More stories

  • in

    Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation

    Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).Article 

    Google Scholar 
    Friedlingstein, P. et al. Global carbon budget 2022. Earth Syst. Sci. Data 14, 4811–4900 (2022).Article 

    Google Scholar 
    Peng, S.-S. et al. Afforestation in China cools local land surface temperature. Proc. Natl Acad. Sci. USA 111, 2915–2919 (2014).Article 

    Google Scholar 
    Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. 6, 6603 (2015).Article 

    Google Scholar 
    Houghton, R. A. & Nassikas, A. A. Global and regional fluxes of carbon from land use and land cover change 1850-2015. Glob. Biogeochem. Cycles 31, 456–472 (2017).Article 

    Google Scholar 
    Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2016).Article 

    Google Scholar 
    Longo, M. et al. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Glob. Biogeochem. Cycles 30, 1639–1660 (2016).Article 

    Google Scholar 
    Qie, L. et al. Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 8, 1966 (2017).Article 

    Google Scholar 
    Smith, I. A., Hutyra, L. R., Reinmann, A. B., Marrs, J. K. & Thompson, J. R. Piecing together the fragments: elucidating edge effects on forest carbon dynamics. Front. Ecol. Environ. 16, 213–221 (2018).Article 

    Google Scholar 
    Franklin, C. M. A., Harper, K. A. & Clarke, M. J. Trends in studies of edge influence on vegetation at human-created and natural forest edges across time and space. Can. J. For. Res. 51, 274–282 (2020).Article 

    Google Scholar 
    Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, eaax8574 (2020).Article 

    Google Scholar 
    Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).Article 

    Google Scholar 
    Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).Article 

    Google Scholar 
    Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).Article 

    Google Scholar 
    Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).Article 

    Google Scholar 
    Schoene, D., Killmann, W., Lüpke, H. V. & LoycheWilkie, M. Forests and Climate Change Working Paper 5: Definitional Issues Related to Reducing Emissions from Deforestation in Developing Countries (FAO, 2007).Goetz, S. J. et al. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environ. Res. Lett. 10, 123001 (2015).Article 

    Google Scholar 
    Pearson, T. R. H., Brown, S., Murray, L. & Sidman, G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag. 12, 3 (2017).Article 

    Google Scholar 
    Cadenasso, M. L., Traynor, M. M. & Pickett, S. T. Functional location of forest edges: gradients of multiple physical factors. Can. J. For. Res. 27, 774–782 (1997).Article 

    Google Scholar 
    Schmidt, M., Jochheim, H., Kersebaum, K.-C., Lischeid, G. & Nendel, C. Gradients of microclimate, carbon and nitrogen in transition zones of fragmented landscapes – a review. Agric. For. Meteorol. 232, 659–671 (2017).Article 

    Google Scholar 
    Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).Article 

    Google Scholar 
    Silva Junior, C. H. L. et al. Amazonian forest degradation must be incorporated into the COP26 agenda. Nat. Geosci. 14, 634–635 (2021).Article 

    Google Scholar 
    Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl Acad. Sci. USA 104, 6550–6555 (2007).Article 

    Google Scholar 
    Windisch, M. G., Davin, E. L. & Seneviratne, S. I. Prioritizing forestation based on biogeochemical and local biogeophysical impacts. Nat. Clim. Change 11, 867–871 (2021).Article 

    Google Scholar 
    Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data 13, 3927–3950 (2021).Article 

    Google Scholar 
    Chuvieco, E. et al. Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth Syst. Sci. Data 10, 2015–2031 (2018).Article 

    Google Scholar 
    Zhao, Z. et al. Fire enhances forest degradation within forest edge zones in Africa. Nat. Geosci. https://doi.org/10.1038/s41561-021-00763-8 (2021).Cook, M., Schott, J. R., Mandel, J. & Raqueno, N. Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a land surface temperature (LST) product from the archive. Remote Sens. https://doi.org/10.3390/rs61111244 (2014).Wan, Z. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ. 140, 36–45 (2014).Article 

    Google Scholar 
    Broadbent, E. N. et al. Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon. Biol. Conserv. 141, 1745–1757 (2008).Article 

    Google Scholar 
    Chaplin-Kramer, R. et al. Degradation in carbon stocks near tropical forest edges. Nat. Commun. 6, 10158 (2015).Article 

    Google Scholar 
    Silva Junior, C. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaaz8360 (2020).Article 

    Google Scholar 
    Laurance, W. F. et al. Biomass collapse in Amazonian forest fragments. Science 278, 1117–1118 (1997).Article 

    Google Scholar 
    Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).Article 

    Google Scholar 
    Zheng, C., Jia, L. & Hu, G. Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite Earth observations. J. Hydrol. 613, 128444 (2022).Article 

    Google Scholar 
    Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 14855 (2017).Article 

    Google Scholar 
    Laurance, W. F. et al. The fate of Amazonian forest fragments: a 32-year investigation. Biol. Conserv. 144, 56–67 (2011).Article 

    Google Scholar 
    de Paula, M. D., Costa, C. P. A. & Tabarelli, M. Carbon storage in a fragmented landscape of Atlantic forest: the role played by edge-affected habitats and emergent trees. Trop. Conserv. Sci. 4, 349–358 (2011).Article 

    Google Scholar 
    van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).Article 

    Google Scholar 
    Gillett, N. P., Arora, V. K., Matthews, D. & Allen, M. R. Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations. J. Clim. 26, 6844–6858 (2013).Article 

    Google Scholar 
    Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).Article 

    Google Scholar 
    Kozlowski, T. T. Responses of woody plants to flooding and salinity. Tree Physiol. 17, 490–490 (1997).Article 

    Google Scholar 
    Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374 (2018).Article 

    Google Scholar 
    Sze, J. S., Carrasco, L. R., Childs, D. & Edwards, D. P. Reduced deforestation and degradation in Indigenous lands pan-tropically. Nat. Sustain. 5, 123–130 (2022).Article 

    Google Scholar 
    Masson-Delmotte, V. et al. IPCC: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds) (Cambridge Univ. Press, 2021).Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global Datasets of Forest Above-Ground Biomass for the Years 2010, 2017 and 2018, v3 (NERC EDS Centre for Environmental Data Analysis, 2021); https://doi.org/10.5285/5f331c418e9f4935b8eb1b836f8a91b8Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).Article 

    Google Scholar 
    Alkama, R. et al. Vegetation-based climate mitigation in a warmer and greener world. Nat. Commun. 13, 606 (2022).Article 

    Google Scholar 
    Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. 9, 679 (2018).Article 

