Ecology

Residual HMs in the flushed soil and their mobilityOne of the aims of soil remediation is a permanent and substantial reduction in the amount, toxicity or mobility of pollutants. In this study, many factors affected HM removal, such as the type of WA, the flow rate of the WA and the type of HM. In general, the residual HM contents in soil flushed at a flow rate of 1.0 ml/min. were significantly lower than those in soil flushed at 0.5 ml/min (p  More

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In June 2018, 180 cars fanned out across Denmark and parts of Germany on a grand insect hunt. Armed with white, funnel-shaped nets mounted on their car roofs, enthusiastic citizen naturalists roamed through cities, farmlands, grasslands, wetlands and forests. The drivers sent the haul from their ‘InsectMobiles’ to scientists at the National History Museum of Denmark in Copenhagen and the German Centre for Integrative Biodiversity Research in Leipzig.The researchers dried and weighed the collections to determine the total mass of flying insects in each landscape. They expected some bad news. The previous year, scientists in Germany had found that the flying-insect biomass in their nature reserves had plunged by 76% over 27 years1. Similar studies had led to news headlines that screamed of an ongoing “insectageddon” and “insect apocalypse”. British columnist George Monbiot wrote in The Guardian: “Insectageddon: farming is more catastrophic than climate breakdown”.But when the researchers tallied the InsectMobile results2, they didn’t see evidence of declines everywhere. Insect biomass totals were higher than expected in agricultural fields, and indeed in all places except cities in their study, which is yet to be peer reviewed2. Aletta Bonn, an entomologist at the Leipzig centre and a co-author of the study, says this could be because the fertilizers that farmers use are leading to lush plant life, which is reverberating through the ecosystem. That said, she cautions, not every insect species in the study area might be doing well; some could be thriving, others not so much.“We do need to understand better what kind of insects are affected and to which degree,” Bonn says. “I think the generalization that all agriculture is bad — I wouldn’t say so.”The findings resonate with what biologist Mark Vellend and his colleagues have seen in their studies of trees at the edge of boreal forests in eastern Canada. They’ve found that spruce, eastern white cedar, eastern hemlock and American beech have been struggling to maintain their roothold since European and American settlers began clearing land more than a century ago. But poplar, paper birch, maple and balsam fir are thriving3. Vellend, who teaches at the University of Sherbrooke in Quebec, Canada, poses a question to his students every year: if they were to count the plant species in the province, would the number have gone up or down since Europeans arrived?Most students so far have got it wrong. “Many of them are surprised to learn that there’s 25% more [species] than there were 500 years ago, before people of European origin laid a foot here,” Vellend says.
Humans are driving one million species to extinction
Something odd is going on in biodiversity studies. Scientists have long warned that animal and plant species are disappearing at an alarming rate. In 2019, an international group of hundreds of researchers produced the most comprehensive report on biodiversity ever assembled, and they concluded that some one million animals and plant species are facing extinction. On top of that, humans have cleared landscapes and chopped down nearly one-third of the world’s forests since the Industrial Revolution — all of which bodes poorly for protecting species.So, scientists naturally assumed that they would find precipitous declines in biodiversity nearly everywhere they looked. But they haven’t. And a consensus is emerging that, even though species are disappearing globally at alarming rates, scientists cannot always detect the declines at the local level. Some species, populations and ecosystems are indeed crashing, but others are ebbing more slowly, holding steady or even thriving. This is not necessarily good news. In most places, new species are moving in when older ones leave or blink out, changing the character of the communities. And that has important implications, because biodiversity at the small scale has outsize importance; it provides food, fresh water, fuel, pollination and many ecosystem services that humans and other organisms depend on.“Ecosystems don’t work at the global scale,” says Maria Dornelas, an ecologist at the University of St Andrews, UK. “I’m interested in what is happening to biodiversity at the local scale, because that’s the scale that we experience.”Scientists say it’s clear that there’s a biodiversity crisis, but there are many questions about the details. Which species will lose? Will new communities be healthy and desirable? Will the rapidly changing ecosystems be able to deal with climate change? And where should conservation actions be targeted?To find answers, scientists need better data from field sites around the world, collected at regular intervals over long periods of time. Such data don’t exist for much of the world, but scientists are trying to fill the gaps in Europe. They are planning a comprehensive network, called EuropaBON, that will combine research plots, citizen scientists, satellite sensors, models and other methods to generate a continuous stream of biodiversity data for the continent. The effort will inform European policymakers, who are pushing for a strong and verifiable global biodiversity agreement when nations next meet to renew the United Nations’ Convention on Biological Diversity (CBD) — an international pact to halt and reverse biodiversity loss.How to measure biodiversityBiological diversity is a shape-shifting term that has been used in many ways. The CBD takes a broad approach, defining it as “the variability among living organisms from all sources”. This includes, it says, “diversity within species, between species and of ecosystems”.“Everybody could sign up to such a definition,” says Chris Thomas, an ecologist at the University of York, UK. “It means that different people can pick on different aspects that are all included within that all-encompassing definition, and find almost whatever trend they want.”Scientists measure biodiversity through many metrics, but the most common is species richness: a simple count of the number of species in the area. They also check the relative abundance of different organisms — a metric called species evenness — and track the identity of species to learn the ‘community composition’. Further complicating matters, scientists sometimes tally biomass instead of species richness, especially when it comes to insects.Using such measures, the clearest signal that the world is losing biodiversity comes from the bookkeeper of species, the International Union for Conservation of Nature. It has found that 26% of all mammals, 14% of birds and 41% of amphibians are currently threatened globally. Insufficient data are available for other groups, such as most plants and fungi. Extinction rates in the past few centuries are much higher than they had been before humans started to transform the planet; some estimates suggest current rates are 1,000 times the background level. One calculation estimates that, if high rates continue, then within 14,000 years, we could enter the sixth mass extinction — an event similar to the one that wiped out about three-quarters of the planet’s species, including dinosaurs, 65 million years ago4. For the most critically endangered species, the death knell could come within decades.

