Philippa Kaur

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

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

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

Tölgyesi, C., Buisson, E., Helm, A., Temperton, V. M. & Török, P. Urgent need for updating a slogan of global climate actions from ‘tree planting’ to ‘restore native vegetation’. Restor. Ecol. 30, e13594. https://doi.org/10.1111/rec.13594 (2021).Article 

Google Scholar 
Dengler, J., Janišová, M., Török, P. & Wellstein, C. Biodiversity of Palaearctic grasslands: A synthesis. Agric. Ecosyst. Environ. 182, 1–14 (2014).Article 

Google Scholar 
Dass, P., Houlton, B. Z., Wang, Y. & Warlind, D. Grasslands may be more reliable carbon sinks than forests in California. Environ. Res. Lett. 13, 074027. https://doi.org/10.1088/1748-9326/aacb39 (2018).Article 
ADS 

Google Scholar 
Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 (2021).Article 
ADS 
CAS 

Google Scholar 
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).Article 
ADS 
CAS 

Google Scholar 
Stevens, C. J. Recent advances in understanding grasslands. F1000 Res. https://doi.org/10.12688/f1000research.15050.1 (2018).Article 

Google Scholar 
Klaus, V. H. et al. Do biodiversity-ecosystem functioning experiments inform stakeholders how to simultaneously conserve biodiversity and increase ecosystem service provisioning in grasslands?. Biol. Conserv. 245, 108552. https://doi.org/10.1016/j.biocon.2020.108552 (2020).Article 

Google Scholar 
Dudley, N. et al. Grasslands and savannahs in the UN decade on ecosystem restoration. Restor. Ecol. 28, 1313–1317 (2020).Article 

Google Scholar 
Bardgett, R. D. et al. Combatting global grassland degradation. Nat. Rev. Earth Environ. 2, 720–735 (2021).Article 
ADS 

Google Scholar 
Lengyel, S. et al. Restoration for variability: Emergence of the habitat diversity paradigm in terrestrial ecosystem restoration. Restor. Ecol. 28, 1087–1099 (2020).Article 

Google Scholar 
Waldén, E. & Lindborg, R. Long term positive effect of grassland restoration on plant diversity: Success or not?. PLoS ONE 11, e0155836. https://doi.org/10.1371/journal.pone.0155836 (2016).Article 
CAS 

Google Scholar 
Lengyel, S. et al. Grassland restoration to conserve landscape-level biodiversity: A synthesis of early results from a large-scale project. Appl. Veg. Sci. 15, 264–276 (2012).Article 

Google Scholar 
Sojneková, M. & Chytrý, M. From arable land to species-rich semi-natural grasslands: Succession in abandoned fields in a dry region of central Europe. Ecol. Eng. 77, 373–381 (2015).Article 

Google Scholar 
Ellis, E. C. et al. Used planet: A global history. Proc. Natl. Acad. Sci. USA 110, 7978–7985 (2013).Article 
ADS 
CAS 

Google Scholar 
Levers, C., Schneider, M., Prishchepov, A. V., Estel, S. & Kuemmerle, T. Spatial variation in determinants of agricultural land abandonment in Europe. Sci. Total Environ. 644, 95–111 (2018).Article 
ADS 
CAS 

Google Scholar 
Winkler, K., Fuchs, R., Rounsevell, M. & Herold, M. Global land use changes are four times greater than previously estimated. Nat. Commun. 12, 2501. https://doi.org/10.1038/s41467-021-22702-2 (2021).Article 
ADS 
CAS 

Google Scholar 
Perpiña Castillo, C. et al. Agricultural Land Abandonment in the EU within 2015–2030 (No: JRC113718) (Joint Research Centre (Seville site), 2018).Müller, D., Leitão, P. J. & Sikor, T. Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees. Agric. Syst. 117, 66–77 (2013).Article 

Google Scholar 
Prishchepov, A. V., Müller, D., Dubinin, M., Baumann, M. & Radeloff, V. C. Determinants of agricultural land abandonment in post-Soviet European Russia. Land Use Policy 30, 873–884 (2013).Article 

