 
    ADS 

    Google Scholar 
    Arns, A. et al. Sea-level rise induced amplification of coastal protection design heights. Sci. Rep. 7, 40171 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Pansch, A., Winde, V., Asmus, R. & Asmus, H. Tidal benthic mesocosms simulating future climate change scenarios in the field of marine ecology. Limnol. Oceanogr. Methods 14, 257–267 (2016).Article 

    Google Scholar 
    Meehl, G. A. et al. Climate Change 2007: The Physical Science Basis: Summary for Policymakers. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2007).
    Google Scholar 
    Miler, O., Albayrak, I., Nikora, V. I. & O’Hare, M. T. Biomechanical properties of aquatic plants and their effects on plant–flow interactions in streams and rivers. Aquat. Sci. 74, 31–44 (2012).Article 

    Google Scholar  More

  • in

    The maternal effects of dietary restriction on Dnmt expression and reproduction in two clones of Daphnia pulex

    Aagaard-Tillery KM, Grove K, Bishop J, Ke X, Fu Q, McKnight R et al. (2008) Developmental origins of disease and determinants of chromatin structure: maternal diet modifies the primate fetal epigenome. J Mol Endocrinol 41:91–102Article 
    CAS 

    Google Scholar 
    Alekseev V, Lampert W (2001) Maternal control of resting – egg production in Daphnia. Nature 414:899–901Article 
    CAS 

    Google Scholar 
    Anderson OS, Sant KE, Dolinoy DC (2012) Nutrition and epigenetics: an interplay of dietary methyl donors, one-carbon metabolism and DNA methylation. J Nutritional Biochem 23:853–859Article 
    CAS 

    Google Scholar 
    Agrawal AA, Laforsch C, Tollrian R (1999) Transgenerational induction of defenses in animals and plants. Nature 401:60–63Article 
    CAS 

    Google Scholar 
    Asselman J, De Coninck DIM, Vandegehuchte MB, Jansen M, Decaestecker E, De Meester L, Busshe JV, Vanhaecke L, Janssen CR, De Schamphelaere KAC (2015) Global cytosine methylation in Daphnia magna depends on genotype, environment, and their interaction. Environ Toxicol 34:5
    Google Scholar 
    Bernardo J (1996) Maternal effects in animal ecology. Am Zool 36:83–105Article 

    Google Scholar 
    Berger SL (2007) The complex language of chromatin regulation during transcription. Nature 447:407–412Article 
    CAS 

    Google Scholar 
    Bewick AJ, Vogel KJ, Moore AJ, Schmitz RJ (2017) Evolution of DNA methylation across insects. Mol Biol Evol 34:654–665CAS 

    Google Scholar 
    Bird A (2007) Perceptions of epigenetics. Nature 447:396–398Article 
    CAS 

    Google Scholar 
    Boersma M (1995) The allocation of resources to reproduction in Daphnia galeata: against the odds? Ecology 76(4):121–1261Article 

    Google Scholar 
    Boersma M (1997) Offspring size in Daphnia: does it pay to be overweight? Hydrobiologia 360:79–88Article 

    Google Scholar 
    Boycott AE, Diver C (1923) On the inheritance of the sinistrality in Limnea peregra. Proc R Soc Lond B 95:207–213Article 

    Google Scholar 
    Brett MT (1993) Resource quality effects on Daphnia longispina offspring fitness. J Plankton Res 15(4):403–412Article 

    Google Scholar 
    Burns CW (1995) Effects of crowding and different food levels on growth and reproductive investment of Daphnia. Oecologia 101:234–244Article 

    Google Scholar 
    Cameron NM, Shahrokh D, Del Corpo A, Dhir SK, Szyf M, Champagne FA, Meaney MJ (2008) Epigenetic programming of phenotypic variations in reproductive strategies in the rat through maternal care. J Neuroendocrinol 20:795–801Article 
    CAS 

    Google Scholar 
    Champagne FA (2012) Epigenetics and developmental plasticity across species. Dev Psychobiol 55:33–41Article 

    Google Scholar 
    Chan SY, Vasilopoulou E, Kilby MD (2009) The role of the placenta in thyroid hormone delivery to the fetus. Nat Clin Pract Endocrinol Metab 5:45–54Article 
    CAS 