Lionfish have invaded the Red Sea, one example of species changes seen in many places.Credit: Alexis Rosenfeld/Getty

More bad news comes from the United Nations-backed Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) — the organization behind the 2019 report warning that about one million species were threatened by extinction. The report also found that the abundance of native species in local terrestrial ecosystems has dropped by an average of around 20% as a result of human activities.Another biodiversity report that draws considerable attention comes from the conservation organization WWF and the Zoological Society of London, among other groups. Every year, they produce the Living Planet Index, which has amassed data for 27,695 populations of 4,806 vertebrate species. Last year, the report stated that population sizes of birds, mammals, fish, amphibians and reptiles declined, on average, by 68% between 1970 and 2016.Some researchers worry that such averaged figures can hide a lot of nuance, because many people might assume incorrectly that the average applies to most species. Dornelas likes to illustrate the danger by pointing out that the ‘average human’ has one breast and one testicle, and doesn’t exist.
Why deforestation and extinctions make pandemics more likely
Last year, Brian Leung, a biologist at McGill University in Montreal, and his colleagues re-analysed the Living Planet Index data from 2018 and found that a handful of populations are declining catastrophically, strongly pulling down the average. If these outliers are dropped from the computation, 98.6% of the populations on the index are holding steady or increasing or declining more slowly5. “We’re not saying there are not problems,” says Leung, who stresses that declines are still bad. “But there should be some caution about using these really broad-based global metrics, even though they are pretty powerful statements. But they can mask a whole lot of variation and be driven by extreme outliers.”When scientists talk about the world entering a sixth mass extinction, what sometimes gets lost is the timescale. Extinction rates for past periods of Earth’s history are calculated per one million years, and at present, researchers are seeing vertebrate species disappear at a rate of about 1% every century, and most of that has happened on islands.It’s clear there is a biodiversity crisis right now, although the pace is uncertain, says Henrique Pereira, a conservation biologist at the German Centre for Integrative Biodiversity Research , and a co-chair of an IPBES expert group. “It doesn’t mean that there is no decline. It means that if there is a decline, it’s much smaller than what maybe we thought.”So is the sixth mass extinction happening? “Well, not yet, if you want my scientific assessment of it. But is it going to be starting? Yes, maybe starting,” says Pereira.Difficult messageIn 2012, Vellend and his colleagues decided to see what’s happening with plant biodiversity by looking at a collection of individual field sites around the world. They compiled more than 16,000 studies in which scientists had monitored plants for at least 5 years, and found that only 8% of the studies noted a strong decline in the total number of species. Most plots showed either no change, smaller declines or even an increase in biodiversity6.The study was rejected by Nature, and one reviewer worried that journalists would garble the results and give the false impression that there were no problems with biodiversity. A Nature spokesperson says the peer-review process is confidential and that editorial decisions are not driven by considerations of potential media coverage. (Nature’s news team is editorially independent of its journal team.)

An experiment to trap and identify moth species in the Netherlands.Credit: Edwin Giesbers/Nature Picture Library

Vellend eventually published the study in the Proceedings of the National Academy of Sciences in 20136.His conclusions were soon backed up by Dornelas and her colleague Anne Magurran, an ecologist at the University of St Andrews, who have been compiling a database of biodiversity field studies, called BioTIME, since 2010. The database now has more than 12 million records for about 50,000 species from 600,000 locations around the world.In a study of 100 field sites worldwide, Dornelas and her colleagues had expected to find declines in species richness and abundance, but the data showed otherwise. Many sites were declining in biodiversity, but an equal proportion were improving. And about 20% showed no change over time. Overall, there wasn’t a clear trend7.At first, the researchers didn’t believe the results, so they reanalysed the data several ways and finally published the findings in 2014.“It was this tremendous shock. What’s going on?” says Pereira, who wasn’t involved in the study.Dornelas says reactions were mixed. Some people worried that the results could be misconstrued to suggest that everything’s fine with biodiversity. Others went even further. “Some people questioned our integrity, which is something that I take offence at, because being an ethical scientist is at the core of what I do,” she says. “But other people reached out to us and said, ‘Oh, interesting, that sort of matches my experience.’”Since then, many studies looking at biodiversity in the oceans, rivers, among insects — almost any grouping or biome one can think of — have found that there is no clear trend of decline. But that doesn’t mean the ecosystems are remaining static. Dornelas and her colleagues have continued to mine the BioTime database and have found that the mix of species in local communities is changing rapidly almost everywhere on Earth8 (see ‘Life on the go’). As some inhabitants disappear, colonizers move in and add to species richness, so the ‘average ecosystem’ shows no change or even an increase in the number of species, she says, with her usual cautions about averages9.

