Ecology

Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).ADS 
CAS 

Google Scholar 
Intergovernmental Panel on Climate Change. Climate Change 2013: 5th Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2013).
Google Scholar 
Torres, O., Kwiatkowski, L., Sutton, A. J., Dorey, N. & Orr, J. C. Characterizing mean and extreme diurnal variability of ocean CO2 system variables across marine environments. Geophys. Res. Lett. 48, 2 (2021).
Google Scholar 
Dorey, N., Lançon, P., Thorndyke, M. & Dupont, S. Assessing physiological tipping point of sea urchin larvae exposed to a broad range of pH. Glob. Change Biol. 19, 3355–3367 (2013).
Google Scholar 
Hauri, C. et al. Spatiotemporal variability and long-term trends of ocean acidification in the California current system. Biogeosci. Discuss. 9, 10371–10428 (2012).ADS 

Google Scholar 
Dupont, S. & Pörtner, H.-O. A snapshot into ocean acidification research. Mar. Biol. 160, 1765–1771 (2013).CAS 

Google Scholar 
Dupont, S. & Thorndyke, M. Chapter: Direct impacts of near-future ocean acidification on sea urchins. in Climate Change Perspective from the Atlantic: Past, Present and Future (eds. Fernández-Palacios, J. et al.) 461–485 (2013).Byrne, M. & Hernández, J. C. Chapter 16: Sea urchins in a high CO2 world: Impacts of climate warming and ocean acidification across life history stages. in Developments in Aquaculture and Fisheries Science vol. 43 281–297 (Elsevier, 2020).Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).PubMed 

Google Scholar 
L. Kelley, A., J. Lunden, J., 1 Ocean Acidification Research Center, College of Fisheries and Ocean Sciences, University of Alaska, Fairbanks, Fairbanks, AK, 99775, USA, & 2 Haverford College, Haverford, PA, 19041, USA. Meta-analysis identifies metabolic sensitivities to ocean acidification. AIMS Environ. Sci. 4, 709–729 (2017).Stumpp, M. et al. Acidified seawater impacts sea urchin larvae pH regulatory systems relevant for calcification. Proc. Natl. Acad. Sci. U. S. A. 109, 18192–18197 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stumpp, M. et al. Digestion in sea urchin larvae impaired under ocean acidification. Nat. Clim. Change 3, 1044–1049 (2013).ADS 
CAS 

Google Scholar 
Runcie, D. E. et al. Genomic characterization of the evolutionary potential of the sea urchin Strongylocentrotus droebachiensis facing ocean acidification. Genome Biol. Evol. 8, 272 (2017).
Google Scholar 
Sewell, M. Utilization of lipids during early development of the sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 304, 133–142 (2005).ADS 
CAS 

Google Scholar 
Lucas, M. I., Walker, G., Holland, D. L. & Crisp, D. J. An energy budget for the free-swimming and metamorphosing larvae of Balanus balanoides (Crustacea: Cirripedia). Mar. Biol. 55, 221–229 (1979).
Google Scholar 
Shilling, F. M., Hoegh-Guldberg, O. & Manahan, D. T. Sources of energy for increased metabolic demand during metamorphosis of the abalone Haliotis rufescens (Mollusca). Biol. Bull. 191, 402–412 (1996).CAS 
PubMed 

Google Scholar 
Meidel, S. K. & Scheibling, R. E. Effects of food type and ration on reproductive maturation and growth of the sea urchin Strongylocentrotus droebachiensis. Mar. Biol. 134, 155–166 (1999).
Google Scholar 
Pearce, C. M. & Scheibling, R. E. Induction of metamorphosis of larvae of the green sea urchin, Strongylocentrotus droebachiensis by coralline red algae. Biol. Bull. 179, 304–311 (1990).CAS 
PubMed 

Google Scholar 
Gosselin, P. & Jangoux, M. From competent larva to exotrophic juvenile: a morphofunctional study of the perimetamorphic period of Paracentrotus lividus (Echinodermata, Echinoida). Zoomorphology 118, 31–43 (1998).
Google Scholar 
Hinegardner, R. T. Growth and development of the laboratory cultured sea urchin. Biol. Bull. 137, 465–475 (1969).CAS 
PubMed 

Google Scholar 
Strathmann, R. R. Length of pelagic period in echinoderms with feeding larvae from the Northeast Pacific. J. Exp. Biol. Ecol. 34, 23–27 (1978).
Google Scholar 
Byrne, M. et al. Unshelled abalone and corrupted urchins: Development of marine calcifiers in a changing ocean. Proc. Biol. Sci. 278, 2376–2383 (2011).PubMed 

Google Scholar 
Dupont, S., Dorey, N., Stumpp, M., Melzner, F. & Thorndyke, M. Long-term and trans-life-cycle effects of exposure to ocean acidification in the green sea urchin Strongylocentrotus droebachiensis. Mar. Biol. 160, 1835–1843 (2013).CAS 

Google Scholar 
Uthicke, S. et al. Impacts of ocean acidification on early life-history stages and settlement of the coral-eating sea star Acanthaster planci. PLoS ONE 8, e82938 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dupont, S., Lundve, B. & Thorndyke, M. Near future ocean acidification increases growth rate of the lecithotrophic larvae and juveniles of the sea star Crossaster papposus. J. Exp. Zool. Mol. Dev. Evol. 314, 382–389 (2010).
Google Scholar 
Lim, Y.-K., Dang, X. & Thiyagarajan, V. Transgenerational responses to seawater pH in the edible oyster, with implications for the mariculture of the species under future ocean acidification. Sci. Total Environ. 782, 146704 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Hettinger, A. et al. Persistent carry-over effects of planktonic exposure to ocean acidification in the Olympia oyster. Ecology 93, 2758–2768 (2012).PubMed 

Google Scholar 
Hettinger, A. et al. Larval carry-over effects from ocean acidification persist in the natural environment. Glob. Change Biol. https://doi.org/10.1111/gcb.12307 (2013).Article 

Google Scholar 
Albright, R. & Langdon, C. Ocean acidification impacts multiple early life history processes of the Caribbean coral Porites astreoides. Glob. Change Biol. 17, 2478–2487 (2011).ADS 

Google Scholar 
Yuan, X. et al. Elevated CO2 delays the early development of scleractinian coral Acropora gemmifera. Sci. Rep. 8, 2787 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Maboloc, E. A. & Chan, K. Y. K. Parental whole life cycle exposure modulates progeny responses to ocean acidification in slipper limpets. Glob. Change Biol. 2, 15647. https://doi.org/10.1111/gcb.15647 (2021).Article 

Google Scholar 
Mos, B., Byrne, M. & Dworjanyn, S. A. Effects of low and high pH on sea urchin settlement, implications for the use of alkali to counter the impacts of acidification. Aquaculture 528, 735618 (2020).CAS 

Google Scholar 
Harianto, J., Aldridge, J., Torres Gabarda, S. A., Grainger, R. J. & Byrne, M. Impacts of acclimation in warm-low pH conditions on the physiology of the sea urchin Heliocidaris erythrogramma and carryover effects for juvenile offspring. Front. Mar. Sci. 7, 588938 (2021).
Google Scholar 
Houlihan, E. P., Espinel-Velasco, N., Cornwall, C. E., Pilditch, C. A. & Lamare, M. D. Diffusive boundary layers and ocean acidification: Implications for sea urchin settlement and growth. Front. Mar. Sci. 7, 577562 (2020).
Google Scholar 
Norderhaug, K. M. & Christie, H. C. Sea urchin grazing and kelp re-vegetation in the NE Atlantic. Mar. Biol. Res. 5, 515–528 (2009).
Google Scholar 
Dickson, A., Sabine, C. L. & Christian, J. R. Guide to best practices for ocean CO2 measurements. (PICES Special Publication 3;191 pp, 2007).Lavigne, H. & Gattuso, J.-P. seacarb: seawater carbonate chemistry with R. R package version 2.4. http://CRAN.R-project.org/package=seacarb. (2011).R Core Team. R: A language and environment for statistical computing. R: A language and environment for statistical computing (2017).Guillard, R. R. L. & Ryther, J. H. Studies of marine planktonic diatoms. I. Cyclotella nana Hustedt and Detonula confervacea (Cleve) Gran. Can. J. Microbiol. 8, 229–239 (1962).CAS 
PubMed 

Google Scholar 
Stumpp, M., Wren, J., Melzner, F., Thorndyke, M. & Dupont, S. CO2 induced seawater acidification impacts sea urchin larval development I: Elevated metabolic rates decrease scope for growth and induce developmental delay. Comp. Biochem. Physiol. Mol. Integr. Physiol. 160, 331–340 (2011).CAS 

Google Scholar 
His, E., Heyvang, I., Geffard, O. & De Montaudouin, X. A comparison between oyster (Crassostrea gigas) and sea urchin (Paracentrotus lividus) larval bioassays for toxicological studies. Water Res. 33, 1706–1718 (1999).CAS 

Google Scholar 
U. S. National Institutes of Health, Bethesda, Maryland, U. ImageJ, Rasband, W.S., http://imagej.nih.gov/ij/.Smith, M. M., Cruz Smith, L., Cameron, R. A. & Urry, L. The larval stages of the sea urchin, Strongylocentrotus purpuratus. J. Morphol. 269, 713–733 (2008).PubMed 

Google Scholar 
Kahm, M., Hasenbrink, G., Lichtenberg-Frate, H., Ludwig, J. & Kschischo, M. grofit: Fitting Biological Growth Curves with R. J. Stat. Softw., 33(7), 1–21. URL http://www.jstatsoft.org/v33/i07/. (2010).Pinheiro, J., Bates, D., & R-core. Package ‘nlme’: Linear and Nonlinear Mixed Effects Models. Cran-R (2018).Pan, T.-C.F., Applebaum, S. L. & Manahan, D. T. Experimental ocean acidification alters the allocation of metabolic energy. Proc. Natl. Acad. Sci. U. S. A. 112, 4696–4701 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jager, T., Ravagnan, E. & Dupont, S. Near-future ocean acidification impacts maintenance costs in sea-urchin larvae: Identification of stress factors and tipping points using a DEB modelling approach. J. Exp. Mar. Biol. Ecol. 474, 11–17 (2016).
Google Scholar 
Hoegh-Guldberg, O. & Emlet, R. B. Energy use during the development of a lecithotrophic and a planktotrophic echinoid. Biol. Bull. 192, 27–40 (1997).CAS 
PubMed 

Google Scholar 
Vaïtilingon, D. et al. Effects of delayed metamorphosis and food rations on the perimetamorphic events in the echinoid Paracentrotus lividus (Lamarck, 1816) (Echinodermata). J. Exp. Mar. Biol. Ecol. 262, 41–60 (2001).
Google Scholar 
García, E., Clemente, S. & Hernández, J. C. Ocean warming ameliorates the negative effects of ocean acidification on Paracentrotus lividus larval development and settlement. Mar. Environ. Res. 110, 61–68 (2015).PubMed 

Google Scholar 
Wangensteen, O. S., Dupont, S., Casties, I., Turon, X. & Palacín, C. Some like it hot: Temperature and pH modulate larval development and settlement of the sea urchin Arbacia lixula. J. Exp. Mar. Biol. Ecol. 449, 304–311 (2013).
Google Scholar 
García, E., Clemente, S. & Hernández, J. C. Effects of natural current pH variability on the sea urchin Paracentrotus lividus larvae development and settlement. Mar. Environ. Res. 139, 11–18 (2018).PubMed 