Google Scholar 
Prishchepov, A. V., Schierhorn, F. & Löw, F. Unraveling the diversity of trajectories and drivers of global agricultural land abandonment. Land 10, 97 (2021).Article 

Google Scholar 
Bossuyt, B. & Honnay, O. Can the seed bank be used for ecological restoration? An overview of seed bank characteristics in European communities. J. Veg. Sci. 19, 875–884 (2008).Article 

Google Scholar 
Humphries, T., Florentine, S., Dowling, K., Turville, C. & Sinclair, S. Weed management for landscape scale restoration of global temperate grasslands. Land Degrad. Dev. 32, 1090–1102 (2021).Article 

Google Scholar 
Valkó, O. et al. Dynamics in vegetation and seed bank composition highlight the importance of post-restoration management in sown grasslands. Restor. Ecol. 29, e13192. https://doi.org/10.1111/rec.13192 (2021).Article 

Google Scholar 
Valkó, O. et al. High-diversity sowing in establishment gaps: A promising new tool for enhancing grassland biodiversity. Tuexenia 36, 359–378 (2016).
Google Scholar 
Kövendi-Jakó, A. et al. Three years of vegetation development worth 30 years of secondary succession in urban-industrial grassland restoration. Appl. Veg. Sci. 22, 138–149 (2019).Article 

Google Scholar 
Kiss, R. et al. Establishment gaps in species-poor grasslands: Artificial biodiversity hotspots to support the colonization of target species. Restor. Ecol. 29, e13135. https://doi.org/10.1111/rec.13135 (2021).Article 

Google Scholar 
Török, P., Vida, E., Deák, B., Lengyel, S. & Tóthmérész, B. Grassland restoration on former croplands in Europe: An assessment of applicability of techniques and costs. Biodivers. Conserv. 20, 2311–2332 (2011).Article 

Google Scholar 
Critchley, C. N. R., Fowbert, J. A., Sherwood, A. J. & Pywell, R. F. Vegetation development of sown grass margins in arable fields under a countrywide agri-environment scheme. Biol. Conserv. 132, 1–11 (2006).Article 

Google Scholar 
Wagner, M., Walker, K. J. & Pywell, R. F. Seed bank dynamics in restored grassland following the sowing of high-and low-diversity seed mixtures. Restor. Ecol. 26, S189–S199 (2018).Article 

Google Scholar 
Lepš, J. et al. Long-term effectiveness of sowing high and low diversity seed mixtures to enhance plant community development on ex-arable fields. Appl. Veg. Sci. 10, 97–110 (2007).
Google Scholar 
Török, P. et al. Restoring grassland biodiversity: Sowing low diversity seed mixtures can lead to rapid favourable changes. Biol. Conserv. 148, 806–812 (2010).Article 

Google Scholar 
Schaub, S. et al. The costs of diversity: Higher prices for more diverse grassland seed mixtures. Environ. Res. Lett. 16, 094011. https://doi.org/10.1088/1748-9326/ac1a9c (2021).Article 
ADS 
CAS 

Google Scholar 
Werner, C. M., Vaughn, K. J., Stuble, K. L., Wolf, K. & Young, T. P. Persistent asymmetrical priority effects in a California grassland restoration experiment. Ecol. Appl. 26, 1624–1632 (2016).Article 

Google Scholar 
Williams, D. W., Jackson, L. L. & Smith, D. D. Effects of frequent mowing on survival and persistence of forbs seeded into a species-poor grassland. Restor. Ecol. 15, 24–33 (2007).Article 

Google Scholar 
Klaus, V. H. et al. Enriching plant diversity in grasslands by large-scale experimental sward disturbance and seed addition along gradients of land-use intensity. J. Plant Ecol. 10, 581–591 (2017).
Google Scholar 
Kiss, R. et al. Zoochory on and off: A field experiment for trait-based analysis of establishment success of grassland species. J. Veg. Sci. 32, e13051. https://doi.org/10.1111/jvs.13051 (2021).Article 