Source: Ref. 8

“Species are at the moment playing musical chairs,” says Dornelas.This can be seen most clearly on isolated islands, where 95% of the world’s extinctions have happened. Take New Zealand, where there were no mammalian predators before humans first settled there, less than 800 years ago. Since then, nearly half of New Zealand’s endemic birds have gone extinct.But despite the extinctions, biodiversity, measured by species richness, has improved over time in New Zealand, Vellend says. Continental birds have replaced the lost endemics. Plant biodiversity is doing well; fewer than 10 native species have gone extinct, and there are now 4,000 plant species on the islands, up from 2,000 before human settlers. And there are more than two dozen new land mammals.The lesson is that species richness or abundance figures might not tell the whole story, says Dornelas. Rather, scientists need to know the identity of all the species in a community, and track their relative abundances. This will allow them to learn which species are declining and which could be targeted for conservation.The story is similar on the continents, except with fewer complete extinctions. In Denmark over the past 140 years, 50 plant species have declined in abundance and range, but 236 have expanded their habitats. The large majority are holding steady10. Scientists looking at Europe’s birds since 1980 have found that 175 species are declining while 203 are increasing11. Rare birds are doing better than more common species, such as the house sparrow (Passer domesticus). A study of vertebrates in North America and Europe by Leung and his colleagues found that, whereas amphibians are declining across the board, other taxa have winners and losers in roughly equal measure12.Even corals seems to show the same pattern: between 1981 and 2013, 26 genera in the Caribbean and Indo-Pacific became more abundant, while 31 declined13.With studies piling up, it’s become increasingly acceptable for scientists to say that biodiversity isn’t declining everywhere and for all taxa, says Dan Greenberg, an ecologist at University of California, San Diego. “The tide is turning,” he says, “but the field is grappling with how to translate that to a public audience, or what does that mean in terms of social consequences.”That doesn’t mean there’s no biodiversity crisis, stresses Helmut Hillebrand, an ecologist at the University of Oldenburg in Germany. Some scientists worry that unusually high turnover, together with signals of instability in some populations, could itself portend ecological collapse. Humans are carrying species into new environs, leading to colonization. Whereas climate change is spurring warm-loving species to expand into new zones, cold-adapted species are losing out. Plus, generalist species that are fast-growing and less particular about where they live are thriving in human-modified landscapes.Specialists that need highly specific environments or that disperse poorly get easily isolated, which increases their extinction risk, says Greenberg. Case in point: amphibians. “If something changes in that environment, you can’t really hop over to another site very easily,” he says.
The battle for the soul of biodiversity
Turnover could lead to distant communities that increasingly resemble each other — a process called homogenization that has been documented in particular regions and taxa. In 2015, César Capinha, a biogeographer at the University of Lisbon, and his colleagues found that snail populations in temperate regions as far flung as Virginia, New Zealand and South Africa had species in common, thanks to human travel and trade14. Similarly, in the plant study in Denmark, scientists found that plant communities are increasingly looking like each other and are dominated by generalists. Scientists worry that such landscapes might not be resilient to environmental change.Dornelas urges caution in interpreting the changes seen so far. There hasn’t yet been a robust global study of homogenization to know the extent to which this is happening. And there is also increased habitat fragmentation, which can counter this process. “We don’t often talk about both of those at the same time,” Dornelas says. “I’ve now learned not to assume I know what’s going on until I’ve seen what the data show.”Scientists have also observed cases in which a colonizer mixes with a resident to rapidly form a new hybrid species, especially in plants, says Thomas. But it’s unclear how long these hybrids will persist, and most other groups usually take one million years or so to form new species. Many of the beasts of today could go extinct before that process can catch up, says Dolph Schluter, an evolutionary biologist at the University of British Columbia in Vancouver, Canada. “We are going to lose a lot of the ancients. And no amount of evolution in the short term is going to replace those,” Schluter says.Keeping tabs on lifeGlobal studies of biodiversity have important biases owing to data gaps. Most of the records of species come from Europe and North America; there are very few data from the tropics, where rainforests house half of all species in just 7% of the Earth’s surface. And even in the most richly monitored places on Earth, such as Europe, the data are patchy. “We are trying to read the book, but we have only a few letters,” says Pereira.Pereira and his colleagues are designing a top-down monitoring network in Europe called EuropaBON that can add in more letters, and maybe even sentences. The project has received 3 million (US$3.5 million) from the European Commission, and was launched last December. Pereira and Jessica Junker, the scientific coordinator of EuropaBON and a conservationist at Martin Luther University Halle-Wittenberg in Germany, have assembled a 350-strong community of national conservation authorities, non-governmental organizations, scientists and government officials. Among the first goals is to create a map that identifies data gaps as well as a list of metrics to be tracked, Pereira says. At the end of the initial three-year stage, EuropaBON aims to set up a coordinating centre for the monitoring network.It’d have to be affordable to be replicable and maintained over time. Lack of funds has hampered a global version of this project, called GEO BON, on which EuropaBON is based, says Dornelas. To contain costs, EuropaBON intends to use existing long-term monitoring sites. Where there are data gaps, the scientists would launch new tracking efforts using technology such as sensors, weather radar and drones, or citizen volunteers, who already do 80% of the biodiversity monitoring in Europe.EuropaBON would also use satellite data of land cover, vegetation growth and other indicators of local biodiversity. The data streams would be combined with modelling to generate seamless biodiversity data over time and across Europe. The plan is that data from the project will help the European Commission to decide what research to fund on the continent’s biodiversity, says Pereira. In a stakeholder meeting in May for EuropaBON, Humberto Delgado Rosa, the director for natural capital at the European Commission, said that the European Union wants to make “huge leaps internationally in biodiversity, as it has done with climate in Paris”. EuropaBON should help Europe to meet its international commitments to report on its biodiversity, Rosa said.“This new global biodiversity framework needs quantification, measurability,” he said. “In a nutshell, we need knowledge.”Dornelas, who is also part of EuropaBON, says she would like to expand this initiative across the world. Canada is exploring a national version, called CanBON. But for now, monitoring remains sparse in the poorer parts of the world, where most of the planet’s biodiversity remains.“Europe is one of the best monitored parts of the planet, and where we’re really, really missing data is from other parts of the world,” she says. “But I guess we got to start somewhere.”