Google Scholar 
Marshall, D. J. & Keough, M. J. Variation in the dispersal potential of non-feeding invertebrate larvae: The desperate larva hypothesis and larval size. Mar. Ecol. Prog. Ser. 255, 145–153 (2003).ADS 

Google Scholar 
Huggett, M. J., Williamson, J. E., de Nys, R., Kjelleberg, S. & Steinberg, P. D. Larval settlement of the common Australian sea urchin Heliocidaris erythrogramma in response to bacteria from the surface of coralline algae. Oecologia 149, 604–619 (2006).ADS 
PubMed 

Google Scholar 
Espinel-Velasco, N., Agüera, A. & Lamare, M. Sea urchin larvae show resilience to ocean acidification at the time of settlement and metamorphosis. Mar. Environ. Res. 159, 104977 (2020).CAS 
PubMed 

Google Scholar 
Lamare, M. & Barker, M. Settlement and recruitment of the New Zealand sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 218, 153–166 (2001).ADS 

Google Scholar 
Martin, S. et al. Early development and molecular plasticity in the Mediterranean sea urchin Paracentrotus lividus exposed to CO2-driven acidification. J. Exp. Biol. 214, 1357–1368 (2011).CAS 
PubMed 

Google Scholar 
Vargas, C. A. et al. Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. Nat. Ecol. Evol. 1, 0084 (2017).
Google Scholar 
Espinel-Velasco, N. et al. Effects of ocean acidification on the settlement and metamorphosis of marine invertebrate and fish larvae: a review. Mar. Ecol. Prog. Ser. 606, 237–257 (2018).ADS 

Google Scholar 
Briffa, M., de la Haye, K. & Munday, P. L. High CO2 and marine animal behaviour: potential mechanisms and ecological consequences. Mar. Pollut. Bull. 64, 1519–1528 (2012).CAS 
PubMed 

Google Scholar 
Gaylord, B. et al. Ocean acidification through the lens of ecological theory. Ecology 96, 3–15 (2015).PubMed 

Google Scholar  More

NEWS
01 April 2022

Tropical forests have big climate benefits beyond carbon storage

Study finds that trees cool the planet by one-third of a degree through biophysical mechanisms such as humidifying the air.

Freda Kreier

Freda Kreier

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Tropical forests create cloud cover that reflects sunlight and cools the air.Credit: Thomas Marent/Minden Pictures

Tropical forests have a crucial role in cooling Earth’s surface by extracting carbon dioxide from the air. But only two-thirds of their cooling power comes from their ability to suck in CO2 and store it, according to a study1. The other one-third comes from their ability to create clouds, humidify the air and release cooling chemicals.
How much can forests fight climate change?
This is a larger contribution than expected for these ‘biophysical effects’ says Bronson Griscom, a forest climate scientist at the non-profit environmental organization Conservation International, headquartered in Arlington, Virginia. “For a while now, we’ve assumed that carbon dioxide alone is telling us essentially all we need to know about forest–climate interactions,” he says. But this study confirms that tropical forests have other significant ways of plugging into the climate system, he says.The analysis, published in Frontiers in Forests and Global Change on 24 March1, could enable scientists to improve their climate models, while helping governments to devise better conservation and climate strategies.The findings underscore growing concerns about rampant deforestation across the tropics. Scientists warn that one-third of the world’s tropical forests have been mown down in the past few centuries, and another one-third has been degraded by logging and development. This, when combined with climate change, could transform vast swathes of forest into grasslands2.“This study gives us even more reasons why tropical deforestation is bad for the climate,” says Nancy Harris, forest-research director at the World Resources Institute in Washington DC.More than a carbon spongeForests are major players in the global carbon cycle because they soak up CO2 from the atmosphere as they grow. Tropical forests, in particular, store around one-quarter of all terrestrial carbon on the planet, making them “centrepieces for climate policy” in their home countries, Griscom says.
Tropical forests may be carbon sources, not sinks
“There’s clear evidence that the tropics are producing excellent climate benefits for the entire planet,” says Deborah Lawrence, an environmental scientist at the University of Virginia in Charlottesville and a co-author of the latest study. She and her colleagues analysed the cooling capacity of forests around the globe, in particular considering biophysical effects alongside carbon storage. Tropical forests, they found, can cool Earth by a whole 1 °C — and biophysical effects contribute significantly.Although scientists knew about these effects, they hadn’t understood to what extent the various factors counter global warming.Trees in the tropics provide shade, but they also act as giant humidifiers by pulling water from the ground and emitting it from their leaves, which helps to cool the surrounding area in a way similar to sweating, Griscom says.“If you go into a forest, it immediately is a considerably cooler environment,” he says.This transpiration, in turn, creates the right conditions for clouds, which like snow and ice in the Arctic, can reflect sunlight higher into the atmosphere and further cool the surroundings. Trees also release organic compounds — for example, pine-scented terpenes — that react with other chemicals in the atmosphere to sometimes create a net cooling effect.Locally coolTo quantify these effects, Lawrence and her colleagues compared how the various effects of forests around the world feed into the climate system, breaking down their contributions in bands of ten degrees of latitude. When they considered only the biophysical effects, the researchers found that the world’s forests collectively cool the surface of the planet by around 0.5 °C.
When will the Amazon hit a tipping point?
Tropical forests are responsible for most of that cooling. But this band of trees across Latin America, Central Africa and southeast Asia is under increasing pressure from climate change and deforestation. Both of these human-caused impacts can lead rainforests to dry out, says Christopher Boulton, a geographer at the University of Exeter, UK. Last month, he and his colleagues published a review2 of nearly 30 years’ worth of satellite images of the Amazon, the largest rainforest in the world. By measuring the biomass of the vegetation in the images, the team discovered that three-quarters of the Amazon is losing resilience — the ability to recover from an extreme weather event such as a drought.Threats to tropical rainforests are dangerous not only for the global climate, but also for communities that neighbour the forests, Lawrence says. She and her colleagues found that the cooling caused by biophysical effects was especially significant locally. Having a rainforest nearby can help to protect an area’s agriculture and cities from heatwaves, Lawrence says. “Every tenth of a degree matters in limiting extreme weather. And where you have forests, the extremes are minimized.”Governments across the tropics have struggled to conserve their forests despite more than two decades of global campaigns to halt deforestation, promote sustainable development and protect the climate. Lawrence says that her team’s findings make it clear that protecting forests is a matter of self-interest, and has immediate benefits for local communities.

doi: https://doi.org/10.1038/d41586-022-00934-6

ReferencesLawrence, D., Coe, M., Walker, W., Verchot, L. & Vandecar, K. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2022.756115 (2022).Article 

Google Scholar 
Boulton, C. A., Lenton, T. M. & Boers, N. Nature Clim. Change 12, 271–278 (2022).Article 

Google Scholar 
Download references

Related Articles

How much can forests fight climate change?

When will the Amazon hit a tipping point?

Tropical forests may be carbon sources, not sinks

Illegal mining in the Amazon hits record high amid Indigenous protests

Subjects

Climate sciences

Climate change

Conservation biology

Latest on:

Climate sciences

Funding battles stymie ambitious plan to protect global biodiversity
News 31 MAR 22

Trends in Europe storm surge extremes match the rate of sea-level rise
Article 30 MAR 22

From the archive: fishy business in 1972 and 1922
News & Views 29 MAR 22

Climate change

Funding battles stymie ambitious plan to protect global biodiversity
News 31 MAR 22

Trends in Europe storm surge extremes match the rate of sea-level rise
Article 30 MAR 22

The race to upcycle CO2 into fuels, concrete and more
News Feature 29 MAR 22

Jobs

Postdoctoral Fellow in Electrochemical CO2 reduction

The University of British Columbia (UBC)
Kelowna, Canada

Staff Member in Project Coordination (for 19 h/week)

Jülich Research Centre (FZJ)
Erlangen-Nürnberg, Germany

Student assistant IT administration (m/f/d)

Alfred Wegener Institute – Helmholtz Centre for Polar and Marine Research (AWI)
Potsdam, Germany

Physicist (postdoctoral researcher) (all genders) in the field of Theoretical Astrophysics

Helmholtz Centre for Heavy Ion Research GmbH (GSI)
Darmstadt, Germany More

Flores, C. O. et al. Proc. Natl Acad. Sci. USA 108, 288–297 (2011).Article 

Google Scholar 
Carlson, M. C. G. et al. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01088-x (2022).Article 

Google Scholar 
Flombaum, P. et al. Proc. Natl Acad. Sci. USA 110, 9824–9829 (2013).CAS 
Article 

Google Scholar 
Coleman, M. L. & Chisholm, S. W. Trends Microbiol. 15, 398–407 (2007).CAS 
Article 

Google Scholar 
Johnson, Z. I. et al. Science 311, 1737–1740 (2006).CAS 
Article 

Google Scholar 
Martiny, A. C. et al. PLoS ONE 11, e0168291 (2016).Article 

Google Scholar 
Wilhelm, S. W. & Suttle, C. A. Bioscience 49, 781–788 (1999).Article 

Google Scholar 
Follett, C. L. et al. Proc. Natl Acad. Sci. USA 119, e2110993118 (2022).CAS 
Article 

