Philippa Kaur

Di Giacomo, G., Hadrich, J., Hutchison, W. D., Peterson, H. & Rogers, M. Economic impact of spotted wing drosophila (Diptera: Drosophilidae) yield loss on minnesota raspberry farms: A grower survey. J. Integr. Pest Manag. https://doi.org/10.1093/jipm/pmz006 (2019).Article 

Google Scholar 
Farnsworth, D. et al. Economic analysis of revenue losses and control costs associated with the spotted wing drosophila, Drosophila suzukii (Matsumura), in the California raspberry industry. Pest. Manag. Sci. 73, 1083–1090. https://doi.org/10.1002/ps.4497 (2017).CAS 
Article 
PubMed 

Google Scholar 
Beers, E. H., Van Steenwyk, R. A., Shearer, P. W., Coates, W. W. & Grant, J. A. Developing Drosophila suzukii management programs for sweet cherry in the western United States. Pest Manag. Sci. 67, 1386–1395. https://doi.org/10.1002/ps.2279 (2011).CAS 
Article 
PubMed 

Google Scholar 
Tait, G. et al. Drosophila suzukii (Diptera: Drosophilidae): A decade of research towards a sustainable integrated pest management program. J. Econ. Entomol. 114, 1950–1974. https://doi.org/10.1093/jee/toab158 (2021).Article 
PubMed 

Google Scholar 
Daane, K. M. et al. First exploration of parasitoids of Drosophila suzukii in South Korea as potential classical biological agents. J. Pest Sci. 89, 823–835. https://doi.org/10.1007/s10340-016-0740-0 (2016).ADS 
Article 

Google Scholar 
Abram, P. K. et al. New records of Leptopilina, Ganaspis, and Asobara species associated with Drosophila suzukii in North America, including detections of L. japonica and G. brasiliensis. J. Hymenoptera Res. 78, 1–17. https://doi.org/10.3897/jhr.78.55026 (2020).Article 

Google Scholar 
Chabert, S., Allemand, R., Poyet, M., Eslin, P. & Gibert, P. Ability of European parasitoids (Hymenoptera) to control a new invasive Asiatic pest, Drosophila suzukii. Biol. Control 63, 40–47. https://doi.org/10.1016/j.biocontrol.2012.05.005 (2012).Article 

Google Scholar 
Gonzalez-Cabrera, J., Moreno-Carrillo, G., Sanchez-Gonzalez, J. A., Mendoza-Ceballos, M. Y. & Arredondo-Bernal, H. C. Single and Combined Release of Trichopria drosophilae (Hymenoptera: Diapriidae) to Control Drosophila suzukii (Diptera: Drosophilidae). Neotrop. Entomol. 48, 949–956. https://doi.org/10.1007/s13744-019-00707-3 (2019).CAS 
Article 
PubMed 

Google Scholar 
Rossi Stacconi, M. V., Grassi, A., Ioriatti, C. & Anfora, G. Augmentative releases of Trichopria drosophilae for the suppression of early season Drosophila suzukii populations. Biocontrol 64, 9–19. https://doi.org/10.1007/s10526-018-09914-0 (2018).CAS 
Article 

Google Scholar 
Poyet, M. et al. The wide potential trophic niche of the asiatic fruit fly Drosophila suzukii: The key of its invasion success in temperate Europe?. PLoS ONE 10, e0142785. https://doi.org/10.1371/journal.pone.0142785 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mazzetto, F. et al. Drosophila parasitoids in northern Italy and their potential to attack the exotic pest Drosophila suzukii. J. Pest Sci. 89, 837–850. https://doi.org/10.1007/s10340-016-0746-7 (2016).Article 

Google Scholar 
Wang, X. G., Kacar, G., Biondi, A. & Daane, K. M. Foraging efficiency and outcomes of interactions of two pupal parasitoids attacking the invasive spotted wing drosophila. Biol. Control 96, 64–71. https://doi.org/10.1016/j.biocontrol.2016.02.004 (2016).Article 

Google Scholar 
Rossi Stacconi, M. V. et al. Host location and dispersal ability of the cosmopolitan parasitoid Trichopria drosophilae released to control the invasive spotted wing Drosophila. Biol. Control 117, 188–196. https://doi.org/10.1016/j.biocontrol.2017.11.013 (2018).Article 

Google Scholar 
Esteban-Santiago, J. M., Rodríguez-Leyva, E., Lomeli-Flores, J. R. & González-Cabrera, J. Demographic parameters of Trichopria drosophilae in three host species. Entomol. Exp. Appl. 169, 330–337. https://doi.org/10.1111/eea.13026 (2021).CAS 
Article 

Google Scholar 
Häussling, B. J. M., Lienenluke, J. & Stokl, J. The preference of Trichopria drosophilae for pupae of Drosophila suzukii is independent of host size. Sci. Rep. 11, 995. https://doi.org/10.1038/s41598-020-80355-5 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, X. G., Kacar, G., Biondi, A. & Daane, K. M. Life-history and host preference of Trichopria drosophilae, a pupal parasitoid of spotted wing drosophila. Biocontrol 61, 387–397. https://doi.org/10.1007/s10526-016-9720-9 (2016).CAS 
Article 

Google Scholar 
Woltz, J. M. & Lee, J. C. Pupation behavior and larval and pupal biocontrol of Drosophila suzukii in the field. Biol. Control 110, 62–69. https://doi.org/10.1016/j.biocontrol.2017.04.007 (2017).Article 

Google Scholar 
Ballman, E. S., Collins, J. A. & Drummond, F. A. Pupation behavior and predation on Drosophila suzukii (Diptera: Drosophilidae) Pupae in Maine wild blueberry fields. J. Econ. Entomol. 110, 2308–2317. https://doi.org/10.1093/jee/tox233 (2017).Article 
PubMed 