Nature 596, 22-25 (2021)
doi: https://doi.org/10.1038/d41586-021-02088-3

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Paleotemperature estimatorsPhanerozoic paleotemperature data are derived from oxygen isotope (δ18O), clumped isotope (Δ47), and organic geochemistry (TEX86) data. Many published studies have used different conversion formulae to get from measured values to temperatures for each paleothermometry method, which makes it difficult to compare results from various studies. Because of this, we used a uniform formula to re-calculate paleotemperatures. Different estimators generally show consistent trends for a given time interval, e.g. Cretaceous planktonic foraminifera δ18O and TEX8640, and Jurassic belemnite δ18O41.Most Paleozoic and Mesozoic paleotemperature data come from oxygen isotope paleothermometry. Cenozoic paleotemperature data derive mainly from TEX86 and oxygen isotope of planktonic foraminifera. At present, there are major debates over the first-order trend of Phanerozoic surface temperatures calculated from oxygen isotopes12,42,43,44. For instance, it is uncertain whether δ18Osw evolved towards more enriched values due to exchange at mid-ocean ridges, potentially impacting interpretations of seawater temperatures in the early Paleozoic, for instance43. However, this is not an issue in our study because we focus only on changes in temperature rather than absolute values.The data we used for each time bin was selected from a large paleotemperature dataset12 and published literature by adopting the following criteria: 1) data with well-constrained ages; 2) data measured at high time resolution; 3) data from tropical/subtropical regions. For multiple sets of data in the same time bin, we use the average values. Where possible, in order to test the reliability of climate events, we use temperature data from two different proxies or two different regions. For example, δ18O of planktonic foraminifera and TEX86 were used for the late Eocene (Pg4) and Maastrichtian (K8) intervals; and conodont δ18O from South China and Armenia were used for the Lopingian (P5) interval. The results show that data from two different proxies or different regions have a similar magnitude and rate of temperature change (Supplementary Fig. 4).Oxygen isotopesOxygen isotope values from carbonate fossils (i.e., planktonic foraminifera, brachiopod, oyster, and belemnite) are converted to SSTs using the BAYFOX Bayesian model (https://github.com/jesstierney/bayfoxm)45. For phosphate fossil conodont, we used Monte Carlo simulations to propagate parameter uncertainties to temperature estimation by using the equation of Pucéat et al.46:$${SST}=118.7(pm4.9)-4.22(pm0.20)times left({delta }^{18}{O}_{{con}}-{delta }^{18}{O}_{{SW}}right)times 1000$$
(1)
where δ18Ocon is the oxygen isotope composition of conodonts, and δ18OSW is the oxygen isotope composition of seawater.Seawater oxygen isotope composition is affected by salinity and changes in continental ice volume43,47. As such, δ18O data from localities with abnormal salinities (e.g., evaporite facies, upwelling regions) were excluded (see paleotemperature estimators above). Changes in continental ice volume were also considered in our calculation of paleotemperatures. To do this, the oxygen isotope value of seawater was set to −1‰ (VSMOW) for ice-free time intervals48 and +1‰ (VSMOW) during glacial maximum intervals, e.g., the Pennsylvanian Glacial Maximum and the Pleistocene Last Glacial Maximum49. Most oxygen isotope data are from tropical and subtropical regions (Supplementary Fig. 5), therefore, no latitudinal seawater corrections were made. In addition, seawater pH has a further important influence on the δ18O of foraminifera50. During global warming events that are related to CO2 release, seawater pH can decrease, which would lead to underestimated SSTs50,51. Unfortunately, pH estimates for the Phanerozoic time bins are few, and their values are highly uncertain52. Therefore, we did not pH-correct δ18O estimates. Oxygen isotope values that were likely affected by diagenetic alteration were also removed from the database. Diagenetic screening criteria included δ18O values that are unrealistically negative or positive, and δ18O values from carbonate fossil shells with [Mn]  > 250 ppm and [Sr] 0.4 were not used in this study. Other screening criteria of TEX86 were also employed. Notably, data were removed from the database if Methane Index (MI) >0.5 (ref. 58), delta-Ring Index (ΔRI) > 0.3 (ref. 59), %GDGT-0 >67% (ref. 60), and/or fCren′:Cren′ + Cren >0.25 (ref. 40).Age modelGeochronologic constraints can provide absolute age at the initiation and termination of warming/cooling events. In our database, only one warming event, at the Permian-Triassic boundary, meets this criterion. Other dates were obtained using a comprehensive approach including isotope geochronology, astrochronology, and biostratigraphy with reference to the Geologic Time Scale 201261. Age data were applied in the following order of priority: isotope geochronologic age, astrochronologic age, and biostratigraphic age. If isotope geochronologic ages were available, we gave priority to these absolute dates. In the absence of absolute age data, astrochronologic ages were preferred. If neither of these numerical data was available, the climatic events were timed based on the age of biozones in the Geologic Time Scale 201261. Relative ages within individual biozones were constrained based on the stratigraphic position. Age uncertainty was calculated for all events by using Monte Carlo simulation based on the assumption of uniform distribution of ages.The magnitude and rate of temperature changeThe maximum magnitudes (ΔT) of temperature change within each of the 45 defined time intervals were calculated as:$$Delta T={T}_{1}-{T}_{0}$$
(2)
where ({T}_{0}) and ({T}_{1}) represent the initial and terminal temperature of warming/cooling events, respectively. Supplementary Fig. 1 illustrates how the parameters ({T}_{0}) and ({T}_{1}) are derived from a climatic time series (t).The time scale Δt represents the duration of time during which the temperature increases/decreases. Δt was calculated as:$$Delta t={t}_{1}-{t}_{0}$$