Google Scholar 
Weidlich, E. W. A. et al. Priority effects and ecological restoration. Restor. Ecol. 29, e13317. https://doi.org/10.1111/rec.13317 (2021).Article 

Google Scholar 
Wilsey, B. Restoration in the face of changing climate: Importance of persistence, priority effects, and species diversity. Restor. Ecol. 29, e13132. https://doi.org/10.1111/rec.13132 (2021).Article 

Google Scholar 
von Gillhaussen, P. et al. Priority effects of time of arrival of plant functional groups override sowing interval or density effects: A grassland experiment. PLoS ONE 9, e86906. https://doi.org/10.1371/journal.pone.0086906 (2014).Article 
ADS 
CAS 

Google Scholar 
Eddy, K. C. & Van Auken, O. W. Priority effects allow Coreopsis tinctoria to avoid interspecific competition with a C4 grass. Am. Midl. Nat. 181, 104–114 (2019).Article 

Google Scholar 
Delory, B. M., Weidlich, E. W., von Gillhaussen, P. & Temperton, V. M. When history matters: The overlooked role of priority effects in grassland overyielding. Funct. Ecol. 33, 2369–2380 (2019).Article 

Google Scholar 
Fenner, M. The effects of the parent environment on seed germinability. Seed Sci. Res. 1, 75–84 (1991).Article 

Google Scholar 
Ruprecht, E., Donath, T. W., Otte, A. & Eckstein, R. L. Chemical effects of a dominant grass on seed germination of four familial pairs of dry grassland species. Seed Sci. Res. 18, 239–248 (2008).Article 

Google Scholar 
Partzsch, M., Faulhaber, M. & Meier, T. The effect of the dominant grass Festuca rupicola on the establishment of rare forbs in semi-dry grasslands. Folia Geobot. 53, 103–113 (2018).Article 

Google Scholar 
Fenesi, A., Kelemen, K., Sándor, D. & Ruprecht, E. Influential neighbours: Seeds of dominant species affect the germination of common grassland species. J. Veg. Sci. 31, 1028–1038 (2020).Article 

Google Scholar 
Garbowski, M. et al. Getting to the root of restoration: Considering root traits for improved restoration outcomes under drought and competition. Restor. Ecol. 28, 1384–1395 (2020).Article 

Google Scholar 
Rehling, F., Sandner, T. M. & Matthies, D. Biomass partitioning in response to intraspecific competition depends on nutrients and species characteristics: A study of 43 plant species. J. Ecol. 109, 2219–2233 (2021).Article 

Google Scholar 
Gross, K. L. & Mittelbach, G. G. Negative effects of fertilization on grassland species richness are stronger when tall clonal species are present. Folia Geobot. 52, 401–409 (2017).Article 

Google Scholar 
Bakker, J. P. & Berendse, F. Constraints in the restoration of ecological diversity in grassland and heathland communities. Trends Ecol. Evol. 14, 63–68 (1999).Article 
CAS 

Google Scholar 
Kiss, R., Valkó, O., Tóthmérész, B. & Török, P. Seed bank research in Central-European grasslands: An overview. In Seed Banks: Types Roles and Research (ed. Murphy, J.) 1–34 (Nova Science Publishers, 2016).
Google Scholar 
Prach, K., Jongepierová, I. & Řehounková, K. Large-scale restoration of dry grasslands on ex-arable land using a regional seed mixture: Establishment of target species. Restor. Ecol. 21, 33–39 (2013).Article 

Google Scholar 
Adler, P. B. et al. Competition and coexistence in plant communities: intraspecific competition is stronger than interspecific competition. Ecol. Lett. 21, 1319–1329 (2018).Article 