Google Scholar 
Mojica, K. D. A. et al. ISME J. 10, 500–513 (2016).CAS 
Article 

Google Scholar 
Mruwat, N. et al. ISME J. 15, 41–54 (2021).CAS 
Article 

Google Scholar  More

To explore how environmental gradients shape the distribution of cyanophages and picocyanobacteria, we conducted high-resolution surveys in surface waters along five oceanic transects on three cruises covering thousands of kilometres in the North Pacific Ocean in the spring or early summer of 2015, 2016 and 2017 (Fig. 1a–c). These cruises, two of which were out-and-back, passed through distinct regimes from warm, saline and nutrient-poor waters of the North Pacific Subtropical Gyre to cooler, less saline and nutrient-rich waters of higher latitudes influenced by the subpolar gyre (Fig. 1d–i)27. The shift between the two gyres was marked by abrupt changes in trophic indicators such as particulate carbon concentrations (Fig. 1g) and a chlorophyll front (defined as the 0.2 mg m−3 chlorophyll contour28; Fig. 1a–c). As such, the inter-gyre transition zone, defined by salinity and temperature thresholds29 (Fig. 1d), was distinct from both the subtropical and subpolar gyre ecosystems28.Fig. 1: Gradients in environmental conditions across the North Pacific gyres.a–c, Transects of three cruises overlaid on monthly averaged satellite-derived sea-surface chlorophyll in March 2015 (a), April 2016 (b) and June 2017 (c). d, Temperature–salinity diagram showing the boundaries of the subtropical and subpolar gyres (black dashed lines) based on the salinity thresholds reported by Roden29. e–i, Temperature (e), salinity (f) as well as the levels of particulate carbon (g), phosphate (h) and nitrate + nitrite (i) as a function of latitude. The coloured dashed lines show the position of the 0.2 mg m−3 chlorophyll contour. For environmental variables plotted against temperature, see Supplementary Fig. 3.Full size imageUnexpected Prochlorococcus declineProchlorococcus concentrations in the oligotrophic waters of the subtropical gyre were 1.5–3.0 × 105 cells ml−1, comprising an average of approximately 29% of the total bacteria (Extended Data Fig. 1) and numerically dominating the phytoplankton community in all three cruises (Extended Data Fig. 2). Prochlorococcus abundance remained high in the southern region of the transition zone in 2015 and 2016, decreasing precipitously to less than 2,000 cells ml−1 north of the chlorophyll front, generally constituting 80% of cyanophages measured, with the remainder consisting of T7-like clade A and TIM5-like cyanophages (Fig. 3 and Extended Data Fig. 4). Cyanophage abundances correlated positively with total picocyanobacteria in the subtropical gyre (Pearson’s coefficient of multiple correlation (r) = 0.54, P = 0.02, n = 26; Fig. 2d), suggesting that cyanophages were limited by the availability of susceptible hosts in this region and were not regulating picocyanobacterial populations. On average, less than 1% of the cyanobacterial populations were infected (Fig. 4), with higher infection rates by T4-like cyanophages than T7-like cyanophages (Extended Data Figs. 5 and 6). These instantaneous measurements of infection were used to estimate the daily rates of mortality39 (Methods and Supplementary Discussion), which suggests that 0.5–6% of picocyanobacterial populations were lysed by viruses each day (Extended Data Fig. 7). This implicates other factors, such as grazing45, as the major causes of cyanobacterial mortality in the North Pacific Subtropical Gyre.Fig. 3: Cyanophage community composition across the North Pacific gyres.a–c, Cyanophage abundance for the March 2015 (a), April 2016 (b) and June 2017 (c) transects. Insets: T7-like clade A and TIM5-like cyanophage abundances on an expanded scale (similar to the main images, the units for the vertical axes are ×105 viruses ml−1). The grey shaded regions show the position of the virus hotspot. See Extended Data Fig. 4 for the confidence intervals and out-and-back reproducibility and Supplementary Fig. 4 for cyanophage lineages plotted against latitude.Full size imageFig. 4: Viral infection patterns of picocyanobacteria in the North Pacific Ocean.a–f, Viral infection levels (black) of Prochlorococcus (a,c,e) and Synechococcus (b,d,f) plotted against temperature for the March 2015 (a,b), April 2016 (c,d) and June 2017 (e,f) transects. Insets: infection levels on an expanded scale. The solid lines show infection (red), Prochlorococcus (green) and Synechococcus (pink) averaged and plotted for every 0.5 °C. The dashed lines and shaded regions show the position of the chlorophyll front and the virus hotspot, respectively. For plots by latitude and the upper and lower bounds of infection, see Extended Data Figs. 5 and 6.Full size imageWithin the transition zone we observed a steep latitudinal increase in the abundance of cyanophages for every transect, which we define as a cyanophage hotspot (Fig. 2c and Extended Data Figs. 2 and 4). The cyanophage abundances in this hotspot were between three- and tenfold greater than in the subtropical gyre (Fig. 2c). Notably, cyanophages were approximately 25% more abundant (an increase of approximately 5 × 105 viruses ml−1) in the hotspot on the 2017 cruise relative to the other two cruises, reaching a maximum of 2 × 106 viruses ml−1. The hotspot peaked at temperatures of 15–16 °C on all transects, regardless of the geographical location, season or the exact pattern of the Prochlorococcus and Synechococcus distributions (Fig. 2c). Notably, the numbers of T7-like clade B cyanophages increased sharply in the transition zone to become the most abundant lineage, whereas T4-like cyanophages increased more modestly (Fig. 3 and Extended Data Fig. 4). The change in the cyanophage community structure was particularly pronounced in June 2017, when T7-like cyanophages were up to 2.3-fold more abundant than T4-like cyanophages (Fig. 3c). The switch in the relative abundance of T4-like and T7-like clade B cyanophages was diagnostic of the cyanophage hotspot compared with patterns in the subtropical and subpolar gyres.To begin assessing whether cyanophages negatively affected cyanobacterial populations in the hotspot, we tested the relationship between the abundance of cyanophages and total cyanobacteria. This showed a significant negative correlation between cyanophage and cyanobacterial abundances across all three cruises (Pearson’s r = −0.56, two-sided P = 0.0005, n = 34). This relationship was particularly distinct in 2017, when cyanobacteria were at their overall lowest abundances and cyanophages at their highest (Pearson’s r = −0.65, two-sided P = 0.004, n = 18). This suggests that viruses are one of the key regulators of picocyanobacteria in the region of the hotspot. However, no significant correlation was found across all regimes and all years (Pearson’s r = −0.008, two-sided P = 0.9, n = 87; Fig. 2d), indicating that factors other than viruses are likely to be more important in regulating the abundances of cyanobacteria in other regimes.Our single-cell infection measurements allowed us to directly evaluate active viral infection and its impact on picocyanobacteria in the transition zone. Viral infection spiked in this region each year with infection levels that were an average of two- to ninefold higher than those in the subtropical gyre (Fig. 4 and Extended Data Figs. 5,6 and 8). Infection peaked within the temperature range of 12–18 °C and was associated with a concomitant dip in Prochlorococcus abundances in all three cruises (Fig. 4 and Extended Data Fig. 5). These findings provide independent support for the strong negative correlation between cell and virus abundances (Fig. 2d) being the result of virus-induced mortality.Lineage-specific infection was also distinct in the transition zone relative to the subtropical gyre. Infection by T7-like clade B cyanophages generally increased to reach (2015 and 2016) or exceed (2017) those of T4-like cyanophages (Extended Data Figs. 5 and 6). In addition, the ratio of the abundances of T7-like clade B cyanophages to the number of cells they infected was 2.6-fold greater in the hotspot than the subtropics, whereas this ratio was similar in both regions for T4-like cyanophages. Together, these results indicate that, within the hotspot, the T4-like cyanophages displayed increased levels of infection, whereas the T7-like cyanophages displayed both increased levels of infection and produced more viruses per infection, suggesting that T7-like clade B cyanophages are better adapted to conditions in the transition zone (see below).Of the three cruises, the highest levels of viral infection were observed in June 2017, with up to 9.5% and 8.9% of Prochlorococcus and Synechococcus infected, respectively (Fig. 4e,f). This dramatic increase in infection mirrored the massive decline in Prochlorococcus abundances (Fig. 4e and Extended Data Fig. 5i). We estimate that viruses killed 10–30% of Prochlorococcus and Synechococcus cells daily at these high instantaneous levels of infection (Extended Data Fig. 6) based on the expected number of infection cycles cyanophages were able to complete at the light and temperature conditions in the transition zone (Methods and Supplementary Discussion). Given that Prochlorococcus is estimated to double every 2.8 ± 0.8 d at the low temperatures in this region12, we estimate that 21–51% of the population was infected and killed in the interval before cell division. Synechococcus is expected to have faster growth rates at these temperatures, doubling every 1.1 ± 0.2 d (refs. 12,46). Thus, we estimate that less of the Synechococcus population (9–31%) was killed before division.Under quasi-steady state conditions, abiotic controls on the growth rate of Prochlorococcus are balanced by mortality due to viral lysis, grazing and other mortality agents39,45,47. Based on the high levels of virus-mediated mortality, the parallel pattern between Prochlorococcus’ death and viral infection, and the negative correlation between cyanophage and picocyanobacterial abundances in the transition zone, we propose that enhanced viral infection in 2017 disrupted this balance, leading to the unexpected decline in Prochlorococcus populations. Grazing and other mortality agents not investigated here could also have contributed to additional mortality beyond the steady state, resulting in further losses of Prochlorococcus. In contrast to Prochlorococcus, Synechococcus maintained large populations despite high levels of infection (Fig. 4f), presumably due to their faster growth rates enabling them to maintain a positive net growth despite enhanced mortality. These findings suggest that virus-mediated mortality in 2017 was an important factor in limiting the geographic range of Prochlorococcus that resulted in a massive loss of habitat of approximately 550 km.Cyanophage abundances and infection levels dropped sharply in the higher-latitude waters north of the hotspot (Figs. 2c, 4 and Extended Data Figs. 1d,h and 2). The abundances of both T7-like clade B and T4-like cyanophages declined precipitously, yet T4-like cyanophages were the dominant cyanophage lineage (Fig. 3). T7-like clade A cyanophages generally increased locally at the northern border of the hotspot and became the dominant T7-like lineage in two samples between 38 and 39.2° N in 2017 (Fig. 3c and Extended Data Fig. 4). In contrast to all other cyanophages, the abundances of TIM5-like cyanophages increased in waters north of the hotspot (Fig. 3 and Extended Data Fig. 4d,i,m) but remained a minor component of the cyanophage community. No relationship was found between cyanophage and cyanobacterial abundances (Fig. 2d), and less than 1.5% of picocyanobacteria were infected by all cyanophage lineages in these waters (Fig. 4).The cyanophage hotspot in the transition zone is a ridge of high virus activity that separates the subtropical and subpolar gyres. The reproducibility of our observations, which were separated by days to weeks within each cruise (2016 and 2017) and by years among the three cruises (Extended Data Fig. 4), indicates that this virus hotspot is a recurrent feature at the boundary of these two major gyres in the North Pacific Ocean. This suggests that the hotspot forms due to the distinctive environment of the inter-gyre transition zone creating conditions that enhance infection of picocyanobacteria and proliferation of cyanophages. Prochlorococcus in the transition zone may be prone to stress due to being close to the limits of their temperature growth range5,6, which has the potential to increase susceptibility to viral infection. Alternatively, there may be temperature-dependent trade-offs between virus decay and production that lead to replication optima within a narrow temperature range48. Cyanophage infectivity has been observed to decay more slowly at colder temperatures49, which may allow for the accumulation of infective viruses, leading to increased infection. In addition, cyanophage infections may be more productive due to enhanced nutrient supply in the transition zone27 (Fig. 1h,i) relative to the subtropics, given that the cyanophages replicate in hosts with presumably greater intracellular nutrient quota and obtain more extracellular nutrients, both of which may increase progeny production9,10. The environmental factors influencing the production and removal of viruses probably vary in intensity at different times, leading to variability in cyanophage abundance and infection levels. Thus, the putative cyanophage replication optimum in the hotspot may reflect the combined effects of temperature and nutrient conditions that are intrinsically linked to the oceanographic forces that shape the transition zone itself.Changes in the cyanophage community structure over environmental gradients are likely to reflect differences in host range, infection properties and genomic potential to remodel host metabolism9. Our data, together with previous measurements in the North Pacific Subtropical Gyre38,39, indicate that the T4-like cyanophages are the lineage best adapted to the low-nutrient waters of the subtropics (Fig. 2d–f). As these waters are inhabited by hundreds of genomically diverse subpopulations of Prochlorococcus50, the broad host range of many T4-like cyanophages18,19,22,51 may be advantageous for finding a suitable host. T4-like cyanophages also have a large and diverse repertoire of host-derived genes21,51—such as nutrient acquisition, photosynthesis and carbon-metabolism genes—that augment host metabolism52 and may increase fitness in nutrient-poor conditions in the subtropics51. In contrast, T7-like clade A and B cyanophages seem to be better adapted to conditions in the transition zone (Fig. 3). T7-like cyanophages have narrow host ranges19,22,40, with smaller genomes and fewer genes to manipulate the host metabolism23, which may allow them to replicate and produce more progeny in regions with elevated nutrient concentrations relative to subtropical conditions. The maximal abundances of TIM5-like cyanophages were found in the most productive waters at the northern end of the transects where the cyanobacterial abundances were lowest and Synechococcus was the dominant picocyanobacterium. This may be partially due to the narrow host range of TIM5-like cyanophages and their specificity for Synechococcus40,44. Our findings of reproducible lineage-specific responses to changing ocean regimes indicate that cyanophage lineages occupy distinct ecological niches.Temperature and nutrient changes occurring in the transition zone are expected to result in shifts in picocyanobacterial diversity at the sub-genus level (Supplementary Discussion), which we speculate may affect community susceptibility to viral infection. One mechanism for this may be that the picocyanobacteria that thrive in the transition zone are intrinsically more susceptible to viral infection. Another scenario may be related to trade-offs associated with the evolution of resistance to viral infection. The horizontal advection of nutrient-rich waters to the transition zone28 may select for rapidly growing cells adapted for efficient resource utilization. Viral resistance in picocyanobacteria often incurs the cost of reduced growth rates53,54. Thus, competition for nutrients in this region may favour cells with faster growth rates but increased susceptibility to viral infection. Thus, it is probable that the cyanophage distributions do not always follow the cyanobacterial patterns (Extended Data Fig. 2) because of complex interactions between lineage-specific cyanophage traits, host community structure and environmental variables, which may vary seasonally or annually as a result of interannual variability in environmental conditions (see below).Despite consistent features in cyanophage distributions across the North Pacific Ocean, cyanophage infection was higher (Fig. 4 and Extended Data Fig. 7), whereas Prochlorococcus abundances were consistently lower (Fig. 2a), across the June 2017 transects relative to the March 2015 and April 2016 transects. Seasonality and/or climate variability could explain this interannual variability, although the data currently available to assess this are sparse. Viral infection of picocyanobacteria in the subtropical gyre increased from early spring to summer, suggesting a potential seasonal pattern that may extend across the transect (Extended Data Fig. 9a). In addition, the June 2017 transect occurred during a neutral-to-negative El Niño phase with lower sea-surface temperatures relative to the 2015 and 2016 transects, which were in years of a record marine heatwave, followed by a strong El Niño55 (Extended Data Fig. 9b). In 2015 and 2016, the Prochlorococcus abundances were found to be higher than usual in the North Pacific Ocean in this (Fig. 2a) and other studies56,57. Irrespective of the underlying drivers for the observed interannual variability, we speculate that an ecosystem tipping point was reached in the hotspot under the prevailing conditions in June 2017, aided by the higher cyanophage abundances yet smaller Prochlorococcus population sizes. In this scenario, picocyanobacterial populations were subjected to high infection levels that resulted in an accumulation of cyanophages, initiating a stronger than usual positive-feedback loop between infection and virus production, and precipitating the unexpected Prochlorococcus decline. Continued observations in the North Pacific Ocean are needed to evaluate the potential link between seasonality and/or large-scale climate forcing as ultimate drivers affecting virus–host interactions.Predicting basin-scale virus dynamicsMeasurements of cyanobacterial and cyanophage abundances rely on discrete sample collection from shipboard oceanographic expeditions, which limits the geographical and seasonal extent of available data. Therefore, we developed a multiple regression model based on high-resolution satellite data of temperature and chlorophyll to predict cyanophage abundances, a key proxy of cyanobacterial infection (Pearson’s r = 0.61, two-sided P = 1.7 × 10−8, degrees of freedom = 68, n = 70). We used the model to estimate the geographical extent of the virus hotspot. The model accurately predicted the location of the hotspot and cyanophage abundances along a fourth transect in April 2019 (Supplementary Table 1), with the majority of observations falling within the 95% confidence intervals of the model predictions (Fig. 5a–c). Application of the model to the larger region predicted that the virus hotspot formed a boundary extending across the North Pacific Ocean, with lower cyanophage abundances on both sides (Fig. 5d,e and Supplementary Fig. 1). This boundary had the hallmarks of the hotspot with a core that was dominated by T7-like cyanophages and the flanking gyre regions dominated by T4-like cyanophages. Thus, this feature may be more appropriately termed a ‘hot-zone’ due to its substantial projected aerial extent. Assuming the infection levels observed in the hotspot in June 2017 were similar throughout the hot-zone, the potential habitat loss for Prochlorococcus would be about 3.2 × 106 km2, approximately half of the cumulative area loss of the Amazonian rainforest to date58.Fig. 5: Prediction of cyanophage abundances.a–c, Model-based predictions of cyanophage abundances corresponding to the empirically measured total (a), T4-like (b) and T7-like clade B (c) cyanophage abundances along a transect in the North Pacific in April 2019. The shaded regions show the 95% confidence interval for the model predictions. d,e, Predicted total cyanophages (d) and the ratio of T4-like/T7-like clade B cyanophages (e) in June 2017 in the North Pacific Ocean. The black lines indicate the cruise track. The grey areas represent regions with no values due to cloud cover or that were beyond the limits of the predictive model. The hotspot peak corresponds to yellow regions in d and red regions in e.Full size imageVirus hotspot biogeochemistryWith the ability to predict biogeographic patterns of cyanophages, we evaluated the potential biogeochemical implications of virus-mediated picocyanobacterial lysis and release of organic material in sustaining the bacterial community6,7,8,9. The aerial extent of the hot-zone (approximately 4 × 106 km2) is only 14% of the size of the subtropical gyre (2.9 × 107 km2), and yet the total virus-mediated organic matter released from picocyanobacteria in the hot-zone in June 2017 was estimated to be on par with that for the entire North Pacific Subtropical Gyre (Methods and Supplementary Discussion). We estimate that viral lysate released from picocyanobacteria in the subtropical gyre could sustain 4.4 ± 0.8% of the calculated bacterial carbon demand there (Extended Data Fig. 10). In contrast, viral lysate released in the transition zone could sustain an average of 21 ± 12% of the bacterial carbon demand, reaching 33% in some regions (Extended Data Fig. 10), assuming that the bacterial assimilation and growth efficiencies were similar between the subtropical gyre and the hotspot. Thus, local generation of cyanobacterial viral lysate in the transition zone is likely to be an important source of carbon for the heterotrophic bacterial community that can rapidly utilize large molecular weight dissolved organic matter59 and may have contributed to the increase in their abundances south of the chlorophyll front in 2017 (Extended Data Fig. 1a,e). More