Google Scholar 
Guillén, L., Aluja, M. N., Equihua, M. & Sivinski, J. Performance of two fruit fly (Diptera: Tephritidae) pupal parasitoids (Coptera haywardi [Hymenoptera: Diapriidae] and Pachycrepoideus vindemiae [Hymenoptera: Pteromalidae]) under different environmental soil conditions. Biol. Control 23, 219–227. https://doi.org/10.1006/bcon.2001.1011 (2002).Article 

Google Scholar 
Yi, C. et al. Life history and host preference of Trichopria drosophilae from Southern China, one of the effective pupal parasitoids on the Drosophila species. Insects 11, 103. https://doi.org/10.3390/insects11020103 (2020).Article 
PubMed Central 

Google Scholar 
BoychevaWoltering, S., Romeis, J. & Collatz, J. Influence of the rearing host on biological parameters of Trichopria drosophilae, a potential biological control agent of Drosophila suzukii. Insects. https://doi.org/10.3390/insects10060183 (2019).Article 

Google Scholar 
Otto, M. & Mackauer, M. The developmental strategy of an idiobiont ectoparasitoid, Dendrocerus carpenteri: Influence of variations in host quality on offspring growth and fitness. Oecologia 117, 353–364. https://doi.org/10.1007/s004420050668 (1998).ADS 
Article 
PubMed 

Google Scholar 
Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (Vienna, Austria, 2008).Johnson, S. N. & Gregory, P. J. Chemically-mediated host-plant location and selection by root-feeding insects. Physiol. Entomol. 31, 1–13. https://doi.org/10.1111/j.1365-3032.2005.00487.x (2006).CAS 
Article 

Google Scholar 
Bezerra Da Silva, C. S., Park, K. R., Blood, R. A. & Walton, V. M. Intraspecific competition affects the pupation behavior of spotted-wing drosophila (Drosophila suzukii). Sci. Rep. 9, 7775. https://doi.org/10.1038/s41598-019-44248-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Renkema, J. M. & Devkota, S. Pupation depth of spotted wing drosophila (Drosophila suzukii) and effects of field sanitation in Florida strawberries. Viii Int. Strawberry Symp. 1156, 849–855. https://doi.org/10.17660/ActaHortic.2017.1156.125 (2017).Article 

Google Scholar 
Tsitsipis, J. A. & Papanicolaou, E. P. Pupation depth in artificially reared olive fruits-flies Dacus-oleae (Diptera, Tephritidae), as affected by several physical characteristics of the substrates. Annales De Zoologie Ecologie Animale 11, 31–40 (1979).
Google Scholar 
Dimou, I., Koutsikopoulos, C., Economopoulos, A. P. & Lykakis, J. Depth of pupation of the wild olive fruit fly, Bactrocera (Dacus) oleae (Gmel.) (Dipt., Tephritidae), as affected by soil abiotic factors. J. Appl. Entomol. 127, 12–17. https://doi.org/10.1046/j.1439-0418.2003.00686.x (2003).Article 

Google Scholar 
de Belle, J. S., Hilliker, A. J. & Sokolowski, M. B. Genetic localization of foraging (for): A major gene for larval behavior in Drosophila melanogaster. Genetics 123, 157–163. https://doi.org/10.1093/genetics/123.1.157 (1989).Article 
PubMed 

Google Scholar 
Sokolowski, M. B. et al. Ecological genetics and behaviour of Drosophila melanogaster larvae in nature. Anim. Behav. 34, 403–408. https://doi.org/10.1016/S0003-3472(86)80109-9 (1986).Article 

Google Scholar 
Rodriguez, L., Sokolowski, M. B. & Shore, J. S. Habitat selection by Drosophila melanogaster larvae. J. Evol. Biol. 5, 61–70. https://doi.org/10.1046/j.1420-9101.1992.5010061.x (1992).Article 

Google Scholar 
McIntosh, H., Atucha, A., Townsend, P. A., Hills, W. B. & Guédot, C. Plastic mulches reduce adult and larval populations of Drosophila suzukii in fall-bearing raspberry. J. Pest. Sci. 95, 525–536. https://doi.org/10.1007/s10340-021-01456-2 (2021).Article 

Google Scholar 
Ballman, E. & Drummond, F. Larval movement of spotted wing drosophila, Drosophila suzukii (Matsumura) (Diptera: Drosophilidae). J. Kansas Entomol. Soc. 92, 412–421. https://doi.org/10.2317/0022-8567-92.1.412 (2019).Article 