Google Scholar 
Baskin, C. C. & Baskin, J. M. Seeds: Ecology, Biogeography, And Evolution of Dormancy and Germination (Academic Press, 1998).
Google Scholar 
Kövendi-Jakó, A. et al. Effect of seed storing duration and sowing year on the seedling establishment of grassland species in xeric environments. Restor. Ecol. 29, e13209. https://doi.org/10.1111/rec.13209 (2020).Article 

Google Scholar 
Cevallos, D., Szitár, K., Halassy, M., Kövendi-Jakó, A. & Török, K. Larger seed mass predicts higher germination and emergence rates in sand grassland species with non-dormant seeds. Acta Bot. Hung. 64, 237–258 (2022).Article 

Google Scholar 
Leishman, M. R., Wright, I. J., Moles, A. T. & Westoby, M. The evolutionary ecology of seed size. In Seeds: The Ecology of Regeneration in Plant Communities (ed. Fenner, M.) 31–57 (CAB International, 2000).Chapter 

Google Scholar 
Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: Some leading dimensions of variation between species. Annu. Rev. Ecol. Evol. Syst. 33, 125–215 (2002).Article 

Google Scholar 
Moles, A. T. & Westoby, M. Seed size and plant strategy across the whole life cycle. Oikos 113, 91–105 (2006).Article 

Google Scholar 
Scotton, M. Seed production in grassland species: Morpho-biological determinants in a species-rich semi-natural grassland. Grass Forage Sci. 73, 764–776 (2018).Article 

Google Scholar 
Thompson, K., Bakker, J. P. & Bekker, R. M. The Soil Seed Banks of North West Europe: Methodology, Density and Longevity (Cambridge University Press, 1997).
Google Scholar 
Fick, S. E. & Hijmans, R. J. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
ENSCONET (European Native Seed Conservation Network). ENSCONET Seed Collecting Manual for Wild Species. ENSCONET, Royal Botanic Gardens, Kew and Universidad Politécnica de Madrid (2009). http://www.kew.org/sites/default/files/ENSCONET_Collecting_protocol_English.pdf. Accessed 15 April 2014).Borhidi, A. Social behaviour types, the naturalness and relative indicator values of the higher plants in the Hungarian flora. Acta Bot. Hung. 39, 97–181 (1995).
Google Scholar 
Király, G. (ed). Új magyar füvészkönyv. Magyarország hatásos növényei (New Hungarian Herbal. The Vascular Plants of Hungary. Identification Key) [in Hungarian]. (Aggtelek National Park Directorate, 2009).R Core Team. R: A Language and Environment for Statistical Computing (4.0.5). Computer Software. R Foundation for Statistical Computing. https://www.R-project.org (2021).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 
Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means (Version 1.3.4) [R]. https://CRAN.R-project.org/package=emmeans (2019). More

Field parameters and major ionsThe results of the hydrochemistry and environmental isotopes for the 40 lakes are presented spatially in Figs. S1–S11 and are located in Tables S1 and S2.The lake waters are oxic (8.6–12.6 mg l−1) and range from slightly acidic (pH 6.0) to slightly alkaline (pH 9.2). Lake water temperatures are generally highest for lakes along the west coast (greater than 10 °C, Table S2). Phosphate concentrations are below detection level (0.1 mg l−1) for all lakes and nitrate was low ranging from below detection limit ( More

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

Daszak, P. Emerging infectious diseases of wildlife-threats to biodiversity and human health. Science 287, 443–449 (2000).Article 
ADS 
CAS 

Google Scholar 
Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).Article 
ADS 
CAS 

Google Scholar 
Altizer, S., Ostfeld, R. S., Johnson, P. T. J., Kutz, S. & Harvell, C. D. Climate change and infectious diseases: From evidence to a predictive framework. Science 1979(341), 514–519 (2013).Article 
ADS 

Google Scholar 
Kilpatrick, A. M., Briggs, C. J. & Daszak, P. The ecology and impact of chytridiomycosis: An emerging disease of amphibians. Trends Ecol. Evol. 25, 109–118 (2010).Article 

Google Scholar 
Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 1979(323), 227–227 (2009).Article 