NEWS
31 March 2022

Funding battles stymie ambitious plan to protect global biodiversity

Researchers are disappointed with the progress — but haven’t lost hope.

Natasha Gilbert

Natasha Gilbert

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Animals such as this orangutan in Indonesia are endangered because of illegal deforestation.Credit: Jami Tarris/Future Publishing via Getty

Scientists are frustrated with countries’ progress towards inking a new deal to protect the natural world. Government officials from around the globe met in Geneva, Switzerland, on 14–29 March to find common ground on a draft of the deal, known as the post-2020 global biodiversity framework, but discussions stalled, mostly over financing. Negotiators say they will now have to meet again before a highly anticipated United Nations biodiversity summit later this year, where the deal was to be signed.The framework so far sets out 4 broad goals, including slowing species extinction, and 21 mostly quantitative targets, such as protecting at least 30% of the world’s land and seas. It is part of an international treaty known as the UN Convention on Biological Diversity, and aims to address the global biodiversity crisis, which could see one million plant and animal species go extinct in the next few decades because of factors such as climate change, human activity and disease.
China takes centre stage in global biodiversity push
The COVID-19 pandemic has already slowed discussions of the deal. Over the past two years, countries’ negotiators met only virtually; the Geneva meeting was the first in-person gathering since the pandemic began. When it ended, Basile van Havre, one of the chairs of the framework negotiations working group, said that because negotiators couldn’t agree on goals, additional discussions will need to take place in June in Nairobi. The convention’s pivotal summit — its Conference of the Parties (COP15) — is expected to be held in Kunming, China, in August and September, but no firm date has been set.Anne Larigauderie, executive secretary of the Intergovernmental Platform on Biodiversity and Ecosystem Services in Bonn, Germany, who attended the Geneva gathering, told Nature: “We are leaving the meeting with no quantitative elements. I was hoping for more progress.”Robert Watson, a retired environmental scientist at the University of East Anglia, UK, says the quantitative targets are crucial to conserving biodiversity and monitoring progress towards that goal. He calls on governments to “bite the bullet and negotiate an appropriate deal that both protects and restores biodiversity”.Finance fightMany who were at the meeting say that disagreements over funding for biodiversity conservation were the main hold-up to negotiations. For example, the draft deal proposed that US$10 billion of funding per year should flow from developed nations to low- and middle-income countries to help them to implement the biodiversity framework. But many think this is not enough. A group of conservation organizations has called for at least $60 billion per year to flow to poorer nations.
Biodiversity moves beyond counting species
The consumption habits of wealthy nations are among the key drivers of biodiversity loss. And poorer nations are often home to areas rich in biodiversity, but have fewer means to conserve them.“The most challenging aspect is the amount of financing that wealthy nations are committing to developing nations,” says Brian O’Donnell, director of the Campaign for Nature in Washington DC, a partnership of private charities and conservation organizations advocating a deal to safeguard biodiversity. “Nations need to up their level of financing to get progress in the COP.”Other nations, particularly low-income ones, probably don’t want to agree “unless they have assurances of resources to allow them to implement the new framework”, Larigauderie says.Countries including Argentina and Brazil are largely responsible for stalling the deal, several sources close to the negotiations told Nature. They asked to remain anonymous because the negotiations are confidential.
The world’s species are playing musical chairs: how will it end?
No agreement could be reached even on targets with broad international support, such as protecting at least 30% of the world’s land and seas by 2030. O’Donnell says that just one country blocked agreement on this target, questioning its scientific basis.Van Havre downplayed the lack of progress, saying that the brinksmanship at the meeting was part of a “normal negotiating process”. He told reporters: “We are happy with the progress made.” Further delays ‘unacceptable’A bright spot in the negotiations, van Havre said, was a last-minute “major step forward” in discussions on how to fairly and equitably share the benefits of digital sequence information (DSI). DSI consists of genetic data collected from plants, animals and other organisms.
Why deforestation and extinctions make pandemics more likely
When pressed, however, van Havre admitted that the progress was simply an agreement between countries to continue discussing a way forward.Thomas Brooks, chief scientist at the International Union for Conservation of Nature in Gland, Switzerland, says that DSI discussions have actually been fraught. Communities from biodiverse-rich regions where genetic material is collected have little control over the commercialization of the data that come from it, and no way to recoup financial and other benefits, he explains.Like biodiversity financing, DSI rights could hold up negotiations on the overall framework. Low-income countries want a fair and equitable share of the benefits from genetic material that originates in their lands, but rich nations don’t want unnecessary barriers to sharing the information.“We are a long way from a watershed moment, and there remain genuine disagreements,” Brooks says. However, he is optimistic that progress will eventually be made.
The biodiversity leader who is fighting for nature amid a pandemic
Some conservation organizations take hope from new provisional language in the deal that calls for halting all human-caused species extinctions. The previous draft of the deal proposed only a reduction in the rate and risk of extinctions, says Paul Todd, an environmental lawyer at the Natural Resources Defense Council, a non-profit group based in New York City.Given how much work governments must do to reach agreement on the deal, Watson says the extra Nairobi meeting is a “logical” move. But he warns: “Any further delay would be unacceptable.”“This isn’t even the hard work,” Todd says. “Implementing the deal will be the real work.”

doi: https://doi.org/10.1038/d41586-022-00916-8

Related Articles

China takes centre stage in global biodiversity push

The biodiversity leader who is fighting for nature amid a pandemic

Why deforestation and extinctions make pandemics more likely

Biodiversity moves beyond counting species

The battle for the soul of biodiversity

The world’s species are playing musical chairs: how will it end?