Google Scholar  More

In the present work, we extend the base model for the spatial mosquito population dynamics24 to include wild male mosquitoes and genetically modified male mosquitoes. Thus, five populations will be considered: the aquatic mosquito population, including larvae and pupae, the egg mosquito population, the reproductive female mosquito population, the wild male mosquito population, and the genetically modified male population. Similar approaches can be found in the literature25,26.In the following system, we represent mosquito population densities (mosquitoes per m(^2)) by: E – in the egg phase, A – in the aquatic phase, F – female in the reproductive phase, M – wild males, and G – genetically modified male mosquitoes. Due to the very high resistance of the egg phase (up to 450 days27) and as we are interested in an urban spatial macro-scale modeling, we do not consider the mortality in the egg phase. The model is described by the following system of partial differential equations:$$begin{aligned} {left{ begin{array}{ll} partial _t E &{} = alpha beta F M -e E, \ partial _t A &{} = e left( 1 – dfrac{A}{k} right) E -(eta _a+{mu _a})A, \ partial _t F &{} = nabla cdot (D_m nabla F) -mu _f F + reta _{a} A, \ partial _t M &{} = nabla cdot (D_m nabla M) -mu _m M + (1-r)eta _{a} A, \ partial _t G &{} = nabla cdot (D_g nabla G) -mu _{g}G + l, end{array}right. } end{aligned}$$
(1)
where ( alpha ) represents the proportion of wild male mosquitoes to the total number of male mosquitoes (wild males + genetically modified males); (beta ) represents the expected quantity of eggs from the successful encounter between wild females and males; e is the egg hatching rate; k is the carrying capacity of the aquatic phase; ( eta _a ) is the emergence rate for mosquitoes from the aquatic phase to the female or male phases; ( mu _a), (mu _f), (mu _m), and (mu _{g}) are the mortality rates of mosquitoes in the aquatic phase, females, males, and genetically modified males, respectively; r is the proportion of females to males (typically (r=0.5)); (l=l(x,y,t)) is the function representing the number of genetically modified mosquitoes released in a unit of time at any point of the domain; (D_m) is the diffusion coefficient of wild mobiles females and males; (D_g) is the diffusion coefficient of genetically modified males. The proposed model (1) can naturally deal with heterogeneous parameters, such as mortality, diffusion, and carrying capacity coefficients. Thus it is possible to model the influence of rain, wind, and human action. In the context of this work, we are considering that the city neighborhood is divided into two environments: houses and streets. Due to lack of data, we restrict the investigated heterogeneity only to the carrying capacity coefficient.The proposed model can be regarded as an extension of other “economic” models20,24 in the effort to qualitatively reproduce the complex phenomena by using as few parameters as possible. Following this idea, the carrying capacity was neglected in the egg phase because of the skip oviposition phenomenon28 i.e., the female lays the number of eggs that the place holds, without more space, she migrates to other environments to finish laying the eggs. We also do not consider this coefficient in the winged phase as limitations in the winged phase were not reported in any study. On the other hand, we consider it in the aquatic phases (larvae and pupae), where it is effective29.The term ( alpha ), which multiplies the probability of encounters between male and female, represents the impact of the insertion of genetically modified males in the mosquito population to the immobile phase and is defined as$$begin{aligned} alpha = left{ begin{array}{cc} 1, &{} text{ if } M=G= 0, \ dfrac{M}{M + G}, &{} text{ otherwise }. end{array} right. end{aligned}$$
(2)
Similar modeling approach can be found in the literature16. As the release rate of genetically modified males increases, the alpha value decreases, and, consequently, the probability of encounter between females and wild males also decreases. Thus, there is a greater probability of encounter between genetically modified males and females. This approach presents an advantage, when compared to the models found in the literature25, as System  (1) does not present singularities at the equilibrium states, allowing mathematical analysis and numerical simulations. From the biological point of view, the increment of male wild mosquitoes over some critical value does not affect the egg deposition. At first glance, the term FM can lead to a misunderstanding that such property is not satisfied in the presented model. However, in Section “Equilibrium points considering the application of genetically modified male mosquitoes,” we argue that both male and female populations possess mathematical attractor equilibria, blocking the wild male population from growing beyond this value.Finally, any acceptable population model should be invariant in the definition domain, meaning its solution does not present senseless values. Setting the variable domain as$$begin{aligned} 0 le E(x,y,t)< infty ,;; 0 le A(x,y,t) le k, ;; 0 le F(x,y,t)< infty ,;; 0 le M(x,y,t)< infty ,;; 0 le G(x,y,t) < infty , end{aligned}$$ (3) we can verify that it is invariant under the time evolution by the System (1). To prove this statement, it is sufficient to verify that the vector field defined by the right side of (1) points into the domain when (E, A, F, M, G) approaches the domain boundary. When E approaches zero, the right side of the first equation in (1) is not negative. When A approaches zero, the right side of the second equation in (1) is not negative. When A approaches k (bottom), the first term on the right side of the second equation in (1) tends to zero, while the second term remains negative. Since the term ( nabla cdot (D_m nabla F) ) cannot change the F sign, when F approaches zero, the right side of the third equation in (1) is not negative Since the term ( nabla cdot (D_m nabla M) ) cannot change the M sign, when M approaches zero, the right side of the fourth equation in (1) is not negative. Since the term ( nabla cdot (D_g nabla G) ) cannot change the G sign, when G approaches zero, the right side of the fifth equation in (1) is not negative. In the rest of this section, let us explain how to estimate one-by-one all the parameters used in this model from experimental data available in the literature. It is a challenging task as, typically, the development of the Ae. aegypti mosquito depends on food variation30, temperature variations14,15 and rainfall31. This data is not available in the literature in the organized and systematic form. Because of that, we assume the environment is under optimal conditions of temperature, availability of food, and humidity.How to estimate emergence rate ((eta _a)) The emergence rate describes the rate at which the aquatic phase of the mosquito emerges into the adult phases. In the present model, for simplicity, it was considered that no mosquito from the crossing between genetically modified males and females reaches adulthood. Thus, the emergence rate is calculated on the crossing between females and wild males. Under optimal conditions and feeding distribution, based on the literature30, the emergence rate is 0.5596 (text{ day}^{-1}).How to estimate diffusion coefficients ((D_m,D_g)) The diffusion coefficient is one of the most important parameters describing the mosquitoes’ movement. We use the methodology proposed in the previous work24 to obtain the diffusion coefficient of adult mosquitoes (females and males) and genetically modified males.The estimate is done by assuming that all mosquitoes are released at (0, 0), and their movement is described by the corresponding equation in (1) neglecting other terms than diffusion. The population starts spreading in all directions. We define the spreading distance R(t) as the radius of the region centered in (0, 0) where (90%) of the initial mosquitoes population density is present. In Silva et al.24 it is shown that$$begin{aligned} R(t) = sqrt{4Dt} ;text {erf}^{-1}(0.9). end{aligned}$$ (4) Now corresponding diffusion coefficient is estimated by using the average flight distance of the mosquitoes and the characteristic time related to their life expectancy. Under favorable weather conditions, the average lifetime flight distance of females and males is approximately32,33 65 m, while the same for GM males is34 67.3 m. Based on the literature, we consider that the characteristic time for wild females and males32 is 7 days, and the same for genetically modified males is34 2.17 days. Using (4) we estimate the values for (D_m) and (D_g) summarized in Table 1. It would be natural to consider that the mosquitoes’ movement changes in different environments. Unfortunately, we were unable to find the corresponding experimental data, and because of that, we considered that (D_m) and (D_g) are the same in streets and house blocks.How to estimate mortality rates ((mu _a), (mu _f), (mu _m), (mu _{g}))The mortality coefficient represents an average quantity of mosquitoes in the corresponding phase dying each day. As mentioned before, we disregard the mortality rate in the egg phase, as it is negligible due to its great durability27, it does not affect the numerical results, and it complicates analytical estimates. Thus, the aquatic phase mortality rate coefficient is equal to the same for larvae’s coefficient, which is approximately29 (mu _a = 0.025) (1/day).There is no solid agreement on the mortality rate of male and female wild mosquitoes in the literature. Although some results29,30 suggest they are similar, we follow these authors and consider them equal. Considering both natural death and accidental ones, approximately (10%) of females and male mosquitoes in the adult phase die at each day35. Under optimal conditions, the mortality coefficient can be estimated from this data by using the proposed model (1) by neglecting diffusion and emergence terms in the corresponding equation; details can be found in the previous work24. The resulting parameter values are summarized in Table 1.It would be natural to consider that the mosquitoes mortality rate depends on the environment. Unfortunately, we were unable to find the corresponding experimental data, and because of that, we considered that (mu _a), (mu _f), (mu _m), and (mu _{g}) are the same in streets and house blocks.How to estimate the expected egg number ((beta ))This coefficient represents the average quantity of eggs a wild female lays per day, assuming a successful meeting with a wild male. Considering the number of times a female lays eggs in its lifetime36, the average quantity of eggs per lay and the mosquito’s life expectancy, under favorable conditions, this coefficient is estimated as (beta = 34).How to estimate the hatching rate (e)This coefficient determines the average number of eggs hatching in one day. Experimental data37 suggest that, under optimal humidity conditions, the mean value of the hatch rate coefficient is 0.24 given a temperature of 28 ((^{circ })C), which is considered ideal for mosquito development. This is the value used in the present work.How to estimate carrying capacity coefficient (k)The carrying capacity k represents the space limitation of one phase due to situations present in the environment37,38, such as competition for food among the larvae39. In general, it depends on external factors such as food availability, climate, terrain properties, making direct estimation almost impossible. In the Analytical results section, we show how to estimate this coefficient for each grid block. When considering spatial population dynamics in a heterogeneous environment, carrying capacity is one of the most influential parameters as it varies significantly. For example, house block offer more food and a shelter against natural predators resulting to a larger carrying capacity when compared with street environment. Following the literature32 we assume that the 80% of the mosquito’s breeding places are in houses resulting in the relation (k_h=5k_s), where (k_h) and (k_s) are the carrying capacities of the house blocks and in the streets.Genetically modified mosquitoes release rate (l)Function l(x, y, t) determines how many genetically modified mosquitoes are released in the location (x, y) at time t.In a normal situation, the sex ratio between males and females is 1 : 1. The increment of this proportion favoring GM males increases the probability of females to mate with these mosquitoes. As reported in the literature12,30 the initial launch size is 11 times larger than the adult female population, and it is done in some spots in the city. In this work, we analyze different release strategies maintaining the (11times 1) proportion in some scenarios.Table 1 All parameter values are directly taken or estimated from the literature as explained in section Modeling.Full size table More