    Google Scholar 
    Chong S, Whitelaw E (2004) Epigenetic germline inheritance. Curr Opin Genet Dev 14(6):692–696Article 
    CAS 

    Google Scholar 
    Clark J, Garbutt JS, McNally L, Little TJ (2017) Disease spread in age structured populations with maternal age effects. Ecol Lett 20:445–451Article 

    Google Scholar 
    Colbourne JK, Herbert PDN, Taylor DJ (1997) Evolutionary origins of phenotypic diversity. In: Givnish TJ, Systma KJ (eds) Daphnia in molecular evolution and adaptive radiation. Cambridge University Press. p 163–188Colbourne JK, Pfrender ME, Gilbert D, Thomas WK, Tucker A, Oakley TH et al. (2011) The ecoresponsive genome of Daphnia pulex. Science 331(6017):555–561Article 
    CAS 

    Google Scholar 
    Desmarais KH (1997) Keeping Daphnia out of the surface film with cetyl alcohol. J Plankton Res 19(1):149–154Article 

    Google Scholar 
    Dorts J, Falisse E, Schoofs E, Flamion E, Kestermont P, Silvestre F (2016) DNA methyltransferases and stress-related genes expression in zebrafish larvae after exposure to heat and copper during reprogramming of DNA methylation. Sci Rep 6:34254Article 
    CAS 

    Google Scholar 
    Ducker GS, Rabinowitz JD (2016) One-carbon metabolism in health and disease. Cell Metab 25:27–42. https://doi.org/10.1016/j.cmet.2016.08Dudycha JL, Brandon CS, Deitz KC (2012) Population genomics of resource exploitation: insights from gene expression profiles of two Daphnia ecotypes fed alternate resources. Ecol Evol 2:329–340Dzialowski EM, Reed WL, Sotherland PR (2009) Effects of egg size on double-crested cormorant (Phalacrocorax auritus) egg composition and hatchling phenotype. Comp Biochem Physiol A Mol Integr Physiol 152:262–267Article 

    Google Scholar 
    Frost PC, Ebert D, Larson JH, Marcus MA, Wagner ND, Zalewski A (2010) Transgenerational effects of poor elemental food quality on Daphnia magna. Oecologia 162(4):865–872Article 

    Google Scholar 
    Gabsi F, Glazier DS, Hammers-Wirtz M, Ratte HT, Preuss TG (2014) How to interactive maternal traits and environmental factors determine offspring size in Daphnia magna?. Ann Limnol 50:9–18Article 

    Google Scholar 
    Garbutt JS, Little TJ (2016) Bigger is better: changes in body size explain a maternal effect of food on offspring disease resistance. Ecol Evolution 7:1403–1409Article 

    Google Scholar 
    Gibney ER, Nolan CM (2010) Epigenetics and gene expression. Heredity 105:4–13Article 
    CAS 

    Google Scholar 
    Gillis MK, Walsh MR (2019) Individual variation in plasticity dulls transgenerational responses to stress. Funct Ecol 33:1993–2002Glazier DS (1992) Effects of food, genotype, and maternal size and age on offspring investment in Daphnia magna. Ecology 73(3):910–926Article 

    Google Scholar 
    Gliwicz ZM, Guisande C (1992) Family planning in Daphnia: resistance to starvation in offspring born to mothers grown at different food levels. Oceologia 91:463–467Article 

    Google Scholar 
    Goos JM, Swain CJ, Munch SB, Walsh MR (2018) Maternal diet and age alter direct and indirect relationships between lifer-history traits across multiple generations. Funct Ecol 33:491–502Article 

    Google Scholar 
    Goulden CE, Horning LL (1980) Population oscillations and energy reserves in planktonic cladocera and their consequences to competition. Proc Natl Acad Sci USA 77:1716–1720Article 
    CAS 

    Google Scholar 
    Groothuis TG, Schwabl H (2008) Hormone-mediated maternal effects in birds: mechanisms matter but what do we know of them? Philos Trans R Soc Lond B Biol Sci 363:1647–1661Article 
    CAS 