Google Scholar 
Wilfert, L. et al. Deformed wing virus is a recent global epidemic in honeybees driven by Varroa mites. Science 1979(351), 594–597 (2016).Article 
ADS 

Google Scholar 
Garamszegi, L. Z. Climate change increases the risk of malaria in birds. Glob. Change Biol. 17, 1751–1759 (2011).Article 
ADS 

Google Scholar 
Zamora-Vilchis, I., Williams, S. E. & Johnson, C. N. Environmental temperature affects prevalence of blood parasites of birds on an elevation gradient: Implications for disease in a warming climate. PLoS ONE 7, e39208 (2012).Article 
ADS 
CAS 

Google Scholar 
Harvell, D., Altizer, S., Cattadori, I. M., Harrington, L. & Weil, E. Climate change and wildlife diseases: When does the host matter the most?. Ecology 90, 912–920 (2009).Article 

Google Scholar 
Burge, C. A. et al. Climate change influences on marine infectious diseases: Implications for management and society. Ann. Rev. Mar. Sci. 6, 249–277 (2014).Article 

Google Scholar 
Tracy, A. M., Pielmeier, M. L., Yoshioka, R. M., Heron, S. F. & Harvell, C. D. Increases and decreases in marine disease reports in an era of global change. Proc. R. Soc. B Biol. Sci. 286, 20191718 (2019).Article 

Google Scholar 
Lejeusne, C., Chevaldonné, P., Pergent-Martini, C., Boudouresque, C. F. & Pérez, T. Climate change effects on a miniature ocean: The highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25, 250–260 (2010).Article 

Google Scholar 
Basso, L. et al. The Pen Shell, Pinna nobilis: A review of population status and recommended research priorities in the Mediterranean Sea. Adv. Mar. Biol. 71, 109–160 (2015).Article 

Google Scholar 
Catanese, G. et al. Haplosporidium pinnae sp. nov., a haplosporidan parasite associated with mass mortalities of the fan mussel, Pinna nobilis, in the Western Mediterranean Sea. J. Invertebr. Pathol. 157, 9–24 (2018).Article 
CAS 

Google Scholar 
Vázquez-Luis, M. et al. S.O.S. Pinna nobilis: A mass mortality event in western Mediterranean sea. Front. Mar. Sci. 4, 220 (2017).Article 

Google Scholar 
García-March, J. R. et al. Can we save a marine species affected by a highly infective, highly lethal, waterborne disease from extinction?. Biol. Conserv. 243, 108498 (2020).Article 

Google Scholar 
Prado, P. et al. Pinna nobilis in suboptimal environments are more tolerant to disease but more vulnerable to severe weather phenomena. Mar. Environ. Res. 163, 105220 (2021).Article 
CAS 

Google Scholar 
Cabanellas-Reboredo, M. et al. Tracking a mass mortality outbreak of pen shell Pinna nobilis populations: A collaborative effort of scientists and citizens. Sci. Rep. 9, 13355 (2019).Article 
ADS 

Google Scholar 
Kersting, D. K. et al. Recruitment disruption and the role of unaffected populations for potential recovery after the Pinna nobilis mass mortality event. Front. Mar. Sci. 7, 1–11 (2020).Article 
ADS 

Google Scholar 
Box, A. et al. Reduced antioxidant response of the fan mussel Pinna nobilis related to the presence of haplosporidium pinnae. Pathogens 9, 1–14 (2020).Article 

Google Scholar 
Peyran, C., Morage, T., Nebot-Colomer, E., Iwankow, G. & Planes, S. Unexpected residual habitats raise hope for the survival of the fan mussel Pinna nobilis along the Occitan coast (Northwest Mediterranean Sea). Endanger Species Res. 48, 123–137 (2022).Article 

Google Scholar 
Rosa, R. D. et al. A hemocyte gene expression signature correlated with predictive capacity of oysters to survive Vibrio infections. BMC Genomics 13, 1–12 (2012).Article 

Google Scholar 
van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002).Article 