Marzluff, J. M. Worldwide urbanization and its effects on birds. In Avian Ecology and Conservation in an Urbanizing World (eds Marzluff, J. et al.) 19–47 (Springer, Boston, 2001).Chapter 

Google Scholar 
McKinney, M. L. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst. 11, 161–176 (2008).Article 

Google Scholar 
Luniak, M. Synurbization–adaptation of animal wildlife to urban development in Proceedings 4th international urban wildlife symposium (eds. Shaw, W., Harris, L.,Vandruff, L.) 50–55 (University of Arizona, Tucson, ARI, 2004).Isaksson, C. Impact of urbanization on birds in Bird Species how they arise, modify and vanish (ed. Tietze D. T.) 235–257 (Springer, 2018).Minias, P. Successful colonization of a novel urban environment is associated with an urban behavioural syndrome in a reed-nesting waterbird. Ethology 121, 1178–1190 (2015).Article 

Google Scholar 
Møller, A. P. et al. Urban habitats and feeders both contribute to flight initiation distance reduction in birds. Behav. Ecol. 26, 861–865 (2015).Article 

Google Scholar 
Jokimäki, J. & Suhonen, J. Distribution and habitat selection of wintering birds in urban environments. Landsc. Urban Plan. 39, 253–263 (1998).Article 

Google Scholar 
Francis, R. A. & Chadwick, M. A. What makes a species synurbic?. Appl. Geogr. 32, 514–521 (2012).Article 

Google Scholar 
Møller, A. P. et al. High urban population density of birds reflects their timing of urbanization. Oecologia 170, 867–875 (2012).PubMed 
Article 
ADS 