    Google Scholar 
    Matthews, H. D., Gillett, N. P., Stott, P. A. & Zickfeld, K. The proportionality of global warming to cumulative carbon emissions. Nature 459, 829–832 (2009).Article 

    Google Scholar 
    Li, W. et al. Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations. Biogeosciences 14, 5053–5067 (2017).Article 

    Google Scholar 
    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 

    Google Scholar  More

  • in

    Evaluation of the growth, adaption, and ecosystem services of two potentially-introduced urban tree species in Guangzhou under drought stress

    Study site, tree selections, and drought-simulation experimentThis research was performed in Guangzhou (22°26′-23°56′N, 112°57′-114°03′E), which is a core city located in subtropical zones. With an area of 7434.4 km2 and a population of 18.87 million, Guangzhou’s urbanization rate has reached 86.46%. To cope with multiple environmental challenges, several urban-forest nurseries were established to cultivate and introduce various tree species. Among them, we selected the one in Tianhe District as our study site. This nursery was not only abundant with native and exotic tree species but also equipped with similar edatope in cities, which was ideal for our research.Tilia cordata Mill. (Tc) and Tilia tomentosa Moench (Tt), originating from the west of Britain and southeast of Europe, were common urban tree species planted in European cities. Based on their performance in providing ecological and landscape functions, these two tree species were considered to be introduced for urban greening. Therefore, Tilia cordata Mill. (Tc) and Tilia tomentosa Moench (Tt) were selected as our objectives, which were investigated for their growth and ecosystem services to evaluate their adaption in Guangzhou. In addition, a native tree species Tilia miqueliana Maxim (Tm) was also implemented concurrent measurement as a comparison.For each of the three surveyed tree species, ten trees with a diameter at breast height (DBH) around 5.5 cm and tree height around 2.5 m were chosen for our experiment, which were thought to possess similar initial statuses. To investigate the impact of drought on the growth and ecosystem services of the three selected tree species, a controlled experiment was launched from January to December in 2020. For each tree species, five trees were planted in the common environment as the controlled group, while the other five trees were under the precipitation-exclusion installation (PEI) as the drought-simulation group. Consisting of several water-proof tents, PEI was adequately large and could completely prevent trees from obtaining rainfalls, which created a precipitation-exclusive environment to simulate an enduring drought event within the whole research period (Fig. 1).Figure 1Schematic diagram of the drought simulation experiment for the three surveyed tree species.Full size imageEnvironmental monitoring systemsClimatic data were sampled every 10 min with a weather station (WP3103 mesoscale automatic weather station, China) located at an unshaded site in the nursery. The data were stored in the logger and copied to our laboratory to produce daily or monthly data. All the climatic variables, including photosynthetically active radiation (PAR, µmol m-2 s-1), wind speed (m s-1), precipitation (mm), and air temperature (°C) were calculated from January to December in 2020.For volumetric soil water content (%; VWC), the HOBO MX2307 system (Onetemp, Adelaide, Australia), placed in a shaded box in the nursery, was applied for all the three tree species from both the controlled and drought-simulation groups. For each individual tree, the sensing probe was inserted horizontally at the depths of 30 cm and located 20 cm in the northern direction from the tree stems. Based on the daily readings, monthly means were calculated from January to December in 2020.Measurement of above-ground growthTo investigate the above-ground growth of the three tree species from both the controlled and drought-simulation groups, their DBH (diameter at breast height, cm), tree height (m), and LAI (leaf area index) were measured at the beginning of each month in 2020. DBH was measured with the help of a caliper (Altraco Inc., Sausalito, California, USA), and their tree heights were measured using a standard tape. The crown analytical instrument CI-110 (Camas, Washington State, USA) was used to capture an accurate image of tree crowns and calculate LAI. Sufficient numbers of points were measured and recorded to describe each tree’s average crown shape. The software FV2200 (LICOR Biosciences, Lincoln, NE) helped compute each tree’s crown width and crown area.Measurement of below-ground growthFine root coring campaigns were launched for all the trees of the three tree species from both the controlled and drought treatment groups every three to four months, i.e., in February, May, September, and December. Although the coring campaign might damage part of the roots, the fine roots obtained each time were a mere portion of the whole root system, not affecting the general development of trees’ underground processes. For every individual tree, two 30-cm soil cores were applied in each direction of north, south, east, and west, of which one was located at 20 cm to the trunk (paracentral roots) and the other one was located at 40 cm (outer roots). In addition, the soil samples were evenly divided into three horizons which were 0–10 cm (shallow layer), 10–20 cm (middle layer), and 20–30 cm (deep layer). Then a sieve with 2-mm mesh size was used to filter all the fine roots. The fine roots were washed carefully to remove the adherent soils and dried in an oven at 65 ℃ for 72 h. Finally, all the samples were weighed using a balance with an accuracy of four decimal places to obtain the dry weight. The fine root biomass at different depths was calculated using the dry weight divided by the cross-sectional area of the auger20.Model’s simulation of