Subjects

Biodiversity

Conservation biology

Climate change

Latest on:

Biodiversity

Are there limits to economic growth? It’s time to call time on a 50-year argument
Editorial 16 MAR 22

Africa: sequence 100,000 species to safeguard biodiversity
Comment 15 MAR 22

Rewilding Argentina: lessons for the 2030 biodiversity targets
Comment 07 MAR 22

Climate change

Trends in Europe storm surge extremes match the rate of sea-level rise
Article 30 MAR 22

The race to upcycle CO2 into fuels, concrete and more
News Feature 29 MAR 22

Biden bids again to boost science spending — but faces long odds
News 28 MAR 22

Jobs

wiss. Mitarbeiter/in (m/w/d)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

wiss. Mitarbeiter/in (m/w/d)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Junior Research Group Leader on Robustness and Decision Making in Cells and Tissues

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Junior Research Group Leader on Physical Measurement and Manipulation of Living Systems

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany More

Martin, G., Martin-Clouaire, R. & Duru, M. Farming system design to feed the changing world. A review. Agron. Sustain. Dev. 33, 131–149 (2013).
Google Scholar 
McElwee, G. & Bosworth, G. Exploring the strategic skills of farmers across a typology of farm diversification approaches. J. Farm Manag. 13, 819–838 (2010).
Google Scholar 
Maghrebi, M. et al. Iran’s agriculture in the anthropocene. Earth’s Future. https://doi.org/10.1029/2020EF001547 (2020).Article 

Google Scholar 
Raorane, A. A. & Kulkarni, R. V. Data mining: An effective tool for yield estimation in the agricultural sector. Int. J. Emerg. Trends Technol. Comput. Sci. 1, 1–4 (2012).
Google Scholar 
Gonzalez-Sanchez, A., Frausto-Solis, J. & Ojeda-Bustamante, W. Attribute selection impact on linear and nonlinear regression models for crop yield prediction. Sci. World J. 2014, 509429 (2014).
Google Scholar 
Salman, S. A. et al. Changes in climatic water availability and crop water demand for Iraq region. Sustainability 12, 3437 (2020).
Google Scholar 
Mahmood, N., Arshad, M., Kächele, H., Ullah, A. & Müller, K. Economic efficiency of rainfed wheat farmers under changing climate: Evidence from Pakistan. Environ. Sci. Pollut. Res. 27, 34453–34467 (2020).
Google Scholar 
Pracha, A. S. & Volk, T. A. An edible energy return on investment (EEROI) analysis of wheat and rice in Pakistan. Sustainability 3, 2358–2391 (2011).
Google Scholar 
Canadell, J. et al. Abberton, M., Conant, R., & Batello, C. (Eds.). (2010). Grassland carbon sequestration: Management, policy and economics. Food and Agriculture Organization of the United Nations, Integrated Crop Management, Vol. 11–2010. Ahlstrom, A., Raupach, M., Schurgers. Sensit. A Semi-Arid Grassl. To Extrem. Precip. Events 127, 6 (2021).
Google Scholar 
Canton, H. Food and Agriculture Organization of the United Nations—FAO. In The Europa Directory of International Organizations 2021 (ed. Canton, H.) 297–305 (Routledge, 2021).
Google Scholar 
Abdullah, A. et al. Potential for sustainable utilisation of agricultural residues for bioenergy production in Pakistan: An overview. J. Clean. Prod. 287, 125047 (2020).
Google Scholar 
Mughal, I. et al. Protein quantification and enzyme activity estimation of Pakistani wheat landraces. PLoS ONE 15, e0239375 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dorosh, P. & Salam, A. Wheat markets and price stabilisation in Pakistan: An analysis of policy options. Pak. Dev. Rev. 47, 71–87 (2008).
Google Scholar 
Fowke, V. The National Policy and the Wheat Economy (University of Toronto Press, 2019).
Google Scholar 
Hussain, S. et al. Study the effects of COVID-19 in Punjab, Pakistan using space-time scan statistic for policy measures in regional agriculture and food supply chain. Environ. Sci. Pollut. Res. Int. 20, 1–14 (2021).
Google Scholar 
Sajjad, S. A. Story of Pakistan’s Elite Wheat (The Express Tribune, 2017).
Google Scholar 
Durgun, Y. Ö., Gobin, A., Duveiller, G. & Tychon, B. A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time. Int. J. Appl. Earth Obs. Geoinf. 86, 101988 (2020).
Google Scholar 
Vannoppen, A. et al. Wheat yield estimation from NDVI and regional climate models in Latvia. Remote Sens. 12, 2206 (2020).ADS 

Google Scholar 
Irmak, A. et al. Artificial neural network model as a data analysis tool in precision farming. Trans. ASABE 49, 2027–2037 (2006).
Google Scholar 
Bannerjee, G., Sarkar, U., Das, S. & Ghosh, I. Artificial intelligence in agriculture: A literature survey. Int. J. Sci. Res. Comput. Sci. Appl. Manag. Stud. 7, 1–6 (2018).
Google Scholar 
Patrício, D. I. & Rieder, R. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Comput. Electron. Agric. 153, 69–81 (2018).
Google Scholar 
Yaseen, Z. M. et al. Prediction of evaporation in arid and semi-arid regions: A comparative study using different machine learning models. Eng. Appl. Comput. Fluid Mech. 14, 70–89 (2019).
Google Scholar 
Bauer, M. E. The role of remote sensing in determining the distribution and yield of crops. In Advances in Agronomy (ed. Sparks, D. L.) 271–304 (Elsevier, 1975). https://doi.org/10.1016/s0065-2113(08)70012-9.Chapter 

Google Scholar 
Dempewolf, J. et al. Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics. Remote Sens. 6, 9653–9675 (2014).ADS 

Google Scholar 
Hamid, N., Pinckney, T. C., Gnaegy, S. & Valdes, A. The Wheat Economy of Pakistan: Setting and Prospects (IFPRI, 2015).
Google Scholar 
Muhammad, K. Description of the Historical Background of Wheat Improvement in Baluchistan, Pakistan (Agriculture Research Institute (Sariab, Quetta, Baluchistan, Pakistan), 1989).
Google Scholar 
Iqbal, N., Bakhsh, K., Maqbool, A. & Abid Shohab, A. Use of the ARIMA model for forecasting wheat area and production in Pakistan. J. Agric. Soc. Sci. 1, 120–122 (2005).
Google Scholar 
Sher, F. & Ahmad, E. Forecasting wheat production in Pakistan. LAHORE J. Econ. 13, 57–85 (2008).
Google Scholar 
Khan, N. et al. Determination of cotton and wheat yield using the standard precipitation evaporation index in Pakistan. Arab. J. Geosci. 14, 1–16 (2021).
Google Scholar 
Rahman, M. M., Haq, N. & Rahman, R. M. Machine learning facilitated rice prediction in Bangladesh. In 2014 Annual Global Online Conference on Information and Computer Technology. https://doi.org/10.1109/gocict.2014.9 (2014).Chen, C. & Mcnairn, H. A neural network integrated approach for rice crop monitoring. Int. J. Remote Sens. 27, 1367–1393 (2006).
Google Scholar 
Kaul, M., Hill, R. L. & Walthall, C. Artificial neural networks for corn and soybean yield prediction. Agric. Syst. 85, 1–18 (2005).
Google Scholar 
Deo, R. C., Samui, P., Kisi, O. & Yaseen, Z. M. Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation: Theory and Practice of Hazard Mitigation (Springer Nature, 2020).
Google Scholar 
Sanikhani, H. et al. Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors. Comput. Electron. Agric. 152, 242–260 (2018).
Google Scholar 
Hai, T. et al. Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model. IEEE Access 8, 12026–12042 (2020).
Google Scholar 
Ramos, A. P. M. et al. A random forest ranking approach to predict yield in maize with UAV-based vegetation spectral indices. Comput. Electron. Agric. 178, 105791 (2020).
Google Scholar 
Suchithra, M. S. & Pai, M. L. Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters. Inf. Process. Agric. 7, 72–82 (2020).
Google Scholar 
Feng, Z., Huang, G. & Chi, D. Classification of the complex agricultural planting structure with a semi-supervised extreme learning machine framework. Remote Sens. 12, 3708 (2020).ADS 

Google Scholar 
Tur, R. & Yontem, S. A comparison of soft computing methods for the prediction of wave height parameters. Knowl. Based Eng. Sci. 2, 31–46 (2021).
Google Scholar 
Yaseen, Z. M., Ali, M., Sharafati, A., Al-Ansari, N. & Shahid, S. Forecasting standardized precipitation index using data intelligence models: Regional investigation of Bangladesh. Sci. Rep. 11, 1–25 (2021).
Google Scholar 
Sharafati, A., Asadollah, S. B. H. S. & Neshat, A. A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125468 (2020).Article 

Google Scholar 
Huang, G.-B., Zhu, Q.-Y. & Siew, C.-K. Extreme learning machine: Theory and applications. Neurocomputing 70, 489–501 (2006).
Google Scholar 
Adnan, R. M. et al. Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization. Knowl. Based Syst. 230, 107379 (2021).
Google Scholar 
Yaseen, Z. M. et al. Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. J. Hydrol. 542, 603–614 (2016).ADS 

Google Scholar 
Prasad, R., Deo, R. C., Li, Y. & Maraseni, T. Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors. Soil Tillage Res. https://doi.org/10.1016/j.still.2018.03.021 (2018).Article 

Google Scholar 
Tiyasha, T. et al. Functionalization of remote sensing and on-site data for simulating surface water dissolved oxygen: Development of hybrid tree-based artificial intelligence models. Mar. Pollut. Bull. 170, 112639 (2021).CAS 
PubMed 

Google Scholar 
Ali, M. et al. Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology. Energy Rep. 7, 6700–6717 (2021).
Google Scholar 
Khozani, Z. S. et al. Determination of compound channel apparent shear stress: Application of novel data mining models. J. Hydroinform. 21, 798–811 (2019).MathSciNet 

Google Scholar 
Dorigo, M. & Di Caro, G. Ant colony optimization: A new meta-heuristic. In Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999. https://doi.org/10.1109/CEC.1999.782657 (1999).Mullen, R. J., Monekosso, D., Barman, S. & Remagnino, P. A review of ant algorithms. Expert Syst. Appl. https://doi.org/10.1016/j.eswa.2009.01.020 (2009).Article 

Google Scholar 
Sweetlin, J. D., Nehemiah, H. K. & Kannan, A. Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images. Comput. Methods Prog. Biomed. https://doi.org/10.1016/j.cmpb.2017.04.009 (2017).Article 

Google Scholar 
Cordon, O., Herrera, F. & Stützle, T. A review on the ant colony optimization metaheuristic: Basis, models and new trends. Mathw. Comput. 9, 2–3 (2002).MathSciNet 
MATH 

Google Scholar 
Singh, G., Kumar, N. & Kumar Verma, A. Ant colony algorithms in MANETs: A review. J. Netw. Comput. Appl. https://doi.org/10.1016/j.jnca.2012.07.018 (2012).Article 