Google Scholar 
Tella, J. L. et al. Offspring body condition and immunocompetence are negatively affected by high breeding densities in a colonial seabird: A multiscale approach. Proc. R. Soc. B 268, 1455–1461 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Savoca, M. S., Bonter, D. N., Zuckerberg, B., Dickinson, J. L. & Ellis, J. C. Nesting density is an important factor affecting chick growth and survival in the Herring Gull. Condor 113, 565–571 (2011).Article 

Google Scholar 
Minias, P., Włodarczyk, R. & Janiszewski, T. Opposing selective pressures may act on the colony size in a waterbird species. Evol. Ecol. 29, 283–297 (2015).Article 

Google Scholar 
Kamiński, M. et al. Density-dependence of nestling immune function and physiological condition in semi-precocial colonial bird: A cross-fostering experiment. Front. Zool. 18, 7 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ward, P. & Zahavi, A. The importance of certain assemblages of birds as “information-centres” for food-finding. Ibis 115, 517–534 (1973).Article 

Google Scholar 
Danchin, E. & Wagner, R. H. The evolution of coloniality: The emergence of new perspectives. Trends Ecol. Evol. 12, 342–347 (1997).CAS 
PubMed 
Article 

Google Scholar 
Brown, C. R. & Brown, M. B. Coloniality in the Cliff Swallow: The Effect of Group Size on Social Behavior (University of Chicago Press, 1996).
Google Scholar 
Evans, J. C., Votier, S. C. & Dall, S. R. Information use in colonial living. Biol. Rev. 91, 658–672 (2016).PubMed 
Article 

Google Scholar 
Brown, C. R. & Brown, M. B. Avian coloniality. In Current Ornithology (eds Brown, C. R. & Brown, M. B.) 1–82 (Springer, Boston, 2001).
Google Scholar 
Coulson, J. C., Duncan, N. & Thomas, C. Changes in the breeding biology of the herring gull (Larus argentatus) induced by reduction in the size and density of the colony. J. Anim. Ecol. 51, 739–756 (1982).Article 

Google Scholar 
Ots, I. & Horak, P. Great tits Parus major trade health for reproduction. Proc. R. Soc. B. 263, 1443–1447 (1996).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Richner, H. & Tripet, F. Ectoparasitism and the trade-off between current and future reproduction. Oikos 86, 535–538 (1999).Article 

Google Scholar 
Fokkema, R. W., Ubels, R. & Tinbergen, J. M. Great tits trade off future competitive advantage for current reproduction. Behav. Ecol. 27, 1656–1664 (2016).
Google Scholar 
Horak, P. & Leberton, J. D. Survival of adult Great Tits Parus major in relation to sex and habitat; a comparison of urban and rural populations. Ibis 140, 205–209 (1998).Article 

Google Scholar 
Stracey, C. M. & Robinson, S. K. Are urban habitats ecological traps for a native songbird? Season-long productivity, apparent survival, and site fidelity in urban and rural habitats. J. Avian Biol. 43, 50–60 (2012).Article 

Google Scholar 
Sepp, T., McGraw, K. J., Kaasik, A. & Giraudeau, M. A review of urban impacts on avian life-history evolution: Does city living lead to slower pace of life?. Glob. Change Biol. 24, 1452–1469 (2018).Article 
ADS 

Google Scholar 
Phillips, J. N., Gentry, K. E., Luther, D. A. & Derryberry, E. P. Surviving in the city: Higher apparent survival for urban birds but worse condition on noisy territories. Ecosphere 9, e02440 (2018).Article 

Google Scholar 
Johnston, R. F. & Janiga, M. Feral Pigeons (Oxford University Press on Demand, 1995).
Google Scholar 
Giunchi, D., Mucci, N., Bigi, D., Mengoni, C. & Baldaccini, N. E. Feral pigeon populations: Their gene pool and links with local domestic breeds. Zoology 142, 125817 (2020).PubMed 
Article 

Google Scholar 
Sol, D. Artificial selection, naturalization, and fitness: Darwin’s pigeons revisited. Biol. J. Linn. Soc. 93, 657–665 (2008).Article 

Google Scholar 
Giunchi, D., Albores-Barajas, Y. V., Baldaccini, N. E., Vanni, L. & Soldatini, C. Feral pigeons: Problems, dynamics and control methods. In Integrated Pest Management and Pest Control. Current and Future Tactics (eds Soloneski, S. & Larramendy, M.) 215–240 (InTechOpen, London, 2012).
Google Scholar 
Senar, J. C., Navalpotro, H., Pascual, J. & Montalvo, T. Nicarbazin has no effect on reducing feral pigeon populations in Barcelona. Pest Manag. Sci. 77, 131–137 (2021).CAS 
PubMed 
Article 

Google Scholar 
Rose, E., Nagel, P. & Haag-Wackernagel, D. Spatio-temporal use of the urban habitat by feral pigeons (Columba livia). Behav. Ecol. Sociobiol. 60, 242–254 (2006).Article 

Google Scholar 
Corbel, H. et al. Stress response varies with plumage colour and local habitat in feral pigeons. J. Ornithol. 157, 825–837 (2016).Article 

Google Scholar 
Møller, A. P., Merino, S., Brown, C. R. & Robertson, R. J. Immune defense and host sociality: A comparative study of swallows and martins. Am. Nat. 158, 136–145 (2001).PubMed 
Article 

Google Scholar 
Drzewińska-Chańko, J. et al. Immunocompetent birds choose larger breeding colonies. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13540 (2021).Article 
PubMed 

Google Scholar 
Saino, N., Suffritti, C., Martinelli, R., Rubolini, D. & Møller, A. P. Immune response covaries with corticosterone plasma levels under experimentally stressful conditions in nestling barn swallows (Hirundo rustica). Behav. Ecol. 14, 318–325 (2003).Article 