    Google Scholar 
    Guisande C, Gliwicz ZM (1992) Egg size and clutch size in two Daphnia species at different food levels. J Plankton Res 14(7):997–1007Article 

    Google Scholar 
    Hearn J, Chow FW-N, Barton H, Tung M, Wilson P, Blaxter M et al. (2018) Daphnia magna microRNAs respond to nutritional stress and ageing but are not transgenerational. Mol Ecol 27:1402–1412Article 
    CAS 

    Google Scholar 
    Hearn J, Pearson M, Blaxter M, Wilson PJ, Little TJ (2019) Genome-wide methylation is modified by caloric restriction in Daphnia magna. BCM Genetics 20:197Hearn J, Plenderleith F, Little TJ (2021) DNA methylation differs extensively between strains of the same geographical origin and changes with age in Daphnia magna. Epigenetics Chromatin 14:4. https://doi.org/10.1186/s13072-020-00379-zHead JA (2014) Patterns of DNA methylation in animals: an ecotoxicological perspective. Integr Comp Biol 54:77–86Article 
    CAS 

    Google Scholar 
    Hebert PDN (1981) Obligate asexuality in Daphnia. Am Nate 117:784–789Article 

    Google Scholar 
    Herman JJ, Sultan SE (2016) DNA methylation mediates genetic variation for adaptive transgenerational plasticity. Proc Biol Sci 283(1838):20160988. https://doi.org/10.1098/rspb.2016.0988Article 
    CAS 

    Google Scholar 
    Hiruta C, Nishida C, Tochinai S (2010) Abortive meiosis in the oogenesis of parthenogenetic Daphnia pulex. Chromosome Res 18:833–840Article 
    CAS 

    Google Scholar 
    Ho DH, Burggren WW (2010) Epigenetics and transgenerational transfer: a physiological perspective. J Exp Biol 213:3–16Article 
    CAS 

    Google Scholar 
    Ho DH (2008) Morphological and physiological developmental consequences of parental effects in the chicken embryo (Gallus gallus domesticus) and the zebrafish larva (Danio rerio). Diss: University of North TexasInnes DJ, Fox CJ, Winsor GL (2000) Avoiding the cost of males in obligately asexual Daphnia pulex (Leydig). Proc: Biol Sci 267(1447):991–997CAS 

    Google Scholar 
    Jeremias G, Barbosa J, Marques SM, De Schamphelaere KAC, Van Nieuwerburgh F, Deforce D, Gonçalves FJM, Pereira JL, Asselman J (2018) Transgenerational inheritance of dna hypomethylation in Daphnia magna in response to salinity stress. Environ Sci Technol 52(17):10114–10123Article 
    CAS 

    Google Scholar 
    Jian X, Yang W, Zhao S, Liang H, Zhao Y, Chen L et al. (2013) Maternal effects of inducible tolerance against the toxic cyanobacterium Microcystis aeruginosa in the grazer Daphnia carinata. Environ Pollut 178:142–146Article 

    Google Scholar 
    Keating KI (1985) The influence of vitamin-B12 deficiency on the reproduction of Daphnia-Pulex Leydig (Cladocera). J Crustacean Biol 5:30–136Article 

    Google Scholar 
    Kleiven OT, Larsson P, Hobaek A (1992) Sexual reproduction in Daphnia magna requires three stimulie. Oikos 65:197–206Article 

    Google Scholar 
    Kusari F, O’Doherty AM, Hodges NJ, Wojewodzic MW (2017) Bi-directional effects of vitamin B12 and methotrexate on Daphnia magna fitness and genomic methylation. Sci Rep 7:11872Article 

    Google Scholar 
    Kvist J, Athanasio CG, Solari OS, Brown JB, Colbourne JK, Pfrender ME, Mirbahai L (2018) Pattern of DNA methylation in Daphnia: evolutionary perspective. Genome Biol Evolution 10(8):1988–2007Article 
    CAS 

    Google Scholar 
    Lamka GF, Harder AM, Sundaram M, Schwartz TS, Christie MR, DeWoody JA, Willoughby JR (2022) Epigenetics in ecology, evolution, and conservation. Front Ecol Evol 10. https://doi.org/10.3389/fevo.2022.871791LaMontagne JM, McCauley E (2001) Maternal effects in Daphnia: what mothers are telling their offspring and do they listen. Ecol Lett 4:64–71Article 