Google Scholar 
Kumar, S., Solanki, V. K., Choudhary, S. K., Selamat, A. & González Crespo, R. Comparative study on ant colony optimization (ACO) and K-means clustering approaches for jobs scheduling and energy optimization model in internet of things (IoT). Int. J. Interact. Multimed. Artif. Intell. 6, 107 (2020).
Google Scholar 
Paniri, M., Dowlatshahi, M. B. & Nezamabadi-pour, H. MLACO: A multi-label feature selection algorithm based on ant colony optimization. Knowl. Based Syst. 192, 105285 (2020).
Google Scholar 
Yaseen, Z. M., Sulaiman, S. O., Deo, R. C. & Chau, K.-W. An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. J. Hydrol. 569, 387–408 (2019).ADS 

Google Scholar 
Manju Parkavi, R., Shanthi, M. & Bhuvaneshwari, M. C. Recent trends in ELM and MLELM: A review. Adv. Sci. Technol. Eng. Syst. https://doi.org/10.25046/aj020108 (2017).Article 

Google Scholar 
Araba, A. M., Memon, Z. A., Alhawat, M., Ali, M. & Milad, A. Estimation at completion in Civil engineering projects: Review of regression and soft computing models. Knowl. Based Eng. Sci. 2, 1–12 (2021).
Google Scholar 
Tamura, S. & Tateishi, M. Capabilities of a four-layered feedforward neural network: Four layers versus three. IEEE Trans. Neural Netw. 8, 251–255 (1997).CAS 
PubMed 

Google Scholar 
Huang, G.-B. Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans. Neural Netw. 14, 274–281 (2003).PubMed 

Google Scholar 
Ali, M., Deo, R. C., Downs, N. J. & Maraseni, T. Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting. Atmos. Res. 213, 450–464 (2018).
Google Scholar 
Liang, N.-Y., Huang, G.-B., Saratchandran, P. & Sundararajan, N. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 17, 1411–1423 (2006).PubMed 

Google Scholar 
Lan, Y., Soh, Y. C. & Huang, G.-B. Ensemble of online sequential extreme learning machine. Neurocomputing 72, 3391–3395 (2009).
Google Scholar 
Yadav, B., Ch, S., Mathur, S. & Adamowski, J. Discharge forecasting using an online sequential extreme learning machine (OS-ELM) model: A case study in Neckar River, Germany. Measurement 92, 433–445 (2016).ADS 

Google Scholar 
Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).MATH 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 

Google Scholar 
Al-Sulttani, A. O. et al. Proposition of new ensemble data-intelligence models for surface water quality prediction. IEEE Access 9, 108527–108541 (2021).
Google Scholar 
Carranza, C., Nolet, C., Pezij, M. & Van Der Ploeg, M. Root zone soil moisture estimation with random forest. J. Hydrol. 593, 125840 (2021).
Google Scholar 
Evans, J. S., Murphy, M. A., Holden, Z. A. & Cushman, S. A. Modeling species distribution and change using random forest. In Predictive Species and Habitat Modeling in Landscape Ecology (eds Ashton Drew, C. et al.) 139–159 (Springer, 2011).
Google Scholar 
Rahmati, O., Pourghasemi, H. R. & Melesse, A. M. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. CATENA 137, 360–372 (2016).
Google Scholar 
Prasad, R., Ali, M., Kwan, P. & Khan, H. Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation. Appl. Energy 236, 778–792 (2019).
Google Scholar 
Sharafati, A. et al. The potential of novel data mining models for global solar radiation prediction. Int. J. Environ. Sci. Technol. https://doi.org/10.1007/s13762-019-02344-0 (2019).Article 

Google Scholar 
Service, A. M. I. District-Wise Area of Wheat Crop. Available at: http://www.amis.pk/Agristatistics/DistrictWise/2010-2012/Wheat.html (2012).Service, A. M. I. District-Wise Area of Wheat Crop. Available at: http://www.amis.pk/Agristatistics/DistrictWise/2012-2014/Wheat.html (2014).Punjab, P. Population. Available at: https://en.wikipedia.org/wiki/Punjab_Pakistan (2015).Steiniger, S. & Hunter, A. J. S. The 2012 free and open source GIS software map—A guide to facilitate research, development, and adoption. Comput. Environ. Urban Syst. 39, 136–150 (2013).
Google Scholar 
Hsu, C.-W. et al. A practical guide to support vector classification. BJU Int. https://doi.org/10.1177/02632760022050997 (2008).Article 
PubMed 

Google Scholar 
Bergmeir, C. & Benítez, J. M. On the use of cross-validation for time series predictor evaluation. Inf. Sci. (NY) 191, 192–213 (2012).
Google Scholar 
Xia, Y., Liu, C., Li, Y. Y. & Liu, N. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst. Appl. https://doi.org/10.1016/j.eswa.2017.02.017 (2017).Article 

Google Scholar 
Yen, B. C., ASCE Task Committee on Definition of Criteria for Evaluation of Watershed Models of the Watershed Management Committee Irrigation and Drainage Division. Discussion and closure: Criteria for evaluation of watershed models. J. Irrig. Drain. Eng. 121, 130–132 (1995).
Google Scholar 
Yaseen, Z. M. An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. Chemosphere 277, 130126 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Dawson, C. W., Abrahart, R. J. & See, L. M. HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ. Model. Softw. 22, 1034–1052 (2007).
Google Scholar 
Legates, D. R. & Mccabe, G. J. Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233–241 (1999).ADS 

Google Scholar 
Willmott, C. J. & Willmott, C. J. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/1520-0477(1982)063%3c1309:SCOTEO%3e2.0.CO;2 (1982).Article 
MATH 

Google Scholar 
Willmott, C. J. On the validation of models. Phys. Geogr. https://doi.org/10.1080/02723646.1981.10642213 (1981).Article 
MATH 

Google Scholar 
Sharafati, A., Yasa, R. & Azamathulla, H. M. Assessment of stochastic approaches in prediction of wave-induced pipeline scour depth. J. Pipeline Syst. Eng. Pract. 9, 04018024 (2018).
Google Scholar 
Mohammadi, K. et al. A new hybrid support vector machine-wavelet transform approach for estimation of horizontal global solar radiation. Energy Convers. Manag. 92, 162–171 (2015).
Google Scholar 
Willmott, C. J., Robeson, S. M. & Matsuura, K. A refined index of model performance. Int. J. Climatol. 32, 2088–2094 (2012).
Google Scholar 
Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 10, 282–290 (1970).ADS 

Google Scholar 
Yaseen, Z. M. et al. Hourly river flow forecasting: Application of emotional neural network versus multiple machine learning paradigms. Water Resour. Manag. 34, 1075–1091 (2020).
Google Scholar 
Bhagat, S. K., Tung, T. M. & Yaseen, Z. M. Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia. J. Hazard. Mater. 403, 123492 (2021).CAS 
PubMed 

Google Scholar 
Hora, J. & Campos, P. A review of performance criteria to validate simulation models. Expert Syst. 32, 578–595 (2015).
Google Scholar 
Nourani, V., Kisi, Ö. & Komasi, M. Two hybrid Artificial Intelligence approaches for modeling rainfall-runoff process. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2011.03.002 (2011).Article 

Google Scholar 
Ertekin, C. & Yaldiz, O. Comparison of some existing models for estimating global solar radiation for Antalya (Turkey). Energy Convers. Manag. 41, 311–330 (2000).
Google Scholar 
Li, M. F., Tang, X. P., Wu, W. & Liu, H. B. General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Convers. Manag. 70, 139–148. https://doi.org/10.1016/j.enconman.2013.03.004 (2013).Article 

Google Scholar 
Xu, Z., Hou, Z., Han, Y. & Guo, W. A diagram for evaluating multiple aspects of model performance in simulating vector fields. Geosci. Model Dev. 9, 4365–4380 (2016).ADS 

Google Scholar 
Dan Foresee, F. & Hagan, M. T. Gauss–Newton approximation to bayesian learning. In IEEE International Conference on Neural Networks—Conference Proceedings. https://doi.org/10.1109/ICNN.1997.614194 (1997).Akhtar, I. U. H. Pakistan needs a new crop forecasting system (2012).Stathakis, D., Savina, I. & Nègrea, T. Neuro-fuzzy modeling for crop yield prediction. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 34, 1–4 (2006).
Google Scholar 
Kumar, P., Gupta, D. K., Mishra, V. N. & Prasad, R. Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data. Int. J. Remote Sens. 36, 1604–1617 (2015).
Google Scholar 
Sun, J., Xu, W. & Feng, B. A global search strategy of quantum-behaved particle swarm optimization. In 2004 IEEE Conference on Cybernetics and Intelligent Systems. https://doi.org/10.1109/iccis.2004.1460396 (2004)Naganna, S. et al. Dew point temperature estimation: Application of artificial intelligence model integrated with nature-inspired optimization algorithms. Water. https://doi.org/10.3390/w11040742 (2019).Article 

Google Scholar 
Gilles, J. Empirical wavelet transform. IEEE Trans. Signal Process. 61, 3999–4010 (2013).ADS 
MathSciNet 
MATH 

Google Scholar 
Bokde, N., Feijóo, A., Al-Ansari, N., Tao, S. & Yaseen, Z. M. The hybridization of ensemble empirical mode decomposition with forecasting models: Application of short-term wind speed and power modeling. Energies 13, 1666 (2020).
Google Scholar 
Chau, K. W. & Wu, C. L. A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J. Hydroinform. 12, 458–473 (2010).
Google Scholar  More

Emlen, S. T. & Oring, L. W. Ecology, sexual selection, and evolution of mating systems. Science 197(4300), 215–223 (1977).ADS 
CAS 
Article 

Google Scholar 
Lagos, V. O., Bozinovic, F. & Contreras, L. C. Microhabitat use by a small diurnal rodent (Octodon degus) in a semiarid environment: Thermoregulatory constraints or predation risk? J. Mammal. 76(3), 900–905 (1995).Article 

Google Scholar 
Lagos, V. O., Contreras, L. C., Meserve, P. L., Gutiérrez, J. R. & Jaksic, F. M. Effects of predation risk on space use by small mammals: A field experiment with a neotropical rodent. Oikos 74, 259–264 (1995).Article 

Google Scholar 
Schradin, C. & Pillay, N. Female striped mice (Rhabdomys pumilio) change their home ranges in response to seasonal variation in food availability. Behav. Ecol. 17(3), 452–458. https://doi.org/10.1093/beheco/arj047 (2006).Article 

Google Scholar 
Hayes, L. D., Chesh, A. S. & Ebensperger, L. A. Ecological predictors of range areas and use of burrow systems in the diurnal rodent, Octodon degus. Ethology 113, 155–165. https://doi.org/10.1111/j.1439-0310.2006.01305.x (2007).Article 

Google Scholar 
Brivio, F. et al. Forecasting the response to global warming in a heat-sensitive species. Sc. Rep. 9, 3048. https://doi.org/10.1038/s41598-019-39450-5 (2019).ADS 
CAS 
Article 

Google Scholar 
Santamaría, A. E., Olea, P. P., Vinuela, J. & Garcia, J. T. Spatial and seasonal variation in occupation and abundance of common vole burrows in highly disturbed agricultural ecosystems. Eur. J. Wildl. Res. 65, 52. https://doi.org/10.1007/s10344-019-1286-2 (2019).Article 

Google Scholar 
Kinlaw, A. A review of burrowing by semi-fossorial vertebrates in arid environments. J. Arid Environ. 41, 127–145 (1999).ADS 
Article 