Google Scholar 
Goutte, A. et al. Long-term survival effect of corticosterone manipulation in black-legged kittiwakes. Gen. Comp. Endocrinol. 167, 246–251 (2010).CAS 
PubMed 
Article 

Google Scholar 
Møller, A. P., Christe, P., Erritzøe, J. & Mavarez, J. Condition, disease and immune defence. Oikos 83, 301–306 (1998).Article 

Google Scholar 
Navarro, C., Marzal, A., De Lope, F. & Møller, A. P. Dynamics of an immune response in house sparrows Passer domesticus in relation to time of day, body condition and blood parasite infection. Oikos 101, 291–298 (2003).Article 

Google Scholar 
Toïgo, C., Gaillard, J. M., Van Laere, G., Hewison, M. & Morellet, N. How does environmental variation influence body mass, body size, and body condition? Roe deer as a case study. Ecography 29, 301–308 (2006).Article 

Google Scholar 
Jacquin, L. et al. A potential role for parasites in the maintenance of color polymorphism in urban birds. Oecologia 173, 1089–1099 (2013).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Meillère, A., Brischoux, F., Parenteau, C. & Angelier, F. Influence of urbanization on body size, condition, and physiology in an urban exploiter: A multi-component approach. PLoS ONE https://doi.org/10.1371/journal.pone.0135685 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: The scaled mass index as an alternative method. Oikos 118, 1883–1891 (2009).Article 

Google Scholar 
Jacquin, L. et al. Melanin-based coloration is related to parasite intensity and cellular immune response in an urban free living bird: The feral pigeon Columba livia. J. Avian Biol. 42, 11–15 (2011).Article 

Google Scholar 
Liker, A., Papp, Z., Bókony, V. & Lendvai, A. Z. Lean birds in the city: Body size and condition of house sparrows along the urbanization gradient. J. Anim. Ecol. 77, 789–795 (2008).CAS 
PubMed 
Article 

Google Scholar 
Audet, J. N., Ducatez, S. & Lefebvre, L. The town bird and the country bird: Problem solving and immunocompetence vary with urbanization. Behav. Ecol. 27, 637–644 (2016).Article 

Google Scholar 
Kurucz, K., Purger, J. J. & Batáry, P. Urbanization shapes bird communities and nest survival, but not their food quantity. Glob. Ecol. Conserv. 26, e01475 (2021).Article 

Google Scholar 
Partecke, J., Schwabl, I. & Gwinner, E. Stress and the city: Urbanization and its effects on the stress physiology in European blackbirds. Ecology 87, 1945–1952 (2006).PubMed 
Article 

Google Scholar 
Bailly, J. et al. Negative impact of urban habitat on immunity in the great tit Parus major. Oecologia 182, 1053–1062 (2016).PubMed 
Article 
ADS 

Google Scholar 
Glądalski, M. et al. Differences in use of bryophyte species in tit nests between two contrasting habitats: An urban park and a forest. Eur. Zool. J. 88, 807–815 (2021).Article 

Google Scholar 
Tella, J. L., Scheuerlein, A. & Ricklefs, R. E. Is cell–mediated immunity related to the evolution of life-history strategies in birds?. Proc. R. Soc. B 269, 1059–1066 (2002).PubMed 
PubMed Central 
Article 

Google Scholar 
Brown, C. R. & Brown, M. B. Empirical measurement of parasite transmission between groups in a colonial bird. Ecology 85, 1619–1626 (2004).Article 

Google Scholar 
O’Brien, V. A. & Brown, C. R. Group size and nest spacing affect Buggy Creek virus (Togaviridae: Alphavirus) infection in nestling house sparrows. PLoS ONE 6, e25521 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Wilcoxen, T. E. et al. Effects of bird-feeding activities on the health of wild birds. Conserv. Physiol. 3, cov058 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Moyers, S. C., Adelman, J. S., Farine, D. R., Thomason, C. A. & Hawley, D. M. Feeder density enhances house finch disease transmission in experimental epidemics. Philos. Trans. R. Soc. B 373, 20170090 (2018).Article 
CAS 

Google Scholar 
Møller, A. P. Successful city dwellers: A comparative study of the ecological characteristics of urban birds in the Western Palearctic. Oecologia 159, 849–858 (2009).PubMed 
Article 
ADS 

Google Scholar 
Watson, H., Videvall, E., Andersson, M. N. & Isaksson, C. Transcriptome analysis of a wild bird reveals physiological responses to the urban environment. Sci. Rep. 7, 44180 (2017).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Hasselquist, D. & Nilsson, J. Å. Physiological mechanisms mediating costs of immune responses: What can we learn from studies of birds?. Anim. Behav. 83, 1303–1312 (2012).Article 

Google Scholar 
Biard, C., Monceau, K., Motreuil, S. & Moreau, J. Interpreting immunological indices: The importance of taking parasite community into account. An example in blackbirds Turdus merula. Methods Ecol. Evol. 6, 960–972 (2015).Article 

Google Scholar 
Leclaire, S., Czirják, G. Á., Hammouda, A. & Gasparini, J. Feather bacterial load shapes the trade-off between preening and immunity in pigeons. BMC Evol. Biol. 15, 60 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Vinkler, M., Adelman, J. S. & Ardia, D. R. Evolutionary and ecological immunology. In Avian Immunology 3rd edn (eds Kaspers, B. et al.) 519–558 (Academic Press, London, 2021).
Google Scholar 
Davis, A. K., Maney, D. L. & Maerz, J. C. The use of leukocyte profiles to measure stress in vertebrates: A review for ecologists. Funct. Ecol. 22, 760–772 (2008).Article 

Google Scholar 
Indykiewicz, P., Podlaszczuk, P., Kamiński, M., Włodarczyk, R. & Minias, P. Central–periphery gradient of individual quality within a colony of Black-headed Gulls. Ibis 161, 744–758 (2019).Article 