ecosystem servicesThe process-based model City-Tree was used to predict the ecosystem services of the three tree species from both the controlled and drought-simulation groups23. The model required the data of tree growth parameters including tree height, DBH, and crown area together with environmental conditions such as edaphic and climatic data24. In this research, cooling, evapotranspiration and CO2 fixation of the three surveyed tree species in the controlled and drought-treatment groups were simulated at the end of 2020.The actual evapotranspiration eta was calculated from the potential evapotranspiration using fetp[t], Tilia’s factors fetp[t], and the reduction factor fred:$${mathrm{et}}_{mathrm{a}}={mathrm{f}}_{mathrm{red}}*{mathrm{f}}_{mathrm{etp}}left[mathrm{t}right]*{mathrm{et}}_{mathrm{p}}$$The process of tree’s evapotranspiration (etp) was calculated on the basis of SVAT algorithm together with Penman formula in the module on water balance as below:$${mathrm{et}}_{mathrm{p}}=left[mathrm{s }/ left(mathrm{s}+upgamma right)right]*left({mathrm{r}}_{mathrm{s}}-{mathrm{r}}_{mathrm{L}}right) /mathrm{ L}+left[1-mathrm{s }/ left(mathrm{s}+upgamma right)right]*{mathrm{e}}_{mathrm{s}}*mathrm{f }left({mathrm{v}}_{mathrm{u}}right)$$with γ: psychrometric constant in hPa K−1; s: the slope of the saturation vapour pressure curve in hPa K−1; rs: short wave radiation balance in W m−2; rL: long-wave radiation balance in W m−2; L: specific evaporation heat in W m−2 mm−1 d; es: saturation deficit in hPa; f (vu): ventilation function with vu being the daily average wind speed in m s−1.Within the module cooling, the energy needed for the transition of water from liquid to gaseous phase was calculated based on the crown area (CA) and the transpiration eta sum:$${mathrm{E}}_{mathrm{A}}= {mathrm{et}}_{mathrm{a}}*mathrm{CA}-left({mathrm{L}}_{mathrm{O}}* -0.00242*mathrm{temp}right) / {mathrm{f}}_{mathrm{con}}$$with EA: energy released by a tree through transpiration (kWh tree-1), LO: energy needed for the transition of the 1 kg of water from the liquid to gaseous phase = 2.498 MJ (kgH2O)-1 and temp = temperature in ℃, fcon: 0.5.The calculation of new assimilation in the module of photosynthesis and respiration was on the basis of the approach of Haxeltine and Prenticem25. The model assumed that 50% of the incoming short-wave radiation is photosynthetic active radiation (PAR). Using the LAI and a light extinction factor of 0.5, the radiation amount of 1 m2 leaf area can be estimated based on an exponential function according to the Lambert–Beer law. This way, the gross assimilation per m2 leaf area as the daily mean of the month can be derived from:$${text{A}} = {text{d}}*{{left[ {left( {{text{J}}_{{text{p}}} + {text{J}}_{{text{r}}} – {text{sqrt}} left( {left( {{text{J}}_{{text{P}}} + {text{J}}_{{text{r}}} } right)^{2} – 4*uptheta *{text{J}}_{{text{p}}} *{text{J}}_{{text{r}}} } right)} right)} right]} mathord{left/ {vphantom {{left[ {left( {{text{J}}_{{text{p}}} + {text{J}}_{{text{r}}} – {text{sqrt}} left( {left( {{text{J}}_{{text{P}}} + {text{J}}_{{text{r}}} } right)^{2} – 4*uptheta *{text{J}}_{{text{p}}} *{text{J}}_{{text{r}}} } right)} right)} right]} {left( {2*uptheta } right)}}} right. kern-0pt} {left( {2*uptheta } right)}}$$with A: gross assimilation [g C m−2 d−1]; d: mean day length of the month [h]; Jp: reaction of photosynthesis on absorbed photosynthetic radiation [g C m−2 h−1]; Jr: rubisco limited rate of photosynthesis [g C m−2 h−1]; θ: form factor = 0.7.Jp was defined as a function of the photosynthetic active radiation PAR in mol m−2 h−1 and the efficiency of carbon fixation per absorbed PAR [g C mol−1].$${text{J}}_{{text{p}}} = {text{c}}_{{text{p}}} {text{*PAR}}$$$${text{c}}_{{text{p}}} = alpha *left( {{text{p}}_{{{text{ci}}}} – {text{r}}} right){ /}left( {{text{p}}_{{{text{ci}}}} – {text{r}}} right)*gamma *{text{m}}_{{{text{co}}_{2} }} *{text{i}}left[ {text{t}} right]$$with α: intrinsic quantum efficiency for CO2 uptake = 0.08; pci: partial pressure of the internal CO2 [Pa]; r: CO2 compensation point [Pa]; ϒ: species dependent adjustment function for tree age; m CO2: molecular mass of C = 12.0 g mol−1; i[t]: influence of temperature on efficiency.Net assimilation AN [g C m−2 d−1] was then derived from the gross assimilation A and the dark respiration Rd by:$${text{A}}_{{text{N}}} = {text{A}} – {text{R}}_{{text{d}}}$$$${text{R}}_{{text{d}}} =upbeta *{text{V}}_{{text{m}}}$$where Vm was calculated as:$${text{V}}_{{text{m}}} = {1 mathord{left/ {vphantom {1 upbeta }} right. kern-0pt} upbeta } * {{{text{c}}_{{text{p}}} } mathord{left/ {vphantom {{{text{c}}_{{text{p}}} } {{text{c}}_{{text{r}}} * {text{PAR}} * left[ {left( {2uptheta – 1} right) * beta * {{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – left( {2uptheta *upbeta *{{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – {text{c}}_{{text{r}}} } right)*varsigma } right]}}} right. kern-0pt} {{text{c}}_{{text{r}}} * {text{PAR}} * left[ {left( {2theta – 1} right) * beta * {{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – left( {2theta *upbeta *{{text{d}} mathord{left/ {vphantom {{text{d}} {{text{d}}_{max } }}} right. kern-0pt} {{text{d}}_{max } }} – {text{c}}_{{text{r}}} } right)*varsigma } right]}}$$By multiplying AN, the number of days and the total leaf area, the entire monthly net assimilation of the tree can be obtained. In this study, we assumed a fixed share of 50% as respiration based on the gross primary production that the resulting net primary production NPP was transformed in the content of fixed carbon by multiplying the value with the carbon conversion factor 0.524.$${mathrm{Carbon}}_{mathrm{fix}}=0.5*mathrm{NPP}$$Statistical analysesThe software package R was used for statistical analysis. To investigate the differences between means, two-sampled t-test and analysis of variance (ANOVA) with Tukey’s HSD (honestly significant difference) test were used. All the cases, the means were reported as significant when P  More