Google Scholar 
Daly, M., Beherends, P. R. & Wilson, M. I. Activity patterns of kangaroo rats—Granivores in a desert habitat. In Activity Patterns in Small Mammals: An Ecological Approach (eds Halle, S. & Stenseth, N. C.) 145–158 (Springer, 2000).Chapter 

Google Scholar 
Mackin-Rogalska, R., Adamczewska-Andrzejewska, K. & Nabaglo, L. Common vole numbers in relation to the utilization of burrow system. Acta Theriol. 31(2), 17–44 (1986).Article 

Google Scholar 
Powell, R. A. & Fried, J. J. Helping by juvenile pine voles (Microtus pinetorum), growth and survival of younger siblings, and the evolution of pine vole sociality. Behav. Ecol. 3, 325–333 (1992).Article 

Google Scholar 
Randall, J. A., Rogovin, K., Parker, P. G. & Eimes, J. A. Flexible social structure of a desert rodent, Rhombomys opimus: Philopatry, kinship, and ecological constraints. Behav. Ecol. 16, 961–973 (2005).Article 

Google Scholar 
Ebensperger, L. A. et al. Burrow limitations and group living in the communally rearing rodent, Octodon degus. J. Mammal. 92(1), 21–30 (2011).Article 

Google Scholar 
Santini, L. The habits and influence on the environment of the old world porcupine Hystrix cristata L. in the northernmost part of its range. In Proc. 9th Vertebrate Pest Conference, Vol. 34, 149–153 (1980).Felicioli, A., Grazzini, A. & Santini, L. The mounting and copulation behaviour of the crested porcupine Hystrix cristata. Ital. J. Zool. 64, 155–161 (1997).Article 

Google Scholar 
Felicioli, A., Grazzini, A. & Santini, L. The mounting behaviour of a pair of crested porcupine H. cristata L.. Mammalia 61(1), 123–126 (1997).
Google Scholar 
Felicioli, A. Analisi spazio-temporale dell’attività motoria in Hystrix cristata L. Dissertation, University of Pisa (1991).Felicioli, A. & Santini, L. Burrow entrance-hole orientation and first emergence time in the crested porcupine Hystrix cristata L.: Space-time dependence on sunset. Pol. Ecol. Stud. 20(3–4), 317–321 (1994).
Google Scholar 
Mori, E., Nourisson, D. H., Lovari, S., Romeo, G. & Sforzi, A. Self-defence may not be enough: Moonlight avoidance in a large, spiny rodent. J. Zool. 294, 31–40 (2014).Article 

Google Scholar 
Corsini, M. T., Lovari, S. & Sonnino, S. Temporal activity patterns of crested porcupine Hystrix cristata. J. Zool. Lond. 236, 43–54 (1995).Article 

Google Scholar 
Coppola, F., Vecchio, G. & Felicioli, A. Diurnal motor activity and “sunbathing” behaviour in crested porcupine (Hystrix cristata L., 1758). Sci. Rep. 9, 14283 (2019).ADS 
Article 

Google Scholar 
Pigozzi, G. Crested porcupines (Hystrix cristata) within badger setts (Meles meles) in the Maremma Natural Park, Italy. Saugetierk. Mitt. 33, 261–263 (1986).
Google Scholar 
Coppola, F. & Felicioli, A. Reproductive behaviour in free-ranging crested-porcupine Hystrix cristata L., 1758. Sci. Rep. 11, 20142. https://doi.org/10.1038/s41598-021-99819-3 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Monetti, L., Massolo, A., Sforzi, A. & Lovari, S. Site selection and fidelity by crested porcupines for denning. Ethol. Ecol. Evol. 17, 149–159 (2005).Article 

Google Scholar 
Coppola, F., Dari, C., Vecchio, G., Scarselli, D. & Felicioli, A. Co-habitation of settlements among Crested Porcupines (Hystrix cristata), Red Foxes (Vulpes vulpes) and European Badgers (Meles meles). Curr. Sci. 119(5), 817–822 (2020).Article 

Google Scholar 
De Villiers, M. S., Van Aarde, R. J. & Dott, H. M. Habitat utilization by the Cape porcupine Hystrix africaeaustralis in a savanna ecosystem. J. Zool. Lond. 232, 539–549 (1994).Article 

Google Scholar 
Corbet, N. U. & de Aarde, R. J. Social organization and space use in the Cape porcupine in a Southern African savanna. Afr. J. Ecol. 34, 1–14 (1996).Article 

Google Scholar 
Massolo, A., Dani, F. R. & Bella, N. Sexual and individual cues in the peri-anal gland secretum of crested porcupines (Hystrix cristata). Mamm. Biol. 74, 488–496 (2009).Article 

Google Scholar 
Mori, E. & Lovari, S. Sexual size monomorphism in the crested porcupine (Hystrix cristata). Mamm. Biol. 79, 157–160 (2014).Article 

Google Scholar 
Mori, E. et al. Patterns of spatial overlap in a monogamous large rodent, the crested porcupine. Behav. Process. 107, 112–118 (2014).Article 

Google Scholar 
Mukherjee, A., Pilakandy, R., Kumara, H. N., Manchi, S. S. & Bhupathy, S. Burrow characteristics and its importance in occupancy of burrow dwelling vertebrates in Semiarid area of Keoladeo National Park, Rajasthan, India. J. Arid Environ. 141, 7–15 (2017).ADS 
Article 

Google Scholar 
Mukherjee, A., Pal, A., Velankar, A. D., Kumara, H. N. & Bhupathy, S. Stay awhile in my burrow! Interspecific associations of vertebrates to Indian crested porcupine burrows. Ethol. Ecol. Evol. 3(4), 313–328 (2019).Article 

Google Scholar 
Fernandez, N. & Palomares, F. The selection of breeding dens by the endangered Iberian lynx (Lynx pardinus): Implications for its conservation. Biol. Conserv. 94, 51–61 (2000).Article 

Google Scholar 
Ross, S., Kamnitzer, R., Munkhtsog, B. & Harris, S. Den-site selection is critical for Pallas’s cats (Otocolobus manul). Can. J. Zool. 88(9), 905–913. https://doi.org/10.1139/Z10-056 (2010).Article 

Google Scholar 
Libal, N. S., Belant, J. L., Leopold, B. D., Wang, G. & Owen, A. Despotism and risk of infanticide influence grizzly bear den-site selection. PLoS ONE 6(9), e24133. https://doi.org/10.1371/journal.pone.0024133 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Elbroch, L. M., Lendrum, P. E. & Quigley, H. Cougar den site selection in the Southern Yellowstone ecosystem. Mamm. Res. 60, 89–96. https://doi.org/10.1007/s13364-015-0212-6 (2015).Article 

Google Scholar 
Solomon, N. G., Christiansen, A. M., Kirk Lin, Y. & Hayes, L. D. Factors affecting nest location of prairie voles (Microtus ochrogaster). J. Mammal. 86(3), 555–560 (2005).Article 

Google Scholar 
Pereoglou, F. et al. Refuge site selection by the eastern chestnut mouse in recently burnt heath. Wildl. Res. 38(4), 290–298. https://doi.org/10.1071/WR11007 (2011).Article 

Google Scholar 
Grazzini, M. T. Comportamento riproduttivo e accrescimento post-natale in Hystrix cristata L. (Rodentia, Hystricidae). Dissertation, University of Pisa (1992).Capizzi, D. & Santini, L. Hystrix cristata Linnaeus, 1758. In Fauna d’Italia, Mammalia II: Erinaceomorpha, Soricomorpha, Lagomorpha, Rodentia (eds Amori, G. et al.) 695–706 (Edizione Calderini de il Sole 24 Ore, 2008).
Google Scholar 
Coppola, F. New knowledge tools for crested porcupine (Hystrix cristata L., 1758) management in the wild: First census model, new behavioural ecology aspects and preliminary investigation on health status. University of Pisa, PhD thesis (2021).Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall/CRC, 2017).Book 

Google Scholar 
Wood, S. N. A simple test for random effects in regression models. Biometrika 100, 1005–1010 (2013).MathSciNet 
Article 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).Book 

Google Scholar  More

Martin, P. S. & Klein, R. G. Quaternary extinctions: a prehistoric revolution. (University of Arizona Press, 1984).Waguespack, N. M. & Surovell, T. A. Clovis hunting strategies, or how to make out on plentiful resources. Am. Antiq. 68, 333–352 (2003).
Google Scholar 
Surovell, T. A., Pelton, S. R., Anderson-Sprecher, R. & Myers, A. D. Test of Martin’s overkill hypothesis using radiocarbon dates on extinct megafauna. Proc. Natl. Acad. Sci. 113, 886–891 (2016).CAS 
PubMed 
ADS 

Google Scholar 
Martin, P. S. Prehistoric overkill: the global model. In Quaternary extinctions: a prehistoric revolution (eds. Martin, P. S. & Klein, R. G.) 355–403 (University of Arizona Press, 1984).Barnosky, A. D. & Lindsey, E. L. Timing of Quaternary megafaunal extinction in South America in relation to human arrival and climate change. Quatern. Int. 217, 10–29 (2010).
Google Scholar 
Prescott, G. W., Williams, D. R., Balmford, A., Green, R. E. & Manica, A. Quantitative global analysis of the role of climate and people in explaining late Quaternary megafaunal extinctions. Proc. Natl. Acad. Sci. 109, 4527–4531 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Sandom, C., Faurby, S., Sandel, B. & Svenning, J.-C. Global late Quaternary megafauna extinctions linked to humans, not climate change. Proc. R. Soc. B Biol. Sci. 281, 20133254 (2014).
Google Scholar 
Wolfe, A. L. & Broughton, J. M. A foraging theory perspective on the associational critique of North American Pleistocene overkill. J. Archaeol. Sci. 119, 105162 (2020).
Google Scholar 
Berger, J., Swenson, J. E. & Persson, I. L. Recolonizing carnivores and naïve prey: Conservation lessons from pleistocene extinctions. Science 291, 1036–1039 (2001).CAS 
PubMed 
ADS 

Google Scholar 
Brook, B. W. & Bowman, D. M. J. S. The uncertain blitzkrieg of Pleistocene megafauna. J. Biogeogr. 31, 517–523 (2004).
Google Scholar 
Johnson, C. N. Determinants of loss of mammal species during the Late Quaternary ‘megafauna’ extinctions: life history and ecology, but not body size. Proc. R. Soc. London. Ser. B Biol. Sci. 269, 2221–2227 (2002).CAS 

Google Scholar 
Bourgon, N. et al. Trophic ecology of a Late Pleistocene early modern human from tropical Southeast Asia inferred from zinc isotopes. J. Hum. Evol. 161, 103075 (2021).PubMed 

Google Scholar 
Meltzer, D. J. Overkill, glacial history, and the extinction of North America’s Ice Age megafauna. Proc. Natl. Acad. Sci. 117, 28555–28563 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stewart, M., Carleton, W. C. & Groucutt, H. S. Climate change, not human population growth, correlates with Late Quaternary megafauna declines in North America. Nat. Commun. 12, 965 (2021).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Nogués-Bravo, D., Rodríguez, J., Hortal, J., Batra, P. & Araújo, M. B. Climate change, humans, and the extinction of the woolly mammoth. PLoS Biol. 6, e79 (2008).PubMed 
PubMed Central 