Google Scholar 
Vleck, C. M., Vertalino, N., Vleck, D. & Bucher, T. L. Stress, corticosterone, and heterophil to lymphocyte ratios in free-living Adélie penguins. Condor 102, 392–400 (2000).Article 

Google Scholar 
Davis, A. K., Cook, K. C. & Altizer, S. Leukocyte profiles in wild house finches with and without mycoplasmal conjunctivitis, a recently emerged bacterial disease. EcoHealth 1, 362–373 (2004).Article 

Google Scholar 
Lobato, E., Moreno, J., Merino, S., Sanz, J. J. & Arriero, E. Haematological variables are good predictors of recruitment in nestling pied flycatchers (Ficedula hypoleuca). Ecoscience 12, 27–34 (2005).Article 

Google Scholar 
Bobby Fokidis, H., Greiner, E. C. & Deviche, P. Interspecific variation in avian blood parasites and haematology associated with urbanization in a desert habitat. J. Avian Biol. 39, 300–310 (2008).Article 

Google Scholar 
Padgett, D. A. & Glaser, R. How stress influences the immune response. Trends Immunol. 24, 444–448 (2003).CAS 
PubMed 
Article 

Google Scholar 
Dimitrov, S. et al. Cortisol and epinephrine control opposing circadian rhythms in T cell subsets. Blood 113, 5134–5143 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ilmonen, P., Hasselquist, D., Langefors, Å. & Wiehn, J. Stress, immunocompetence and leukocyte profiles of pied flycatchers in relation to brood size manipulation. Oecologia 136, 148–154 (2003).PubMed 
Article 
ADS 

Google Scholar 
Minias, P., Gach, K., Włodarczyk, R. & Janiszewski, T. Colony size affects nestling immune function: A cross-fostering experiment in a colonial waterbird. Oecologia 190, 333–341 (2019).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Cyr, N. E., Earle, K., Tam, C. & Romero, L. M. The effect of chronic psychological stress on corticosterone, plasma metabolites, and immune responsiveness in European starlings. Gen. Comp. Endocrinol. 154, 59–66 (2007).CAS 
PubMed 
Article 

Google Scholar 
Schoech, S. J., Bowman, R. & Reynolds, S. J. Food supplementation and possible mechanisms underlying early breeding in the Florida Scrub-Jay (Aphelocoma coerulescens). Horm. Behav. 46, 565–573 (2004).CAS 
PubMed 
Article 

Google Scholar 
Ibáñez-Álamo, J. D. et al. Physiological stress does not increase with urbanization in European blackbirds: Evidence from hormonal, immunological and cellular indicators. Sci. Total Environ. 721, 137332 (2020).PubMed 
Article 
ADS 
CAS 

Google Scholar 
Bonier, F. Hormones in the city: Endocrine ecology of urban birds. Horm. Behav. 61, 763–772 (2012).CAS 
PubMed 
Article 

Google Scholar 
Valdebenito, J. O. et al. Seasonal variation in sex-specific immunity in wild birds. Sci. Rep. 11, 1349 (2021).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Hetmański, T. Timing of breeding in the Feral Pigeon Columba livia f. domestica in Słupsk (NW Poland). Acta Ornithol. 39, 105–110 (2004).Article 

Google Scholar 
Dijkstra, C. et al. An adaptive annual rhythm in the sex of first pigeon eggs. Behav. Ecol. Sociobiol. 64, 1393–1402 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Swanson, D. L. Seasonal variation of vascular oxygen transport in the dark-eyed junco. Condor 92, 62–66 (1990).Article 

Google Scholar 
Niedojadlo, J., Bury, A., Cichoń, M., Sadowska, E. T. & Bauchinger, U. Lower haematocrit, haemoglobin and red blood cell number in zebra finches acclimated to cold compared to thermoneutral temperature. J. Avian Biol. 49, e01596 (2018).Article 

Google Scholar 
Roulin, A. Condition-dependence, pleiotropy and the handicap principle of sexual selection in melanin-based colouration. Biol. Rev. 91, 328–348 (2016).PubMed 
Article 

Google Scholar 
Statistics Poland. https://stat.gov.pl/en/ (2021).Sol, D. & Senar, J. C. Urban pigeon populations: Stability, home range, and the effect of removing individuals. Can. J. Zool. 73, 1154–1160 (1995).Article 

Google Scholar 
Minias, P. Reproduction and survival in the city: Which fitness components drive urban colonization in a reed-nesting waterbird?. Curr. Zool. 62, 79–87 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Meissner, W. & Fischer, I. Sexing of common gull, Larus canus, using linear measurements. Folia Zool. 66, 183–188 (2017).Article 

Google Scholar 
Haag-Wackernagel, D., Heeb, P. & Leiss, A. Phenotype-dependent selection of juvenile urban feral pigeons Columba livia. Bird Study 53, 163–170 (2006).Article 

Google Scholar 
Harter, T. S., Reichert, M., Brauner, C. J. & Milsom, W. K. Validation of the i-STAT and HemoCue systems for the analysis of blood parameters in the bar-headed goose, Anser indicus. Conserv. Physiol. 3, cov021 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Minias, P. The use of haemoglobin concentrations to assess physiological condition in birds: A review. Conserv. Physiol. 3, cov007 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Martin, L. B. et al. Phytohemagglutinin-induced skin swelling in birds: Histological support for a classic immunoecological technique. Funct. Ecol. 20, 290–299 (2006).Article 

Google Scholar 
Brown, G. P., Shilton, C. M. & Shine, R. Measuring amphibian immunocompetence: Validation of the phytohemagglutinin skin-swelling assay in the cane toad, Rhinella marina. Methods Ecol. Evol. 2, 341–348 (2011).Article 