  • in

    Interspecific interactions alter the metabolic costs of climate warming

    Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).Article 
    CAS 

    Google Scholar 
    Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).Article 

    Google Scholar 
    Havird, J. C. et al. Distinguishing between active plasticity due to thermal acclimation and passive plasticity due to Q10 effects: why methodology matters. Funct. Ecol. 34, 1015–1028 (2020).Article 

    Google Scholar 
    Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).Article 
    CAS 

    Google Scholar 
    White, C. R., Alton, L. A., Bywater, C. L., Lombardi, E. J. & Marshall, D. J. Metabolic scaling is the product of life history optimization. Science 377, 834–839 (2022).Article 
    CAS 

    Google Scholar 
    Savage, V. M., Gilloly, J. F., Brown, J. H. & Charnov, E. L. Effects of body size and temperature on population growth. Am. Nat. 163, 429–441 (2004).Article 

    Google Scholar 
    Bernhardt, J. R., Sunday, J. M. & O’Connor, M. I. Metabolic theory and the temperature–size rule explain the temperature dependence of population carrying capacity. Am. Nat. 192, 687–697 (2018).Article 

    Google Scholar 
    Damuth, J. Population density and body size in mammals. Nature 290, 699–700 (1981).Article 

    Google Scholar 
    Schuster, L., Cameron, H., White, C. R. & Marshall, D. J. Metabolism drives demography in an experimental field test. Proc. Natl Acad. Sci. USA 118, e2104942118 (2021).Article 
    CAS 

    Google Scholar 
    Amarasekare, P. & Coutinho, R. M. The intrinsic growth rate as a predictor of population viability under climate warming. J. Anim. Ecol. 82, 1240–1253 (2013).Article 

    Google Scholar 
    Amarasekare, P. & Savage, V. A framework for elucidating the temperature dependence of fitness. Am. Nat. 179, 178–191 (2012).Article 

    Google Scholar 
    Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Nat. 142, 911–927 (1993).Article 

    Google Scholar 
    Comeault, A. A. & Matute, D. R. Temperature-dependent competitive outcomes between the fruit flies Drosophila santomea and Drosophila yakuba. Am. Nat. 197, 312–323 (2021).Article 

    Google Scholar 
    Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998).Article 
    CAS 

    Google Scholar 
    Davis, A. J., Lawton, J. H., Shorrocks, B. & Jenkinson, L. S. Individualistic species responses invalidate simple physiological models of community dynamics under global environmental change. J. Anim. Ecol. 67, 600–612 (1998).Article 

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

    Google Scholar 
    Janča, M. & Gvoždík, L. Costly neighbours: heterospecific competitive interactions increase metabolic rates in dominant species. Sci. Rep. 7, 5177 (2017).Article 

    Google Scholar 
    Pettersen, A. K., Hall, M. D., White, C. R. & Marshall, D. J. Metabolic rate, context-dependent selection, and the competition–colonization trade-off. Evol. Lett. 4, 333–344 (2020).Article 

    Google Scholar 
    DeLong, J. P., Hanley, T. C. & Vasseur, D. A. Competition and the density dependence of metabolic rates. J. Anim. Ecol. 83, 51–58 (2014).Article 

    Google Scholar 
    Reid, D., Armstrong, J. D. & Metcalfe, N. B. Estimated standard metabolic rate interacts with territory quality and density to determine the growth rates of juvenile Atlantic salmon. Funct. Ecol. 25, 1360–1367 (2011).Article 

    Google Scholar 
    Ayala, F. J. in Essays in Evolution and Genetics in Honor of Theodosius Dobzhansky (eds Hecht, M. K. & Steere, W. C.) 121–158 (Springer, 1970).Atkinson, W. D. & Shorrocks, B. Aggregation of larval Diptera over discrete and ephemeral breeding sites: the implications for coexistence. Am. Nat. 124, 336–351 (1984).Article 

    Google Scholar 
    McKenzie, J. A. & McKechnie, S. W. A comparative study of resource utilization in natural populations of Drosophila melanogaster and D. simulans. Oecologia 40, 299–309 (1979).Article 
    CAS 

    Google Scholar 
    Alton, L. A. et al. Developmental nutrition modulates metabolic responses to projected climate change. Funct. Ecol. 34, 2488–2502 (2020).Article 

    Google Scholar 
    Mitchell, K. A. & Hoffmann, A. A. Thermal ramping rate influences evolutionary potential and species differences for upper thermal limits in Drosophila. Funct. Ecol. 24, 694–700 (2010).Article 

    Google Scholar 
    Overgaard, J., Kristensen, T. N., Mitchell, K. A. & Hoffmann, A. A. Thermal tolerance in widespread and tropical Drosophila species: does phenotypic plasticity increase with latitude? Am. Nat. 178, S80–S96 (2011).Article 

    Google Scholar 
    Kellermann, V. et al. Comparing thermal performance curves across traits: how consistent are they? J. Exp. Biol. 222, jeb193433 (2019).Article 

    Google Scholar 
    Terblanche, J. S., Clusella-Trullas, S. & Chown, S. L. Phenotypic plasticity of gas exchange pattern and water loss in Scarabaeus spretus (Coleoptera: Scarabaeidae): deconstructing the basis for metabolic rate variation. J. Exp. Biol. 213, 2940–2949 (2010).Article 

    Google Scholar 
    Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).Article 
    CAS 

    Google Scholar 
    Bos, M., Burnet, B., Farrow, R. & Woods, R. A. Mutual facilitation between larvae of the sibling species Drosophila melanogaster and D. simulans. Evolution 31, 824–828 (1977).Article 
    CAS 

    Google Scholar 
    Arthur, W. On the complexity of a simple environment: competition, resource partitioning and facilitation in a two-species Drosophila system. Phil. Trans. R. Soc. B 313, 471–508 (1986).
    Google Scholar 
    Hodge, S., Mitchell, P. & Arthur, W. Factors affecting the occurrence of facilitative effects in interspecific interactions: an experiment using two species of Drosophila and Aspergillus niger. Oikos 87, 166–174 (1999).Article 

    Google Scholar 
    Bath, E., Morimoto, J. & Wigby, S. The developmental environment modulates mating-induced aggression and fighting success in adult female Drosophila. Funct. Ecol. 32, 2542–2552 (2018).Article 

    Google Scholar 
    Thibert, J., Farine, J. P., Cortot, J. & Ferveur, J. F. Drosophila food-associated pheromones: effect of experience, genotype and antibiotics on larval behavior. PLoS ONE 11, e0151451 (2016).Article 

    Google Scholar 
    Chown, S. L. et al. Scaling of insect metabolic rate is inconsistent with the nutrient supply network model. Funct. Ecol. 21, 282–290 (2007).Article 

    Google Scholar 
    Becker, R. A., Wilks, A. R. & Brownrigg, R. mapdata: extra map databases. R version 2.3.0 https://CRAN.R-project.org/package=mapdata (2018).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Bolker, B. & R Development Core Team bbmle: tools for general maximum likelihood estimation. R version 1.0.25 https://CRAN.R-project.org/package=bbmle (2022).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

    Google Scholar 
    Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn (Sage, 2019).Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R version 0.4.6 https://CRAN.R-project.org/package=DHARMa (2022).Messamah, B., Kellermann, V., Malte, H., Loeschcke, V. & Overgaard, J. Metabolic cold adaptation contributes little to the interspecific variation in metabolic rates of 65 species of Drosophilidae. J. Insect Physiol. 98, 309–316 (2017).Article 
    CAS 