(3)
where ({t}_{0}) and ({t}_{1}) represent the initial and terminal time of warming/cooling events, respectively.The rate (R) of temperature change was computed as:$$R=Delta T/Delta t$$
(4)
The ratio (R) represents the rate of a single warming/cooling event over geological time.ΔT, R and their uncertainties were calculated by using Monte Carlo simulation based on the distribution of data from two groups around t0 and t1.Temperature databaseThe temperature database is composed of the most significant warming/cooling events in 45 time intervals from the Late Ordovician (445 Ma) to early Miocene (16 Ma) (Supplementary Data 162). The time intervals are consistent with the time bins used to compute biodiversity and evolutionary rates63,64, and are defined by one or several neighboring geologic stages with roughly uniform durations (averaging 9.71 Myr). Time bins range between 4.7 and 18.9 Myr. The 45 bins are Katian and Hirnantian (Or5), Llandovery (S1), Wenlock (S2), Ludlow and Pridoli (S3), Lochkovian and Pragian (D1), Emsian (D2), Eifelian and Givetian (D3), Frasnian (D4), Famennian (D5), Toutnaisian (C1), early Visean (C2), late Visean and Serpukhovian (C3), Bashkirian (C4), Moscovian and Kasimovian (C5), Gzhelian (C6), Asselian and Sakmarian (P1), Artinskian (P2), Kungurian and Roadian (P3), Wordian and Capitanian (P4), Wuchiapingian and Changhsingian (P5), Induan and Olenekian (T1), Anisian and Ladinian (T2), Carnian (T3), Norian (T4), Rhaetian (T5), Hettangian and Sinemurian (J1), Pliensbachian (J2), Toarcian and Aalenian (J3), Bajocian-Callovian (J4), Oxfordian (J5), Kimmeridgian and Tithonian (J6), Berriasian and Valanginian (K1), Hauterivian and Barremian (K2), Aptian (K3), Albian (K4), Cenomanian (K5), Turonian-Santonian (K6), Campanian (K7), Maastrichtian (K8), Paleocene (Pg1), Ypresian (Early Eocene, Pg2), Lutetian (early Middle Eocene, Pg3), Bartonian and Priabonian (late Middle-Late Eocene, Pg4), Oligocene (Pg5), and Aquitanian and Burdigalian (Early Miocene, Ng1).Original proxy data, calculated temperature data, geologic age, time span, paleolatitudes, and relevant references are provided in Supplementary Methods and Source Data. Most paleotemperature data are sea surface temperatures (SST) from tropical and subtropical regions (between 40°N and 40°S, Supplementary Fig. 5). Only one collection is from a mid-latitude region with a paleolatitude of 42.71°N (Source Data). Paleolatitudes were reconstructed using PointTracker v7 rotation files published by the PALEOMAP Project65.For most time bins, the major climate change events (warming/cooling) were restricted to that bin. A few events occurred that cross two adjacent bins, e.g., the rapid climate warming around the Permian-Triassic and the Triassic-Jurassic boundaries17,66,67. Both these events had relatively short durations (61–400 kyr) and were initiated towards the end of the first (earliest) time bin. Therefore, these warmings would have primarily impacted organisms in the first bin too. Accordingly, we didn’t divide the warming event into two intervals belonging to the two adjacent bins, but take instead the warming event as belonging to the first bin.Extinction rateThere are a few well-established methods to calculate extinction rate in geological history, e.g., boundary-crosser, gap-filler (GF), and three-timer (3 T) rates63,68,69. Both the GF and 3 T rates are calculated from occurrence data and have higher accuracy than the boundary-crosser method64. The boundary-crosser method is more susceptible to major biases such as the Signor-Lipps effect and sampling bias because it uses full age ranges instead of investigating sampling patterns across limited temporal windows64. The 3 T method can be noisy in the case of high turnover rates and poor sampling. The GF method is more precise when sampling is very poor.Gap-filler and three-timer extinction rates of marine animals were calculated using data from the Paleobiology Database (PBDB, http://paleobiodb.org), which was downloaded on 4 January 2021. The fossil dataset includes all metazoans except for Arachnida, Insecta, Ostracoda, and Tetrapoda and consists of 850,840 fossil occurrences of 37,134 genera. These four groups that are excluded (in keeping with some previous studies64,69) were not used because many of them (Arachnida, Insecta, and Tetrapoda) are terrestrial and appear in marine strata. Ostracoda also has a record in terrestrial rocks. Similar to most studies using data from PBDB, we use genus-level occurrences because genus-level taxonomy is better standardized than the taxonomy of species. GF extinction rate (({r}_{{GF}})) is calculated by using the equation:$${r}_{{GF}}={{{{{rm{log }}}}}}left(frac{2T+{pT}}{3T+{pT}+{GF}}right)$$
(5)
Three-timer extinction rate (({r}_{3T})) is calculated by using the equation:$${r}_{3T}={{{{{rm{log }}}}}}left(frac{2{T}_{i}}{3T}right)+{{{{{rm{log }}}}}}left(frac{3T}{3T+{pT}}right)$$
(6)
where ({GF}) represents gap-fillers, i.e., the number of genera in time bins (i-1) and (i+2) and not within bin (i+1); (2{T}_{i}) represents the number of genera sampled before and within the ith time bin; (3T) represents the number of genera sampled in three consecutive time bins; and ({pT}) represents the number of genera sampled before and after but not within a time bin.Autocorrelation analysisWe tested for serial correlation in the time series of extinction rate, ∆T, and log R, because autocorrelation could impact the calculated correlation coefficients between these data. The autocorrelation functions (ACFs) of extinction rate, ∆T, and log R are very low at all lags >1 (Supplementary Fig. 6), indicating that all-time series lack significant serial correlation.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