Google Scholar 
Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness. In Methods in Molecular Biology 227–245 https://doi.org/10.1007/978-1-4939-9173-0_14 (2019).Smith-Unna, R., Boursnell, C., Patro, R., Hibberd, J. M. & Kelly, S. TransRate: Reference-free quality assessment of de novo transcriptome assemblies. Genome Res. 26, 1134–1144 (2016).Article 
CAS 

Google Scholar 
Guo, X. & Ford, S. E. Infectious diseases of marine mollusks and host responses as revealed by genomic tools. Philos. Trans. R. Soc. B Biol. Sci. https://doi.org/10.1098/rstb.2015.0206 (2016).Article 

Google Scholar 
Pauletto, M. et al. Deep transcriptome sequencing of Pecten maximus hemocytes: A genomic resource for bivalve immunology. Fish Shellfish Immunol. 37, 154–165 (2014).Article 
CAS 

Google Scholar 
Caurcel, C. et al. MolluscDB: A genome and transcriptome database for molluscs. Philos. Trans. R. Soc. Lond. B Biol. Sci. 376, 20200157 (2021).Article 
CAS 

Google Scholar 
de Oliveira, A. L. et al. Comparative transcriptomics enlarges the toolkit of known developmental genes in mollusks. BMC Genomics 17, 1–23 (2016).Article 

Google Scholar 
Richardson, M. F. & De Sherman, C. D. H. De novo assembly and characterization of the invasive Northern Pacific Seastar transcriptome. PLoS ONE 10, e0142003 (2015).Article 

Google Scholar 
Zhang, D., Wang, F., Dong, S. & Lu, Y. D. De novo assembly and transcriptome analysis of osmoregulation in Litopenaeus vannamei under three cultivated conditions with different salinities. Gene 578, 185–193 (2016).Article 
CAS 

Google Scholar 
Werner, G. D. A., Gemmell, P., Grosser, S., Hamer, R. & Shimeld, S. M. Analysis of a deep transcriptome from the mantle tissue of Patella vulgata Linnaeus (Mollusca: Gastropoda: Patellidae) reveals candidate biomineralising genes. Mar. Biotechnol. 15, 230–243 (2013).Article 
CAS 

Google Scholar 
Ding, J. et al. Transcriptome sequencing and characterization of Japanese scallop Patinopecten yessoensis from different shell color lines. PLoS ONE 10, e0116406 (2015).Article 

Google Scholar 
Harney, E. et al. De novo assembly and annotation of the European abalone Haliotis tuberculata transcriptome. Mar Genomics 28, 11–16 (2016).Article 

Google Scholar 
Khalturin, K., Hemmrich, G., Fraune, S., Augustin, R. & Bosch, T. C. G. More than just orphans: Are taxonomically-restricted genes important in evolution?. Trends Genet. 25, 404–413. https://doi.org/10.1016/j.tig.2009.07.006 (2009).Article 
CAS 

Google Scholar 
Gibson, A. K., Smith, Z., Fuqua, C., Clay, K. & Colbourne, J. K. Why so many unknown genes? Partitioning orphans from a representative transcriptome of the lone star tick Amblyomma americanum. BMC Genomics 14, 135 (2013).Article 
CAS 

Google Scholar 
Albertin, C. B. et al. The octopus genome and the evolution of cephalopod neural and morphological novelties. Nature 524, 220–224 (2015).Article 
ADS 
CAS 

Google Scholar 
Vogeler, S., Galloway, T. S., Lyons, B. P. & Bean, T. P. The nuclear receptor gene family in the Pacific oyster, Crassostrea gigas, contains a novel subfamily group. BMC Genomics 15, 369 (2014).Article 

Google Scholar 
Allam, B. & Raftos, D. Immune responses to infectious diseases in bivalves. J. Invertebr. Pathol. 131, 121–136. https://doi.org/10.1016/j.jip.2015.05.005 (2015).Article 
CAS 