    Google Scholar 
    Chamberlain, S. et al. rgbif: interface to the global biodiversity information facility API. R version 3.7.3 https://CRAN.R-project.org/package=rgbif (2022).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    Hijmans, R. J. raster: geographic data analysis and modeling. R version 3.6-3 https://CRAN.R-project.org/package=raster (2022).Alton, L. A. & Kellermann, V. Data for “Interspecific interactions alter the metabolic costs of climate warming”. Zenodo https://doi.org/10.5281/zenodo.7475922 (2023).White, C. R. et al. Geographical bias in physiological data limits predictions of global change impacts. Funct. Ecol. 35, 1572–1578 (2021).Article 

    Google Scholar  More

  • in

    Combining socioeconomic and biophysical data to identify people-centric restoration opportunities

    Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    IKI. The Bonn Challenge. https://www.bonnchallenge.org/ (2022).UNCCD. Land Degradation Neutrality. https://www.unccd.int/land-and-life/land-degradation-neutrality/overview (2022).Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Brancalion, P. H. S. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Erbaugh, J. T. et al. Global forest restoration and the importance of prioritizing local communities. Nat. Ecol. Evol. 4, 1472–1476 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Fleischman, F. et al. Restoration prioritization must be informed by marginalized people. Nature 607, E5–E6 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Chaturvedi, R. et al. Restoration Opportunities Atlas of India. www.india.restorationatlas.org/methodology (2022).McLain, R., Lawry, S., Guariguata, M. R. & Reed, J. Toward a tenure-responsive approach to forest landscape restoration: a proposed tenure diagnostic for assessing restoration opportunities. Land Use Policy 104, 103748 (2021).Article 

    Google Scholar 
    Binod, B., Bhattarcharjee, A. & Ishwar, N. M. Bonn Challenge and India: Progress on Restoration Efforts Across States and Landscapes (IUCN, 2018).Government of India. Aspirational Districts Phase 1 (vikaspedia, 2018).Government of India. Census of India. https://censusindia.gov.in/2011census/dchb/DCHB.html (2011).DeFries, R. et al. Land management can contribute to net zero. Science 376, 1163–1165 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Borah, B., Bhattacharya, A. & Ishwar, N. M. Bonn Challenge and India. Progress On Restoration Efforts Across States and Landscapes. https://www.bonnchallenge.org/pledges/india (2018).Gopalakrishna, T. et al. Existing land uses constrain climate change mitigation potential of forest restoration in India. Conserv. Lett. https://doi.org/10.1111/conl.12867 (2022).Dhyani, S. et al. Agroforestry to achieve global climate adaptation and mitigation targets: are South Asian countries sufficiently prepared? Forests 12, 303 (2021).Article 

    Google Scholar 
    Nerlekar, A. N. et al. Removal or utilization? Testing alternative approaches to the management of an invasive woody legume in an arid Indian grassland. Restor. Ecol. https://doi.org/10.1111/rec.13477 (2022).Coleman, E. A. et al. Limited effects of tree planting on forest canopy cover and rural livelihoods in Northern India. Nat Sustain 4, 997–1004 (2021).Article 

    Google Scholar 
    Ramprasad, V., Joglekar, A. & Fleischman, F. Plantations and pastoralists: afforestation activities make pastoralists in the Indian Himalaya vulnerable. Ecol. Soci. https://doi.org/10.5751/ES-11810-250401 (2020).DeFries, R. et al. Improved household living standards can restore dry tropical forests. Biotropica https://doi.org/10.1111/btp.12978 (2021).Lele, S., Khare, A. & Mokashi, S. Estimating and Mapping CFR Potential (ATREE, 2020).Agarwala, M. et al. Impact of biogas interventions on forest biomass and regeneration in southern India. Global Ecol. Conservation 11, 213–223 (2017).Article 

    Google Scholar 
    Menon, A. & Schmidt-Vogt, D. Effects of the COVID-19 pandemic on farmers and their responses: a study of three farming systems in Kerala. South India. Land 11, 144 (2022).
    Google Scholar 
    Fremout, T. et al. Diversity for Restoration (D4R): Guiding the selection of tree species and seed sources for climate‐resilient restoration of tropical forest landscapes. J. Appl. Ecol. 59, 664–679 (2022).Article 

    Google Scholar 
    Hughes, K. A. et al. Can restoration of the commons reduce rural vulnerability? A Quasi-experimental comparison of COVID-19 livelihood-based coping strategies among rural households in three Indian States. Int. J. Common. 16, 189 (2022).Article 

    Google Scholar 
    Madhusudan, M. D. & Vanak, A. Mapping the Distribution and Extent of India’s Semi-arid Open Natural Ecosystems. https://doi.org/10.1002/essoar.10507612.1 (2021).Vanak, A. T., Hiremath, A. J., Ganesh, T. & Rai, N. D. Filling in the (Forest) Blanks: the Past, Present and Future of India’s Savanna Grasslands (ATREE, 2017).Oxford Poverty & Human Development Initiative. Global Multidimensional Poverty Index 2018. The Most Detailed Picture to Date of the World’s Poorest People. https://ophi.org.uk/wp-content/uploads/G-MPI_2018_2ed_web.pdf (2018).Hijmans, R. J. et al. raster: Geographic Data Analysis and Modeling. https://rspatial.org/raster (2023).Bivand, R. et al. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. https://cran.r-project.org/web/packages/rgdal/index.html (2023).QGIS Development Team. QGIS Geographic Information System (Open Source Geospatial Foundation Project, 2022). More

  • in

    Observed reductions in rainfall due to tropical deforestation

    RESEARCH BRIEFINGS
    01 March 2023

    Tropical deforestation affects local and regional precipitation, but the effects are uncertain and have not been determined using observations. Satellite data sets were used to show reductions in precipitation over areas of tropical forest loss, with stronger reductions seen as the deforested area expands. More

  • in

    Open-source software for geospatial analysis

    Satellite imagery provides insight into where and how Earth’s surface changes, particularly in remote areas where in situ measurements are generally lacking. With the large volumes of data produced by satellites, we need streamlined computational pipelines for optimized processing capabilities. Although a multitude of platforms exists to process satellite data, these often have expensive license requirements that price out much of the geospatial community. Moreover, many of these platforms are propriety, but transparency is key when developing geospatial processing workflows. Open-source programming is critical to the creation of efficient imagery processing pipelines. More