    Google Scholar 
    Li Q, Jiang X (2014) Offspring tolerance to toxic Microcystis aeruginosa in Daphnia pulex shaped by maternal food availability and age. Fundam Appl Limnol 185:315–319Article 

    Google Scholar 
    Mastorci F, Vicentini M, Viltart O, Manghi M, Graiani G, Quaini Fet al. (2009) Long-term effects of prenatal stress: changes in adult cardiovascular regulation and sensitivity to stress. Neurosci Biobehav Rev 33:191–203Article 

    Google Scholar 
    Mkee D, Ebert D (1996) The interactive effects of temperature, food level and maternal phenotype on offspring size in Daphnia magna. Oecologia 107(2):189–196Article 

    Google Scholar 
    Mousseau TA, Fox CW (1998) The adaptive significance of maternal effects. Trends Ecol Evol 13:403–407Article 
    CAS 

    Google Scholar 
    Nguyen ND, Matsuura T, Kato Y, Watanabe H (2020) Caloric restriction upregulates the expression of DNMT3.1, lacking the conserved catalytic domain, in Daphnia magna. Genesis 58:12Article 

    Google Scholar 
    Nguyen ND, Matsuura T, Kato Y, Watanabe H (2021) DNMT3.1 controls trade-offs between growth, reproduction, and life span under starved conditions in Daphnia magna. Sci Rep 11:7326Article 
    CAS 

    Google Scholar 
    Nusslein-Volhard C, Frohnhofer HG, Lehmann R (1987) Determination of anteroposterior polarity in Drosophila. Science 238:1675–1681Article 
    CAS 

    Google Scholar 
    Pieters BJ, Liess M (2006) Maternal nutritional state determines the sensitivity of Daphnia magna offspring to short-term fenvalerate exposure. Aquat Toxicol 76:286–277Article 

    Google Scholar 
    R Core Team (2021) R: a language and environmental for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/Richards EJ (2006) Inherited epigenetic variation – revisiting soft inheritance. Nat Rev Genet 7:395–401Article 
    CAS 

    Google Scholar 
    Stollewerk A (2010) The water flea Daphnia – a new model system for ecology and evolution? J Biol 9(2):21Article 

    Google Scholar 
    Sturtevant AH (1923) Inheritance of direction of coiling in Limnea. Science 58:269Article 
    CAS 

    Google Scholar 
    Suter MA, Chen A, Burdine MS, Choudhury M, Harris RA, Lane RH et al. (2012) A maternal high-fat diet modulates fetal SIRT1 histone and protein deacetylase activity in nonhuman primates. FASEB J 26:5106–5114Article 
    CAS 

    Google Scholar 
    Tessier AJ, Consolatti NL (1989) Variation in offspring size in Daphnia and consequences for individual fitness. Oikos 56:269–276Article 

    Google Scholar 
    Tessier AJ, Consolatti NL (1991) Resource quantity and offspring quality in Daphnia. Ecology 72(2):468–478Article 

    Google Scholar 
    Trerotola M, Relli V, Simeone P, Alberti S (2015) Epigenetic inheritance and the missing heritability. Hum Genomics 9(1):17. https://doi.org/10.1186/s40246-015-0041-3Article 
    CAS 

    Google Scholar 
    Trijau M, Asselman J, Armant O, Adam-Guillermin C, De Schamphelaere KAC, Alonzo F (2018) Transgenerational DNA methylation changes in Daphnia magna exposed to chronic γ irradiation. Environ Sci Technol 52(7):4331–4339Article 
    CAS 

    Google Scholar 
    Urabe J, Sterner RW (2001) Contrasting effects of different types of resource depletion on life-history traits in Daphnia. Funct Ecol 15:165–174Article 

    Google Scholar 
    Vandegehuchte MB, Kyndt T, Vanholme B, Haegeman A, Gheysen G, Janssen CR (2009a) Occurrence of DNA methylation in Daphnia magna and influence of multigeneration Cd exposure. Environ Int 35(4):700–706Article 
    CAS 