Google Scholar 
Allam, B. & Pales Espinosa, E. Bivalve immunity and response to infections: Are we looking at the right place?. Fish Shellfish Immunol. 53, 4–12. https://doi.org/10.1016/j.fsi.2016.03.037 (2016).Article 
CAS 

Google Scholar 
Qiu, L., Song, L., Xu, W., Ni, D. & Yu, Y. Molecular cloning and expression of a Toll receptor gene homologue from Zhikong Scallop, Chlamys farreri. Fish Shellfish Immunol. 22, 451–466 (2007).Article 
CAS 

Google Scholar 
Zhang, L., Li, L., Zhu, Y., Zhang, G. & Guo, X. Transcriptome analysis reveals a rich gene set related to innate immunity in the eastern oyster (Crassostrea virginica). Mar. Biotechnol. 16, 17–33 (2014).Article 

Google Scholar 
Moreira, R. et al. Transcriptomics of in vitro immune-stimulated hemocytes from the Manila clam Ruditapes philippinarum using high-throughput sequencing. PLoS ONE 7, e35009 (2012).Article 
ADS 
CAS 

Google Scholar 
Toubiana, M. et al. Toll-like receptors and MyD88 adaptors in Mytilus: Complete cds and gene expression levels. Dev. Comp. Immunol. 40, 158–166 (2013).Article 
CAS 

Google Scholar 
He, Y. et al. Transcriptome analysis reveals strong and complex antiviral response in a mollusc. Fish Shellfish Immunol. 46, 131–144 (2015).Article 
CAS 

Google Scholar 
Zhang, L. et al. Massive expansion and functional divergence of innate immune genes in a protostome. Sci. Rep. 5, 8693 (2015).Article 
CAS 

Google Scholar 
Casadevall, A. & Pirofski, L. A. Host–pathogen interactions: The attributes of virulence. J. Infect. Dis. 184, 337–344. https://doi.org/10.1086/322044 (2001).Article 
CAS 

Google Scholar 
Jones, B., Pascopella, L. & Falkow, S. Entry of microbes into the host: Using M cells to break the mucosal barrier. Curr. Opin. Immunol. 7, 474–478 (1995).Article 
CAS 

Google Scholar 
Liévin-Le Moal, V. & Servin, A. L. The front line of enteric host defense against unwelcome intrusion of harmful microorganisms: Mucins, antimicrobial peptides, and microbiota. Clin. Microbiol. Rev. 19, 315–337. https://doi.org/10.1128/CMR.19.2.315-337.2006 (2006).Article 
CAS 

Google Scholar 
Trigos, S., Vicente, N., Prado, P. & Espinós, F. J. Adult spawning and early larval development of the endangered bivalve Pinna nobilis. Aquaculture 483, 102–110 (2018).Article 

Google Scholar 
Vázquez-Luis, M., Nebot-Colomer, E., Deudero, S., Planes, S. & Boissin, E. Natural hybridization between pen shell species: Pinna rudis and the critically endangered Pinna nobilis may explain parasite resistance in P. nobilis. Mol. Biol. Rep. https://doi.org/10.1007/s11033-020-06063-5 (2021).Article 

Google Scholar 
Katsares, V., Tsiora, A., Galinou-Mitsoudi, S. & Imsiridou, A. Genetic structure of the endangered species Pinna nobilis (Mollusca: Bivalvia) inferred from mtDNA sequences. Biologia 63, 412–417 (2008).Article 
CAS 

Google Scholar 
Gonzalez-Wanguemert, M. et al. Highly polymorphic microsatellite markers for the Mediterranean endemic fan mussel Pinna nobilis. Mediterr. Mar. Sci. 16, 31 (2014).Article 

Google Scholar 
Peyran, C., Planes, S., Tolou, N., Iwankow, G. & Boissin, E. Development of 26 highly polymorphic microsatellite markers for the highly endangered fan mussel Pinna nobilis and cross-species amplification. Mol. Biol. Rep. 47, 2551–2559 (2020).Article 
CAS 

Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).Article 
CAS 

Google Scholar  More

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