  • in

    Sub-continental-scale carbon stocks of individual trees in African drylands

    OverviewThis study establishes a framework for mapping carbon stocks at the level of individual trees at a sub-continental scale in semi-arid sub-Saharan Africa north of the Equator. We used satellite imagery from the early dry season (Extended Data Fig. 1). The deep learning method developed by a previous study1 allowed us to map billions of discrete tree crowns at the 50-cm scale from West Africa to the Red Sea. Then we used allometry to convert tree crown area into tree wood, foliage and root carbon for the 0–1,000 mm year−1 precipitation zone in which our allometry was collected (Extended Data Fig. 2). We introduce a viewer that enables the billions of trees to be viewed at different scales, with information on location, metadata of the Maxar satellite image used, tree crown area and the estimated wood, foliage and root carbon content based on our allometry (Fig. 4). We also make available our output data for the 1,000 mm year−1 precipitation zone southward to 9.5° N latitude with information on location, precipitation, metadata of the Maxar satellite image used, tree crown area, tree wood carbon, tree root carbon and tree leaf carbon.Satellite imageryWe used 326,523 Maxar multispectral images from the QuickBird-2, GeoEye-1, WorldView-2 and WorldView-3 satellites collected from 2002 to 2020 from November to March from 9.5° N to 24° N latitude within Universal Transverse Mercator (UTM) zones 28–37 for Africa (Extended Data Table 1a). These images were obtained by NASA through the NextView License from the National Geospatial-Intelligence Agency. Data were assembled over several years with a focus on later years to achieve a relatively recent and complete wall-to-wall coverage.When using satellite data from different satellites over several years, with varying sun–target–satellite angles, with varying radiometric calibration of satellite spectral bands and different atmospheric compositions through which the surface is imaged, there are two possibilities for using hundreds of thousands of satellite images together quantitatively. One approach, used extensively in NASA’s, NOAA’s and the European Space Agency’s Earth-viewing satellite programmes, is to quantitatively inter-calibrate radiometrically the satellite channels through time; correct these data for time-dependent atmospheric effects such as aerosols, clouds, haze, smoke, dust and other atmospheric constituent effects and then normalize the viewing perspective to the same sun–target–satellite angle38. Another approach is to use the satellite data as collected; assemble training data of trees viewed from different satellites under different sun–target–satellite angles, different times, different atmospheric conditions and use machine learning with high-performance computing to perform the tree mapping at the 50-cm scale. The key to successful machine learning is to account for all the sources of variation within the domain of study in the training data to ensure accurate identification of trees under all circumstances. We included trees viewed substantially off-nadir, trees collected under different aerosol optical thicknesses, trees collected under cirrus cloud conditions, trees viewed in the forward and backward scan directions, trees on sandy soils, trees on clay soils, trees on burn scars, trees in laterite areas and trees in riverine settings. Our training data were collected by one team member and are a carefully selected manual delineation of 89,899 individual trees under a range of atmospheric conditions, viewing perspectives and ecological settings.All multispectral and panchromatic bands associated with our Maxar images were orthorectified to a common mapping basis. We next pan-sharpened all multispectral bands to the 0.5-m scale with the associated panchromatic band. The absolute locational uncertainty of pixels at the 0.5-m scale from orbit is approximately ±11 m, considering the root-mean-square location errors among the QuickBird-2, GeoEye-1, WorldView-2 and WorldView-3 satellites (Extended Data Table 1). We formed the normalized difference vegetation index (NDVI)39 from every image in the traditional way from the pan-sharpened red and near-infrared bands. We also associated the panchromatic band with the NDVI band and ensured that the panchromatic and NDVI bands were highly co-registered. The NDVI was used to distinguish tree crowns from non-vegetated background because the images were taken from a period when only woody plants were photosynthetically active in this area36. Our training data were labelled on images from the early dry season when only trees have green leaves. Because most semi-arid savannah trees continue to photosynthesize in the early dry season after herbaceous vegetation senesces, green leaf tree crowns are easily mapped because of their higher NDVI values than their senescent herbaceous vegetation surroundings. We substantiate this by analysis of 308 individual trees using NDVI time series with 4-m PlanetScope imagery that emphasized the importance of satellite data from the November, December and January early dry-season months (Extended Data Fig. 1).We next formed our data into mosaics by applying a set of decision rules, resulting in a collection of 16 × 16-km tiles within each UTM zone from 9.5° N to 24° N latitude for Africa. The initial round of scoring considered percentage cloud cover, sun elevation angle and sensor off-nadir angle: preference was given to imagery that had lower cloud cover, then higher sun elevation angle and finally view angles closest to nadir. In the second round of scoring, selections were assigned priority to favour early dry-season months and off-nadir view angles: preference was given to imagery from November, December and January with off-nadir angle less than ±15°; second to imagery from November to January with off-nadir angle between ±15° and ±30°; third to imagery from February or March with off-nadir angle less than ±15°; and finally to imagery from February or March with off-nadir angle between ±15° and ±30°. Image mosaics were necessary to eliminate multiple counting of trees. We formed mosaics using 94,502 images for tree segmentation, with 94% of these being from November, December and January. Ninety percent of our selected mosaic imagery was within ±15° of nadir, 87% were acquired between 2010 and 2020 and 94% were from the early dry season (Extended Data Fig. 7). A summary of month, year, solar elevation and off-nadir angle by UTM zone can be found in Supplemental Information Fig. 1.Possible obscuration of the surface by clouds totalled 4.1% of our input mosaic data area and aerosol optical depth >0.6 at 470-nm (ref. 40) areas totalled 3.4% of our input data. However, we mapped 691,477,772 trees in our possible cloud-cover-affected and aerosol-affected areas, indicating that cloud and aerosol effects were lower than these numbers. In addition, 0.9% of our input data did not process. We include a data layer in our viewer for these three conditions.Mapping tree crowns with deep learningWe used convolutional neural network models developed by a previous study1. The models were trained with manually delineated and annotated 89,899 individual trees along a north–south gradient from 0 to 1,000 mm year−1 rainfall1. Only features that showed a distinct crown area and associated shadow were included, which excluded small bushes, grass tussocks, rocks and other features that might have green leaves or cast a shadow from our classification. All training data and model training was done in UTM zones 28 and 29. Because tree floristic diversity in the 0–1,000 mm year−1 zone of our study is highly similar from the Atlantic Ocean to the Red Sea across Africa41,42,43, we added no further training data as our study moved further eastward. We used state-of-the-art deep learning to segment trees crowns at the 50-cm scale1. We used two different models based on a U-Net architecture, one for lower-rainfall desert regions with 150 mm year−1. Details about the network architecture, training process and hyperparameter choices can be found in ref. 1. Previous evaluation showed that early dry-season images performed better than late dry-season images, which was a limitation of our previous study. We reduced this error by using early dry-season images with only 6% of our area being covered by images from February and March. The models were also designed to separate clumped trees by highlighting spaces between different crowns during the learning process, similar to a strategy for separating touching cells in microscopic imagery22.AllometryVery-high-resolution satellite images and deep learning have achieved mapping of individual trees over large areas1. Each tree is georeferenced in the satellite data and defined by crown area. The challenge was to develop allometric equations for foliage, wood and root dry masses or carbon based on crown area regardless of species. This was met by reanalysing existing Sahelian and Sudanian woody plant data from destructive sampling. Overall, the seasonal maximum foliage, wood and root dry masses were measured on 900, 698 and 26 trees or shrubs from 27, 26 and 5 species, respectively, for which crown area was also measured. Several allometric regression models tested for foliage, wood or root masses are power functions and independent of species. All the regression outputs were inter-compared for fit indicators, by systematic estimates of prediction uncertainty and by root-to-wood ratios and foliage-to-wood ratios over the range of crown areas. This resulted in a set of ordinary least squares log–log equations with crown area as the independent variable. The Sahelian and Sudanian allometry equations were also compared with published allometry equations for tropical trees, primarily from more humid tropics, which are generally based on stem diameter, tree height and wood density. Our allometric predictions are within the range of other allometry predictions, reinforcing the confidence in their use beyond the Sahelian and Sudanian domains