    Google Scholar 
    Vandegehuchte MB, Lemiere F, Janssen CR (2009b) Quantitative DNA-methylation in Daphnia magna and effects of multigeneration Zn exposure. Comp Biochem Physiol C Toxicol Pharmacol 150:343–348Article 
    CAS 

    Google Scholar 
    Vandegehuchte MB, Janssen CR (2011) Epigenetics and its implications for ecotoxicology. Ecotoxicology 20:607–624Article 
    CAS 

    Google Scholar 
    Vandegehuchte MB, Janssen CR (2014) Epigenetics in an ecotoxicological context. Mutat Res Genet Toxicol Environ Mutagen 764–765:36–45Article 

    Google Scholar 
    Walsh MR, La Pierre KJ, Post DM (2014) Phytoplankton composition modifies predator-driven life history evolution in Daphnia. Evol Ecol 28:397–411Article 

    Google Scholar 
    Walsh MR, Cooley F, Biles K, Munch SB (2015) Predator-induced phenotypic plasticity within- and across generations: a challenge for theory? Proc R Soc B Biol Sci 282:20142205Article 

    Google Scholar 
    Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4. https://ggplot2.tidyverse.orgWolf JB, Wade MJ (2009) What are maternal effects (and what are they not)? Philos Trans R Soc Lond B Biol Sci 364(1520):1107–1115Article 

    Google Scholar 
    Zaffagnini F (1987) Reproduction in Daphnia. Mem Ist Ital Idrobiol 45:245–284
    Google Scholar 
    Zhao S, Fernald RD (2005) Comprehensive algorithm for quantitative real-time polymerase chain reaction. J Comput Biol 12(8):1045–1062Article 

    Google Scholar  More

  • in

    Widespread herbivory cost in tropical nitrogen-fixing tree species

    Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Chang. 4, 471–476 (2014).Article 
    ADS 

    Google Scholar 
    Wright, S. J. Plant responses to nutrient addition experiments conducted in tropical forests. Ecol. Monogr. 89, e01382 (2019).Article 

    Google Scholar 
    Levy-Varon, J. H. et al. Tropical carbon sink accelerated by symbiotic dinitrogen fixation. Nat. Commun. 10, 5637 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Batterman, S. A. et al. Key role of symbiotic dinitrogen fixation in tropical forest secondary succession. Nature 502, 224–227 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Ter Steege, H. et al. Continental-scale patterns of canopy tree composition and function across Amazonia. Nature 443, 444–447 (2006).Article 
    ADS 

    Google Scholar 
    Hedin, L. O., Brookshire, E. N. J., Menge, D. N. L. & Barron, A. R. The nitrogen paradox in tropical forest ecosystems. Annu. Rev. Ecol. Evol. Syst. 40, 613–635 (2009).Article 

    Google Scholar 
    Menge, D. N. L. et al. Patterns of nitrogen-fixing tree abundance in forests across Asia and America. J. Ecol. 107, 2598–2610 (2019).Article 
    CAS 

    Google Scholar 
    Matson, W. J.Jr Herbivory in relation to plant nitrogen content. Annu. Rev. Ecol. Syst. 11, 119–161 (1980).Article 

    Google Scholar 
    Coley, P. D., Bateman, M. L. & Kusar, T. A. The effects of plant quality on caterpillar growth and defense against natural enemies. Oikos 115, 219–228 (2006).Article 

    Google Scholar 
    Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Barron, A. R., Purves, D. W. & Hedin, L. O. Facultative nitrogen fixation by canopy legumes in a lowland tropical forest. Oecologia 165, 511–520 (2011).Article 
    ADS 

    Google Scholar 
    McCulloch, L. A. & Porder, S. Light fuels while nitrogen suppresses symbiotic nitrogen fixation hotspots in neotropical canopy gap seedlings. New Phytol. 231, 1734–1745 (2021).Article 
    CAS 

    Google Scholar 
    Brookshire, E. N. J. et al. Symbiotic N fixation is sufficient to support net aboveground biomass accumulation in a humid tropical forest. Sci Rep. 9, 7571 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Gei, M. et al. Legume abundance along successional and rainfall gradients in Neotropical forests. Nat. Ecol. Evol. 2, 1104–1111 (2018).Article 