The CV represents the discrete degree of trait values, that is, the size of the trait space (Fig. 6b; Supplementary Note S1); S and K are generally used to describe the shape of trait distribution (Fig. 6c,d, Supplementary Note S1). Environment filtering can force a trait to deviate from the original distribution, with characteristics of smaller CV and larger S and K values16,17. Partly consistent with our hypothesis, MAT significantly exerted positive effects on the total concentration of CV, S, and K, but weaker negative effects on the three values of Chl a/b (Fig. 6a). That is, the distributions of Chl concentration and composition shifted in opposite directions under global warming: Chl concentration was distributed in a broader but more differential way (Fig. 7a), while Chl a/b was distributed in a narrower but more uniform way (Fig. 7b).Figure 7Theoretical sketches of distribution shifts for (a) chlorophyll concentration and (b) composition (Chl a/b) under global warming. Purple curves denote the current distributions, and pink ones represent the scenario under global warming. Dashed lines denote the normal distribution in the respective scenarios. It is supposed that the distribution of chlorophyll concentration will shift toward higher mean, CV, S and K values, while Chl a/b shifts toward higher mean but lower CV, S and K values under warming. Chl chlorophyll, CV coefficient of variation, S skewness, K kurtosis.Full size imageThe trait distributional shift under warming is possibly caused by the relative role of species turnover and intraspecific variation (due to plasticity and/or heritable differentiation)25. For Chl concentration and composition, very weak phylogenetic signals were found in three plateaus (Supplementary Table S2), indicating the phenotypic plasticity of Chl, which environments have influenced during the long-term evolution. However, plasticity and intraspecific variation are not the focus of the discussion. Because the species compositions were significantly different among the three plateaus: with only a few species overlapping (Supplementary Fig. S3), and the dominant species and co-existing species gradually varied along the 30 sites (Supplementary Table S3). Shifts in Chl distributions under warming may be interpreted mainly by the alternation of species composition.For Chl concentration, a broader trait space (higher CV) and a more skewed distribution (higher S and K) under warming conditions indicate several new species that differ in functions (here refer to rare species with higher Chl concentration) appeared or increased. This contributed to the long tail of the curve and raised the average Chl concentration. At the same time, most of the other species converged at lower Chl concentrations; that is, Chl concentration undergoes more substantial differentiation and functional contrasting species co-exist under warming. The concentration of Chl is representative of plant growth rate and production ability. Its distribution shift may imply a possible trend of polarisation in functions: both acquisitive and conservative species occur simultaneously. This alteration in species composition indicates changing biotic interactions26. The co-existence of functional contrasting species allows individuals to avoid competition and enhance the exploitation of resources and niche27,28, which is of great importance in optimising community functions28,29. In desert and alpine regions, functional contrasting species with large inter-specific trait variations improve community multi-functionality and enable better resistance to climate change17,30.However, despite the shift in species composition, the distribution of Chl a/b only changed slightly compared to the Chl concentration under warming. The ratio of Chl a to Chl b represents the plant allocation to RC and LHC in PS and the efficiency trade-offs between light capture and light conversion6,7. This ratio is characteristic of conservatism which is mainly manifested in the following aspects: (1) Chl a/b is independent of Chl concentration (orthogonal relationship of the two; Supplementary Fig. S2); (2) Chl a/b distributed more converged with higher K and lower CV (Supplementary Table S1); (3) relative fixed allometric relationships were found between Chl a and Chl b (beside TP; Fig. 8). Plants may adjust their RC and LHC allocation to a common ratio of 3:1 despite large variations in light availability or Chl concentration, which has also been confirmed by a study from forests14. Considering that RC is costlier than LHC, plants tend to sustain the Chl a/b as low as possible unless there is a functional imbalance caused by environmental stress such as warming9,31.Figure 8Standardised major axis regression of chlorophyll a to chlorophyll b in three plateaus. Slopes were given and compared among regions; different lowercase words denote significant (p  More

This study uses SincNet according to the instructions provided by the authors for its application in a different dataset32. This section provides an introduction to SincNet and NIPS4Bplus before detailing the experimental procedure.SincNetThe first convolutional layer of a standard CNN trained on the raw waveform learns filters from the data, where each filter has a number of parameters that matches the filter length (Eq. 1).$$yleft[ n right] = xleft[ n right] times fleft[ n right] = mathop sum limits_{i = 0}^{I – 1} xleft[ i right] cdot fleft[ {n – i} right],$$
(1)

where (xleft[ n right]) is the chunk of the sound, (fleft[ n right]) is the filter of length (I), and (yleft[ n right]) is the filtered output. All the elements of the filter ((i)) are learnable parameters. SincNet replaces (fleft[ n right]) with another function (g) that only depends on two parameters per filter: the lower and upper frequencies of a rectangular bandpass filter (Eq. 2).$$gleft[ {n,f_{l} ,f_{h} } right] = 2f_{h} sincleft( {2pi f_{h} n} right) – 2f_{l} sincleft( {2pi f_{l} n} right),$$
(2)