into sub-humid savannahs for discrete trees19.On the basis of ref. 19, we predicted the wood (w), foliage (f) and root (r) dry mass as functions of the crown area (A) of a single tree as:$$begin{array}{c}{text{mass}}_{{rm{w}}}(A)=3.9448times {A}^{1.1068},({N}_{{rm{w}}}=698)\ {text{mass}}_{{rm{f}}}(A)=0.2693times {A}^{0.9441},({N}_{{rm{f}}}=900)\ {text{mass}}_{{rm{r}}}(A)=0.8339times {A}^{1.1730},({N}_{{rm{r}}}=26)end{array}$$The tree mass components of wood, leaves and roots were combined to predict the total mass(A) in kg of a tree from its crown area A in m2:$$text{mass}left(Aright)={text{mass}}_{{rm{w}}}left(Aright)+{text{mass}}_{{rm{f}}}left(Aright)+{text{mass}}_{{rm{r}}}left(Aright)$$As in ref. 1, a crown area of size A  > 200 m2 was split into ({rm{lfloor }}A/100{rm{rfloor }}) areas of size 100 m2 and one area with the remaining m2 if necessary. We converted dry mass to carbon by multiplying with a factor of 0.47 (ref. 44).Uncertainty analysisWe evaluated the uncertainty of our tree crown area mapping and carbon estimation in two ways. First, we quantified our tree crown mapping omission and commission errors by inspecting randomly selected areas from UTM zones 28–37, validating that our neural network generalized over UTM zones consistently (Extended Data Fig. 8).Second, we quantified the relative error of our tree crown area estimation. We consider the uncertainty Δx of a quantity x and the corresponding relative uncertainty δx defined by the absolute and relative error, respectively45. To assess the relative error in crown area estimation resulting from errors by the neural network, we considered external validation data from ref. 1, which were not used in the model-building process. We considered expert-labelled tree crowns as well as the predicted tree crowns from 78 plots of 256 × 256 pixels. The hand-labelled set contained 5,925 trees and the system delineated 5,915 trees. The total hand-labelled tree crown area was 118,327 m2 and the neural network predicted 121,898 m2. This gave a relative error in crown area mapping of δarea = 3.3%. We matched expert-labelled and predicted tree crowns and computed the root-mean-square error (RMSE) per tree, taking overlapping areas and missed trees into account (see Extended Data Fig. 8). We estimated the allometric uncertainty (δallometric) using the data from ref. 19 (see below). The two relative errors δarea and δallometric were combined to an overall uncertainty estimate for the carbon prediction of ±19.8% (see below).Omission and commission errorsWe evaluated our tree crown mapping accuracy by analysis of 1,028 randomly selected 512 × 256-pixel areas over the 9.5° N to 24° N latitude within UTM zones 28–37. Because the drier 60% of our study area only contains 1% of the 9,947,310,221 trees we mapped in the 0–1,000 mm year−1 rainfall zone, we applied an 80% bias for selecting evaluation areas above the 200 mm year−1 precipitation line46, as >98% of tree identifications were above the 200 mm year−1 precipitation isoline. Identified tree polygons were further categorized into tree crown area classes from 0–15 m2, 15–50 m2, 50–200 m2 and >200 m2, with a total of 50,570 trees evaluated. Although a previous study reported greatest uncertainty in both the smallest and largest area classes1, our more expansive work found the greatest uncertainty in our smallest tree class. We excluded from evaluation any tiles that had annual precipitation46 >1,000 mm year−1 and all areas that were devoid of vegetation, leaving us with 850 areas.Seven members of our team evaluated the accuracy in terms of commission and omission by tree crown area classes for the 850 areas. Input data provided for every area were the NDVI layer, the panchromatic shadow layer and the neural net mapping results in each of the four crown area classes. Ancillary data available to evaluators included the centre coordinates for comparison with Google Earth data, the Funk et al.46 rainfall, the acquisition date of the area evaluated and the viewing perspective.We identified areas wrongly classified as tree crowns (commission errors), missed trees (omission errors) and crown areas corresponding to clumped trees (Extended Data Fig. 8). Clumped trees were most common for >200 m2 tree crown area. They were rare in the 3–15 m2 and 15–50 m2 tree classes, which comprise 88% of our tree crowns. In the 850 patches, the number of trees ranged from one tree to 326 trees, with a total of 50,570 trees evaluated and 3,765 errors identified. Overall, the commission and omission error rates were 4.9% and 2.7%, respectively, a net uncertainty of 2.2%.Allometric uncertainty estimationThe prediction of tree carbon from the crown area for a single tree based on crown area alone is inherently uncertain47,48. As the allometric equations are based on three different datasets, we compute their uncertainties independently, combine them and put them in relation to the total carbon measured in the three datasets.The allometric equations were established using an optimal least-squares fit of an affine linear model predicting the logarithmic carbon from the logarithmic tree crown area19. To estimate the uncertainty of the allometric equations, we repeated the fitting using random subsampling. The datasets were randomly split into training data (80%) for fitting the allometric equations and validation data (20%) for assessing the uncertainty. For example, from the root measurements, (({A}_{1},{y}_{1}),ldots ,({A}_{{N}_{{rm{r}}}},,{y}_{{N}_{{rm{r}}}})), we compute ({mu }_{{rm{r}}}=frac{1}{{N}_{{rm{r}}}}mathop{sum }limits_{i=1}^{{N}_{{rm{r}}}}{y}_{i}) and ({hat{mu }}_{{rm{r}}}=frac{1}{{N}_{{rm{r}}}}mathop{sum }limits_{i=1}^{{N}_{{rm{r}}}}{text{mass}}_{{rm{r}}}({A}_{i})). The corresponding error is ({varDelta }_{{rm{r}}}=|{mu }_{{rm{r}}}-{hat{mu }}_{{rm{r}}}|).Because the total carbon for a tree with a certain crown area is the sum of the three carbon components, we add the absolute uncertainties assuming independence45.$${varDelta }_{{rm{a}}{rm{l}}{rm{l}}{rm{o}}{rm{m}}{rm{e}}{rm{t}}{rm{r}}{rm{i}}{rm{c}}}simeq sqrt{{varDelta }_{{rm{f}}}^{2}+{varDelta }_{{rm{w}}}^{2}+{varDelta }_{{rm{r}}}^{2}}$$and compute the relative uncertainty as ({delta }_{text{allometric}}=frac{{varDelta }_{text{allometric}}}{{mu }_{text{mass}}}), in which the average mass μmass is given by the sum of the averages for wood (μw), leaves (μf) and root (μr). This process was repeated ten times, resulting in a mean relative uncertainty of$${bar{delta }}_{{rm{allometric}}}=19.5 % .$$Total carbon uncertaintyWe combine the uncertainties from the neural net mapping and our allometric equations, which can be viewed as considering (1 + A)·(1 + B) with A and B being random variables with standard deviations δarea and δallometric. Neglecting higher-order and interaction terms, we combine the two sources of uncertainty to (delta simeq sqrt{{delta }_{{rm{area}}}^{2}+{bar{delta }}_{{rm{allometric}}}^{2}}), resulting in an uncertainty in total tree carbon for our study of ±19.8%. See also Extended Data Fig. 9 for the RMSEs of our predicted crown areas calculated on external validation data from ref. 1, binned on the basis of the 50th quantiles of the hand-labelled crown areas and converted also into carbon. Extended Data Fig. 10 is a flow diagram summarizing our methods.Our viewerVisualizing our large tree-mapping dataset in an interactive format was essential for quality-control purposes, exploration of the data and hypothesis creation. Creating a web-based viewer serves the purpose of being the initial point of interaction with our dataset for fellow researchers, local stakeholders or the general public. The visualization of more than 10 billion trees in a web browser required maintaining performance, interactivity and individual metadata for each polygon. Users should be able to zoom in to any area within the dataset to view individual tree polygons and query their statistics while at the same time accurately depicting the overall trends of the dataset at lower zoom levels. The visualization also needed to clearly denote where data were missing or possibly affected by clouds or aerosols. Finally, the extent and origin of the source imagery, its acquisition date and a preview of the imagery needed to be available. To accomplish these goals, a vector-tile-based approach was taken, with the data visualized in a Mapbox GL JS map within a React web application. To create vector tiles covering the entire study area, we developed a data-processing pipeline using high-performance computing resources to transform the data into compatible formats, as well as to package, optimize and combine the vector tiles themselves.We used two tracks to store and visualize the results of this study on the web: vector polygon data and generalized rasters representing tree crown density. At the native spatial resolution of 50 cm, the map shows the full-resolution tree polygon dataset. At lower-spatial-resolution zoom levels, rasterized representations of tree density are shown. Visualizing generalized rasters in place of vector polygons improves performance substantially. As users zoom in to higher spatial resolutions, the raster layer fades away and is replaced by the full-resolution polygon layer. Once zoomed far enough to resolve individual polygons, users can click to select a polygon to show a map overlay containing various properties of the tree, as well as the date on which the source imagery was acquired and a link to preview the source imagery.Rainfall dataWe used the rainfall data of Funk et al. to estimate annual rainfall at 5.6-m grids46. We averaged the available data from 1982 to 2017 and extracted the mean annual rainfall for each mapped tree and bilinearly interpolated it to 100 × 100-m resolution. The rainfall data were also used to classify the study area into mean annual precipitation zones: hyper-arid from 0–150 mm year−1, arid from 150–300 mm year−1, semi-arid from 300–600 mm year−1 and sub-humid from 600–1,000 mm year−1 zones. The rainfall data are found at https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_monthly/ (ref. 46). More