Google Scholar 
Koch, P. L. & Barnosky, A. D. Late quaternary extinctions: State of the debate. Annu. Rev. Ecol. Evol. Syst. 37, 215–250 (2006).
Google Scholar 
Cardillo, M. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).CAS 
PubMed 
ADS 

Google Scholar 
Meiri, S. & Liang, T. Rensch’s rule—Definitions and statistics. Glob. Ecol. Biogeogr. 30, 573–577 (2021).
Google Scholar 
Lyons, S. K. et al. The changing role of mammal life histories in Late Quaternary extinction vulnerability on continents and islands. Biol. Lett. 12, 20160342 (2016).PubMed 
PubMed Central 

Google Scholar 
Alroy, J. A multispecies overkill simulation of the end-pleistocene megafaunal mass extinction. Science 292, 1893–1896 (2001).CAS 
PubMed 
ADS 

Google Scholar 
Smaers, J. B. et al. The evolution of mammalian brain size. Sci. Adv. 7, 1–12 (2021).
Google Scholar 
Jerison, H. J. Evolution of the Brain and Intelligence (Academic Press, 1973). https://doi.org/10.2307/4512058.Book 

Google Scholar 
Sol, D., Bacher, S., Reader, S. M. & Lefebvre, L. Brain size predicts the success of mammal species introduced into novel environments. Am. Nat. 172, S63–S71 (2008).PubMed 

Google Scholar 
Møller, A. P. & Erritzøe, J. Brain size in birds is related to traffic accidents. R. Soc. Open Sci. 4, 161040 (2017).PubMed 
PubMed Central 
ADS 

Google Scholar 
Sayol, F., Sol, D. & Pigot, A. L. Brain size and life history interact to predict urban tolerance in birds. Front. Ecol. Evol. 8, 58 (2020).
Google Scholar 
Budd, G. E. & Jensen, S. The origin of the animals and a ‘Savannah’ hypothesis for early bilaterian evolution. Biol. Rev. 92, 446–473 (2017).PubMed 

Google Scholar 
Benoit, J. et al. Brain evolution in Proboscidea (Mammalia, Afrotheria) across the Cenozoic. Sci. Rep. 9, 9323 (2019).PubMed 
PubMed Central 
ADS 

Google Scholar 
Møller, A. P. & Erritzøe, J. Brain size and the risk of getting shot. Biol. Lett. 12, 20160647 (2016).PubMed 
PubMed Central 

Google Scholar 
Di Febbraro, M. et al. Does the jack of all trades fare best? Survival and niche width in Late Pleistocene megafauna. J. Biogeogr. 44, 2828–2838 (2017).
Google Scholar 
Morris, S. D., Kearney, M. R., Johnson, C. N. & Brook, B. W. Too hot for the devil? Did climate change cause the mid-Holocene extinction of the Tasmanian devil Sacrophilus harrisii from mainland Australia? Ecography 2022, (2022).Fillios, M., Crowther, M. S. & Letnic, M. The impact of the dingo on the thylacine in Holocene Australia. World Archaeol. 44, 118–134 (2012).
Google Scholar 
González-Lagos, C., Sol, D. & Reader, S. M. Large-brained mammals live longer. J. Evol. Biol. 23, 1064–1074 (2010).PubMed 

Google Scholar 
Barton, R. A. & Capellini, I. Maternal investment, life histories, and the costs of brain growth in mammals. Proc. Natl. Acad. Sci. U.S.A. 108, 6169–6174 (2011).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Abelson, E. S. Brain size is correlated with endangerment status in mammals. Proc. R. Soc. B Biol. Sci. 283, 20152772 (2016).
Google Scholar 
Gonzalez-Voyer, A., González-Suárez, M., Vilà, C. & Revilla, E. Larger brain size indirectly increases vulnerability to extinction in mammals. Evolution (N.Y.) 70, 1364–1375 (2016).
Google Scholar 
Ives, A. R. & Helmus, M. R. Generalized linear mixed models for phylogenetic analyses of community structure. Ecol. Monogr. 81, 511–525 (2011).
Google Scholar 
Castiglione, S. et al. A new method for testing evolutionary rate variation and shifts in phenotypic evolution. Methods Ecol. Evol. 9, 974–983 (2018).
Google Scholar 
Billet, G. Phylogeny of the Notoungulata (Mammalia) based on cranial and dental characters. J. Syst. Palaeontol. 9, 481–497 (2011).
Google Scholar 
Shultz, S., Bradbury, R. B., Evans, K. L., Gregory, R. D. & Blackburn, T. M. Brain size and resource specialization predict long-term population trends in British birds. Proc. R. Soc. B Biol. Sci. 272, 2305–2311 (2005).
Google Scholar 
Ducatez, S., Sol, D., Sayol, F. & Lefebvre, L. Behavioural plasticity is associated with reduced extinction risk in birds. Nat. Ecol. Evol. 4, 788–793 (2020).PubMed 

Google Scholar 
Abelson, E. S. Big brains reduce extinction risk in Carnivora. Oecologia 191, 721–729 (2019).PubMed 
ADS 

Google Scholar 
Lundgren, E. J. et al. Introduced herbivores restore Late Pleistocene ecological functions. Proceedings of the National Academy of Sciences 117, 7871–7878 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Shultz, S. & Dunbar, R. Encephalization is not a universal macroevolutionary phenomenon in mammals but is associated with sociality. Proceedings of the National Academy of Sciences 107, 21582–21586 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Gould, S. J. & Vrba, E. S. Exaptation—A missing term in the science of form. Paleobiology 8, 4–15 (1982).
Google Scholar 
Wroe, S. et al. Climate change frames debate over the extinction of megafauna in Sahul (Pleistocene Australia-New Guinea). Proc. Natl. Acad. Sci. U.S.A. 110, 8777–8781 (2013).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the Causes of Late Pleistocene Extinctions on the Continents. Science 306, 70–75 (2004).Article 
PubMed 

Google Scholar 
Profico, A., Buzi, C., Melchionna, M., Veneziano, A. & Raia, P. Endomaker, a new algorithm for fully automatic extraction of cranial endocasts and the calculation of their volumes. Am. J. Phys. Anthropol. 172, 511–515 (2020).PubMed 

Google Scholar 
Damuth, J. & Macfadden, B. J. Body Size in Mammalian Paleobiology: Estimation and Biological Implications (Cambridge University Press, 1990).
Google Scholar 
Zagwijn, W. H. The beginning of the Ice Age in Europe and its major subdivisions. Quatern. Sci. Rev. 11, 583–591 (1992).ADS 

Google Scholar 
Hearty, P. J., Hollin, J. T., Neumann, A. C., O’Leary, M. J. & McCulloch, M. Global sea-level fluctuations during the Last Interglaciation (MIS 5e). Quatern. Sci. Rev. 26, 2090–2112 (2007).ADS 

Google Scholar 
Ashwell, K. W. S., Hardman, C. D. & Musser, A. M. Brain and behaviour of living and extinct echidnas. Zoology 117, 349–361 (2014).PubMed 

Google Scholar 
Castiglione, S. et al. The influence of domestication, insularity and sociality on the tempo and mode of brain size evolution in mammals. Biol. J. Linn. Soc. 132, 221–231 (2021).
Google Scholar 
Wilkins, A. S., Wrangham, R. W. & Tecumseh Fitch, W. The ‘domestication syndrome’ in mammals: A unified explanation based on neural crest cell behavior and genetics. Genetics 197, 795–808 (2014).PubMed 
PubMed Central 

Google Scholar 
Sayol, F., Steinbauer, M. J., Blackburn, T. M., Antonelli, A. & Faurby, S. Anthropogenic extinctions conceal widespread evolution of flightlessness in birds. Sci. Adv. 6, eabb6095 (2020).PubMed 
PubMed Central 
ADS 

Google Scholar 
Fromm, A., Meiri, S. & McGuire, J. Big, flightless, insular and dead: Characterising the extinct birds of the Quaternary. J. Biogeogr. 48(9), 2350–2359. https://doi.org/10.1111/jbi.14206 (2021).Article 

Google Scholar 
Meiri, S., Dayan, T. & Simberloff, D. The generality of the island rule reexamined. J. Biogeogr. 33, 1571–1577 (2006).
Google Scholar 
Larramendi, A. & Palombo, M. R. Body Size, Structure, Biology and Encephalization Quotient of Palaeoloxodon ex gr. P. falconeri from Spinagallo Cave (Hyblean plateau, Sicily). Hystrix, the Italian Journal of Mammalogy 26, 102–109 (2015).Article 

Google Scholar 
Slavenko, A., Tallowin, O. J. S., Itescu, Y., Raia, P. & Meiri, S. Late Quaternary reptile extinctions: Size matters, insularity dominates. Glob. Ecol. Biogeogr. 25, 1308–1320 (2016).
Google Scholar 
Tracy, C. R. & George, T. L. On the determinants of extinction. Am. Nat. 139, 102–122 (1992).
Google Scholar 
Manne, L. L., Brooks, T. M. & Pimm, S. L. Relative risk of extinction of passerine birds on continents and islands. Nature 399, 258–261 (1999).CAS 
ADS 

Google Scholar 
Turvey, S. T. In the shadow of the megafauna: prehistoric mammal and bird extinctions across the Holocene. in Holocene Extinctions 17–40 (Oxford University Press, 2009). https://doi.org/10.1093/acprof:oso/9780199535095.003.0002Ebinger, P. A cytoarchitectonic volumetric comparison of brains in wild and domestic sheep. Zeitschrift für Anat. und Entwicklungsgeschichte 144, 267–302 (1974).CAS 

Google Scholar 
Röhrs, M. & Ebinger, P. Welche quantitativen beziehungen bestehen bei säugetieren zwischen schädelkapazität und hirnvolumen? Mammalian Biology 66, 102–110 (2001).Köhler, M. & Moyà-Solà, S. Reduction of brain and sense organs in the fossil insular bovid Myotragus. Brain Behav. Evol. 63, 125–140 (2004).PubMed 

Google Scholar 
de Bello, F. et al. On the need for phylogenetic ‘corrections’ in functional trait-based approaches. Folia Geobot. 50, 349–357 (2015).
Google Scholar 
Bates, D., Sarkar, D., Bates, M. D. & Matrix, L. The lme4 Package. October (2007).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Google Scholar 
Raia, P. & Meiri, S. The tempo and mode of evolution: Body sizes of island mammals. Evolution 65, 1927–1934 (2011).

Google Scholar 
Montgomery, S. H. et al. The evolutionary history of cetacean brain and body size. Evolution 67, 3339–3353 (2013).
PubMed 

Google Scholar 
Li, D., Dinnage, R., Nell, L. A., Helmus, M. R. & Ives, A. R. phyr: An r package for phylogenetic species-distribution modelling in ecological communities. Methods Ecol. Evol. 11, 1455–1463 (2020).
Google Scholar 
Melchionna, M. et al. Macroevolutionary trends of brain mass in Primates. Biological Journal of the Linnean Society 129, 14–25 (2020).Article 

Google Scholar 
Serio, C. et al. Macroevolution of toothed whales exceptional relative brain size. Evol. Biol. 46, 332–342 (2019).
Google Scholar 
Wickham, H. et al. Welcome to the Tidyverse. Journal of Open Source Software 4, 1686 (2019).Barton, K. Package ‘MuMIn’ Title Multi-Model Inference. CRAN-R (2018). More