    Google Scholar 
    Vance, C. P. in Nitrogen-fixing Leguminous Symbioses. Nitrogen Fixation: Origins, Applications, and Research Progress, Vol. 7 (eds Dilworth, M. J. et al.) (Springer, 2008).Vitousek, P. M. & Howarth, R. W. Nitrogen limitation on land and in the sea: how can it occur? Biogeochemistry 13, 87–115 (1991).Article 

    Google Scholar 
    Menge, D. N. L., Levin, S. A. & Hedin, L. O. Evolutionary tradeoffs can select against nitrogen fixation and thereby maintain nitrogen limitation. Proc. Natl Acad. Sci. USA 105, 1573–1578 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Sheffer, E., Batterman, S. A., Levin, S. A. & Hedin, L. O. Biome-scale nitrogen fixation strategies selected by climatic constraints on nitrogen cycle. Nat. Plants 1, 15182 (2015).Article 
    CAS 

    Google Scholar 
    Vitousek, P. M. & Field, C. B. Ecosystem constraints to symbiotic nitrogen fixers: a simple model and its implications. Biogeochemistry 46, 179–202 (1999).Article 
    CAS 

    Google Scholar 
    Coley, P. D. & Barone, J. A. Herbivory and plant defenses in tropical forests. Annu. Rev. Ecol. Syst. 27, 305–335 (1996).Article 

    Google Scholar 
    Fyllas, N. M. et al. Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. Biogeosciences 6, 2677–2708 (2009).Article 
    ADS 

    Google Scholar 
    Batterman, S. A. et al. Phosphatase activity and nitrogen fixation reflect species differences, not nutrient trading or nutrient balance, across tropical rainforest trees. Ecol. Lett. 21, 1486–1495 (2018).Article 

    Google Scholar 
    Menge, D. N. L., Wolf, A. A. & Funk, J. L. Diversity of nitrogen fixation strategies in Mediterranean legumes. Nat. Plants 1, 15064 (2015).Article 
    CAS 

    Google Scholar 
    Ritchie, M. E. & Tilman, D. Responses of legumes to herbivores and nutrients during succession on a nitrogen-poor soil. Ecol. Soc. Am. 76, 2648–2655 (1995).
    Google Scholar 
    Taylor, B. N. & Ostrowsky, L. R. Nitrogen-fixing and non-fixing trees differ in leaf chemistry and defence but not herbivory in a lowland Costa Rican rain forest. J. Trop. Ecol. 35, 270–279 (2019).Article 

    Google Scholar 
    Endara, M.-J. et al. Coevolutionary arms race versus host defense chase in a tropical herbivore–plant system. Proc. Natl Acad. Sci. USA 114, E7499–E7505 (2017).Article 
    CAS 

    Google Scholar 
    Kursar, T. A. & Coley, P. D. Convergence in defense syndromes of young leaves in tropical rainforests. Biochem. Syst. Ecol. 31, 929–949 (2003).Article 
    CAS 

    Google Scholar 
    Kursar, T. A. et al. The evolution of antiherbivore defenses and their contribution to species coexistence in the tropical tree genus Inga. Proc. Natl Acad. Sci. USA 106, 18073–18078 (2009).Article 
    ADS 
    CAS 

    Google Scholar 
    Taylor, B. N. & Menge, D. N. L. Light regulates tropical symbiotic nitrogen fixation more strongly than soil nitrogen. Nat. Plants 4, 655–661 (2018).Article 
    CAS 

    Google Scholar 
    Adams, M., Turnbull, T., Sprent, J. & Buchmann, N. Legumes are different: leaf nitrogen, photosynthesis, and water use efficiency. Proc. Natl Acad. Sci. USA 113, 4098–4103 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Coley, P. D. Effects of plant growth rate and leaf lifetime on the amount and type of anti-herbivore defense. Oecologia 74, 531–536 (1988).Article 
    ADS 
    CAS 