where (f_{l} text{ and } f_{h}) are the learnable parameters corresponding to the low and high frequencies of the filter and (sincleft( x right) = frac{sinleft( x right)}{x}). The function (g) is smoothed with a Hamming window and the learnable parameters are initialised with given cut-off frequencies in the interval (left[ {0,frac{{f_{s} }}{2}} right]), where (f_{s}) is the sampling frequency.This first layer of SincNet performs the sinc-based convolutions for a set number and length of filters, over chunks of the raw waveform of given window size and overlap. A conventional CNN architecture follows the first layer, that in this study maintains the architecture and uses both standard and enhanced settings. The standard settings used are those of the TIMIT speaker recognition experiment27,32. They include two convolutional layers after the first layer with 60 filters of length 5. All three convolutions use layer normalisation. Next, three fully-connected (leaky ReLU) layers with 2048 neurons each follow, normalised with batch normalisation. To obtain frame-level classification, a final softmax output layer, using LogSoftmax, provides a set of posterior probabilities over the target classes. The classification for each file derives from averaging the frame predictions and voting for the class that maximises the average posterior. Training uses the RMSprop optimiser with the learning rate set to 0.001 and minibatches of size 128. A sample of sinc-based filters generated during this study shows their response both in the time and the frequency domains (Fig. 4).Figure 4Examples of learned SincNet filters. The top row (a–c) shows the filters in the time domain, the bottom row (d–f) shows their respective frequency response.Full size imageThe SincNet repository32 provides an alternative set of settings used in the Librispeech speaker recognition experiment27. Tests of the alternative settings, which include changes in the hidden CNN layers, provided similar results to those of the TIMIT settings and are included as Supplementary Information 1.NIPS4BplusNIPS4Bplus includes two parts: sound files and rich labels. The sound files are the training files of the 2013 NIPS4B challenge for bird song classification23. They are a single channel with a 44.1 kHz sampling rate and 32 bit depth. They comprise field recordings collected from central and southern France and southern Spain15. There are 687 individual files with lengths from 1 to 5 s for a total length of 48 min. The tags in NIPS4Bplus are based on the labels released with the 2013 Bird Challenge but annotated in detail by an experienced bird watcher using dedicated software15. The rich labels include the name of the species, the class of sound, the starting time and the duration of each sound event for each file. The species include 51 birds, 1 amphibian and 9 insects. For birds there can be two types of vocalisations: call and song; and there is also the drumming of a woodpecker. Calls are generally short sounds with simple patterns, while songs are usually longer with greater complexity and can have modular structures or produced by one of the sexes8,13. In the dataset, only bird species have more than one type of sound, with a maximum of two types. The labels in NIPS4Bplus use the same 87 tags present in the 2013 Bird Challenge training dataset with the addition of two other tags: “human” and “unknown” (for human sounds and calls which could not be identified). Tagged sound events in the labels typically correspond to individual syllables although in some occasions the reviewer included multiple syllables into single larger events15. The tags cover only 569 files of the original training set of 687 files. Files without tags include 100 that, for the purpose of the challenge, had no bird sounds but only background noise. Other files were excluded for different reasons such as vocalisations hard to identify or containing no bird or only insect sounds15. The 2013 Bird Challenge also includes a testing dataset with no labels that we did not use15.The total number of individual animal sounds tagged in the NIPS4Bplus labels is 5478. These correspond to 61 species and 87 classes (Fig. 5). The mean length of each tagged sound ranges from ~ 30 ms for Sylcan_call (the call of Sylvia cantillans, subalpine warbler) to more than 4.5 s for Plasab_song (the song of Platycleis sabulosa, sand bush-cricket). The total recording length for a species ranges from 0.7 s for Turphi_call (the call of Turdus philomelos, song thrush) to 51.4 s for Plasab_song. The number of individual files for each call type varies greatly from 9 for Cicatr_song (the call of Cicadatra atra, black cicada) to 282 for Sylcan_call.Figure 5Distribution of sound types by number of calls (number of files) and total length in seconds. Sound types are sorted first by taxonomic group and then by alphabetical order.Full size imageProcessing NIPS4BplusThe recommended pre-processing of human speech files for speaker recognition using SincNet includes the elimination of silent leading and trailing sections and the normalisation of the amplitude27. This study attempts to replicate this by extracting each individual sound as a new file according to the tags provided in the NIPS4Bplus labels. A Python script42 uses the content of the labels to read each wavefile, apply normalisation, select the time of origin and length specified in each individual tag and save it as a new wavefile. The name of the new file includes the original file name and a sequential number suffix according to the order in which tags are listed in the label files (the start time of the sound) to match the corresponding call tags at the time of processing. Each wavefile in the new set fully contains a sound according to the NIPS4Bplus labels. A cropped file may contain sounds from more than one species15, with over 20% of the files in the new set overlapping, at least in part, with sound from another species. The machine learning task does not use files containing background noise or the other parts of the files that are not tagged in the NIPS4Bplus labels. A separate Python script42 generates the lists of files and tags that SincNet requires for processing. The script randomly generates a 75:25 split into lists of train and test files and a Python dictionary key that assigns each file to the corresponding tag according to the file name. The script selects only files confirmed as animal sounds (excluding the tags “unknown” and “human”) and generates three different combinations of tags, as follows: (1) “All classes”: includes all the 87 types of tags originally included in the 2013 Bird Challenge training dataset; (2) “Bird classes”: excludes tags for insects and one amphibian species for a total of 77 classes; and (3) “Bird species”: one class for each bird species independently of the sounds type (call, songs and drumming are merged for each species) for a total of 51 classes. The script also excludes three very short files (length shorter than 10 ms) which could not be processed without code modifications.To facilitate the repeatability of the results, this study attempts to maintain the default parameters of SincNet used in the TIMIT speaker identification task27,32. The number and length of filters in the first sinc-based convolutional layer was set to the same values as the TIMIT experiment (80 filters of length 251 samples) as was the architecture of the CNN. The filters were initialised following the Mel scale cut-off frequencies. We did change the following parameters: (1) reduced the window frame size (cw_len) from 200 to 10 ms to accommodate for the short duration of some of the sounds in the NIPS4Bplus tags (such as some bird vocalisations); (2) reduced the window shift (cw_shift) from 10 to 1 ms in proportion to the reduction in window size (a value a 0.5 could not be given without code modifications); (3) updated the sampling frequency setting (fs) from the TIMIT 16,000 to the 44,100 Hz of the present dataset; and (4) updated the number of output classes (class_lay) to match the number of classes in each training run.To evaluate performance, the training sequence was repeated with the same settings and different random train and test file splits. Five training runs took place for each of the selection of tags: “All classes”, “Bird classes” and “Bird species”.Enhancements and comparisonsChanges in the parameters of SincNet result in different levels of performance. To assess possible improvements and provide baselines to compare against other models we attempted to improve the performance by adjusting a series of parameters, but did not modify the number of layers or make functional changes to the code other than the two outlined below. The parameters tested include: the length of the window frame size, the number and length of the filters in the first layer, number of filters and lengths of the other convolutional and fully connected layers, the length and types of normalisation in the normalisation layers, alternative activation and classification functions, and the inclusion of dropouts (Supplementary Information 1). In addition the SincNet code includes a hard-coded random amplification of each sound sequence; we also tested changing the level and excluding this random amplification through changes in the code. In order to process window frames larger than some of the labelled calls in the NIPS4Bplus dataset, the procedure outlined earlier in which files are cut according to the labels was replaced by a purpose-built process. The original files were not cut, instead a custom python script42 generated train and test file lists that contain the start and length of each labelled call. A modification of the SincNet code42 uses these lists to read the original files and select the labelled call. When the call is shorter than the window frame the code randomly includes the surrounding part of the file to complete the length of the window frame. Grid searches for individual parameters or combinations of similar parameters, over a set number of epochs, selected the best performing values. We also tested the use of the Additive Margin Softmax (AM-softmax) as a cost function37. The best performing models reported in the results use combinations of the best parameter values (Supplementary Information 1). All enhancements and model comparisons use the same dataset selection, that is the same train and test dataset split, of the normalised files for each set of tagged classes.The comparison using waveform + CNN models trained directly on the raw waveform, replaces the initial sinc-based convolution of SincNet with a standard 1d convolutional layer27, thus retaining the same network architecture as SincNet. As with SincNet enhancements, a series of parameter searches provided the best parameter combinations to obtain the best performing models.The pre-trained models used for comparison are DenseNet121, ResNet50 and VGG16 with architectures and weights sourced from the Torchvision library of PyTorch33. We tested three types of spectrograms: Fast Fourier Transform (FFT), Mel spectrum (Mel) and Mel-frequency cepstral coefficient (MFCC) to fine-tune the pre-trained models. FFT calculations used a frame length of 1024 samples, 896 samples overlap and a Hamming window. Mel spectrogram calculations used 128 Mel bands. Once normalised and scaled to 255 pixel intensity three repeats of the same spectrogram represented each of the three input channels of the pre-trained models. The length of sound used to generate the spectrograms was 3 s, and similarly with routines above, for labelled calls shorter than 3 s the spectrogram would randomly include the surrounding sounds. That is, the extract would randomly start in the interval between the end of the labelled call minus 3 s and the start of the call plus 3 s. This wholly includes the labelled call but its position is random within the 3 s sample. A fully connected layer replaced the final classifying layer of the pre-trained models to output the number of labelled classes. In the fine-tuning process the number of trainable layers of the model was not limited to the final fully connected layer, but also included an adjustable number of final layers to improve the results. The learning rate set initially to 0.0001 was halved if the validation loss stopped decreasing for 10 epochs.MetricsMeasures of performance include accuracy, ROC AUC, precision, recall, F1 score, top 3 accuracy and top 5 accuracy. Accuracy, calculated as part of the testing routine, is the ratio between the number of correctly predicted files of the test set and the total number of test files. The calculation of the other metrics uses the Scikit-learn module43 relying on the predicted values provided by the model and performing weighted averages. The ROC AUC calculation uses the mean of the posterior probabilities provided by SincNet for each tagged call. In the pre-trained models the ROC AUC calculations used the probabilities obtained after normalising the output with a softmax function. More