  • in

    Carbon stocks of billions of individual African dryland trees estimated

    Tucker, C. et al. Nature 615, 80–86 (2023).Article 

    Google Scholar 
    Bayala, J. et al. Agric. Ecosyst. Environ. 205, 25–35 (2015).Article 

    Google Scholar 
    Keesstra, S. D. et al. Soil 2, 111–128 (2016).Article 

    Google Scholar 
    Dewi, S. et al. Int. J. Biodivers. Sci. Ecosyst. Serv. Mgmt 13, 312–329 (2017).Article 

    Google Scholar 
    Ahlström, A. et al. Science 348, 895–899 (2015).Article 
    PubMed 

    Google Scholar 
    Poulter, B. et al. Nature 509, 600–603 (2014).Article 
    PubMed 

    Google Scholar 
    Prăvălie, R. et al. Environ. Res. 201, 111580 (2021).Article 
    PubMed 

    Google Scholar 
    Reij, C. P. & Smaling, E. M. A. Land Use Policy 25, 410–420 (2008).Article 

    Google Scholar 
    Zomer, R. J., Bossio, D. A., Trabucco, A., van Noordwijk, M. & Xu, J. Circ. Agric. Syst. 2, 3 (2022).Article 

    Google Scholar 
    Chomba, S., Sinclair, F., Savadogo, P., Bourne, M. & Lohbeck, M. Front. For. Glob. Change 3, 571679 (2020).Article 

    Google Scholar 
    Dakpogan, A., Bayala, J., Ouattara, I. & Ellington, E. in United for Lands: From National Coalitions to a Pipeline of Bankable Projects for the Great Green Wall 54–56 (United Nations, 2022).
    Google Scholar 
    Garrity, D. P. & Bayala, J. in Sustainable Development Through Trees on Farms: Agroforestry in its Fifth Decade (ed. van Noordwijk, M.) 153–175 (World Agroforestry, 2019).
    Google Scholar 
    Schnell, S., Kleinn, C. & Ståhl, G. Environ. Monit. Assess. 187, 600 (2015).Article 
    PubMed 

    Google Scholar  More