    Google Scholar 
    Batterman, S. A., Wurzburger, N. & Hedin, L. O. Nitrogen and phosphorus interact to control tropical symbiotic N2 fixation: a test in Inga punctata. J. Ecol. 101, 1400–1408 (2013).Article 
    CAS 

    Google Scholar 
    Eichhorn, M. P., Nilus, R., Compton, S. G., Hartley, S. E. & Burslem, D. F. R. P. Herbivory of tropical rain forest tree seedlings correlates with future mortality. Ecology 91, 1092–1101 (2010).Article 

    Google Scholar 
    Wink, M. Evolution of secondary metabolites in legumes (Fabaceae). South African J. Bot. 89, 164–175 (2013).Article 
    CAS 

    Google Scholar 
    Currano, E. D. & Jacobs, B. F. Bug-bitten leaves from the early Miocene of Ethiopia elucidate the impacts of plant nutrient concentrations and climate on insect herbivore communities. Glob. Planet. Change 207, 103655 (2021).Article 

    Google Scholar 
    Wieder, W. R., Cleveland, C. C., Lawrence, D. M. & Bonan, G. B. Effects of model structural uncertainty on carbon cycle projections: biological nitrogen fixation as a case study. Environ. Res. Lett. 10, 044016 (2015).Article 
    ADS 

    Google Scholar 
    Sprent, J. I. Legume Nodulation: A Global Perspective (John Wiley, 2009).Leigh, E. G. Jr Tropical Forest Ecology: A View from Barro Colorado Island (Oxford Univ. Press, 1999).Comita, L. S., Muller-Landau, H. C., Aguilar, S. & Hubbell, S. P. Asymmetric density dependence shapes species abundances in a tropical tree community. Science 329, 330–332 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Queenborough, S. A., Metz, M. R., Valencia, R. & Wright, S. J. Demographic consequences of chromatic leaf defence in tropical tree communities: do red young leaves increase growth and survival? Ann. Bot. 112, 677–684 (2013).Article 

    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671 (2012).Article 
    CAS 

    Google Scholar 
    Pasquini, S. C. & Santiago, L. S. Nutrients limit photosynthesis in seedlings of a lowland tropical forest tree species. Oecologia 168, 311–319 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Collalti, A. & Prentice, I. C. Is NPP proportional to GPP? Waring’s hypothesis 20 years on. Tree Physiol. 39, 1473–1483 (2019).Article 
    CAS 

    Google Scholar 
    Westbrook, J. W. et al. What makes a leaf tough? Patterns of correlated evolution between leaf toughness traits and demographic rates among 197 shade-tolerant woody species in a Neotropical forest. Am. Nat. 177, 800–811 (2011).Article 

    Google Scholar 
    Wright, S. J. et al. Functional traits and the growth–mortality trade‐off in tropical trees. Ecology 91, 3664–3674 (2010).Article 

    Google Scholar 
    Kitajima, K. et al. How cellulose-based leaf toughness and lamina density contribute to long leaf lifespans of shade-tolerant species. New Phytol. 195, 640–652 (2012).Article 

    Google Scholar 
    Kitajima, K., Wright, S. J. & Westbrook, J. W. Leaf cellulose density as the key determinant of inter- and intra-specific variation in leaf fracture toughness in a species-rich tropical forest. Interface Focus https://doi.org/10.1098/rsfs.2015.0100 (2016).Sedio, B. E., Echeverri, J. C. R., Boya, C. A. & Wright, S. J. Sources of variation in foliar secondary chemistry in a tropical forest tree community. Ecology 98, 616–623 (2017).Article 

    Google Scholar 
    Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

    Google Scholar 
    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 
    Murphy, S. J., Xu, K. & Comita, L. S. Tree seedling richness, but not neighborhood composition, influences insect herbivory in a temperate deciduous forest community. Ecol. Evol. 6, 6310–6319 (2016).Article 

    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. Preprint at https://arxiv.org/abs/1406.5823 (2014).Moles, A. T. & Westoby, M. Do small leaves expand faster than large leaves, and do shorter expansion times reduce herbivore damage? Oikos 90, 517–524 (2000).Article 

    Google Scholar 
    Bürkner, P. C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. https://doi.org/10.18637/jss.v080.i01 (2017). More