Google Scholar 
Kennedy, M. W. & Nager, R. G. The perils and prospects of using phytohaemagglutinin in evolutionary ecology. Trends Ecol. Evol. 21, 653–655 (2006).PubMed 
Article 

Google Scholar 
Vinkler, M., Bainová, H. & Albrecht, T. Functional analysis of the skin-swelling response to phytohaemagglutinin. Funct. Ecol. 24, 1081–1086 (2010).Article 

Google Scholar 
Turmelle, A. S., Ellison, J. A., Mendonça, M. T. & McCracken, G. F. Histological assessment of cellular immune response to the phytohemagglutinin skin test in Brazilian free-tailed bats (Tadarida brasiliensis). J. Comp. Physiol. B 180, 1155–1164 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Griffiths, R., Double, M. C., Orr, K. & Dawson, R. J. A DNA test to sex most birds. Mol. Ecol. 7, 1071–1075 (1998).CAS 
PubMed 
Article 

Google Scholar 
Çakmak, E., Akın Pekşen, Ç. & Bilgin, C. C. Comparison of three different primer sets for sexing birds. J. Vet. Diagn. Investig. 29, 59–63 (2017).Article 
CAS 

Google Scholar 
Kaiser, H. F. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960).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 
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 
Jaeger, B. C., Edwards, L. J., Das, K. & Sen, P. K. An R 2 statistic for fixed effects in the generalized linear mixed model. J. Appl. Stat. 44, 1086–1105 (2017).MathSciNet 
MATH 
Article 

Google Scholar 
Johnson, P. C. Extension of Nakagawa & Schielzeth’s R2GLMM to random slopes models. Methods Ecol. Evol. 5, 944–946 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Bartoń, K. MuMIn: Multi-model inference. R package ver. 1.43.17. CRAN: The Comprehensive R Archive Network, Berkeley, CA, USA. https://CRAN.R-project.org/package=MuMIn (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
Kahle, D. J. & Wickham, H. ggmap: Spatial visualization with ggplot2. R J. 5, 144–161 (2013).Article 

Google Scholar  More

Mariotte, P. et al. Plant–soil feedback: Bridging natural and agricultural sciences. Trends Ecol. Evol. 33, 129–142 (2018).PubMed 
Article 

Google Scholar 
Daryanto, S., Fu, B., Wang, L., Jacinthe, P. A. & Zhao, W. Quantitative synthesis on the ecosystem services of cover crops. Earth-Sci. Rev. 185, 357–373 (2018).ADS 
CAS 
Article 

Google Scholar 
Shackelford, G. E., Kelsey, R. & Dicks, L. V. Effects of cover crops on multiple ecosystem services: Ten meta-analyses of data from arable farmland in California and the Mediterranean. Land Use Policy 88, 104204 (2019).Article 

Google Scholar 
McDaniel, M. D., Tiemann, L. K. & Grandy, A. S. Does agricultural crop diversity enhance soil microbial biomass and organic matter dynamics? A meta-analysis. Ecol. Appl. 24, 560–570 (2014).CAS 
PubMed 
Article 

Google Scholar 
Wittwer, R. A., Dorn, B., Jossi, W. & van der Heijden, M. G. A. A. Cover crops support ecological intensification of arable cropping systems. Sci. Rep. 7, 41911 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chahal, I. & Van Eerd, L. L. Cover crops increase tomato productivity and reduce nitrogen losses in a temperate humid climate. Nutr. Cycl. Agroecosyst. 119, 195–211 (2021).CAS 
Article 

Google Scholar 
Belfry, K. D., Trueman, C., Vyn, R. J., Loewen, S. A. & Van Eerd, L. L. Winter cover crops on processing tomato yield, quality, pest pressure, nitrogen availability, and profit margins. PLoS ONE 12, 1–17 (2017).Article 
CAS 

Google Scholar 
Wall, L. G. et al. Changes of paradigms in agriculture soil microbiology and new challenges in microbial ecology. Acta Oecologica 95, 68–73 (2019).ADS 
Article 

Google Scholar 
Schmidt, R., Gravuer, K., Bossange, A. V., Mitchell, J. & Scow, K. Long-term use of cover crops and no-till shift soil microbial community life strategies in agricultural soil. PLoS ONE 13, 1–19 (2018).
Google Scholar 
Schmidt, R., Mitchell, J. & Scow, K. Cover cropping and no-till increase diversity and symbiotroph:saprotroph ratios of soil fungal communities. Soil Biol. Biochem. 129, 99–109 (2019).CAS 
Article 

Google Scholar 
Ali, A. et al. Hiseq base molecular characterization of soil microbial community, diversity structure, and predictive functional profiling in continuous cucumber planted soil affected by diverse cropping systems in an intensive greenhouse region of Northern China. Int. J. Mol. Sci. 20, 2619 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
Kim, N., Zabaloy, M. C., Guan, K. & Villamil, M. B. Do cover crops benefit soil microbiome? A meta-analysis of current research. Soil Biol. Biochem. 142, 107701 (2020).CAS 
Article 

Google Scholar 
Vukicevich, E., Lowery, T., Bowen, P., Úrbez-Torres, J. R. & Hart, M. Cover crops to increase soil microbial diversity and mitigate decline in perennial agriculture. A review. Agron. Sustain. Dev. 36, 1–14 (2016).CAS 
Article 

Google Scholar 
Nevins, C. J., Nakatsu, C. & Armstrong, S. Characterization of microbial community response to cover crop residue decomposition. Soil Biol. Biochem. 127, 39–49 (2018).CAS 
Article 

Google Scholar 
Peralta, A. L., Sun, Y., McDaniel, M. D. & Lennon, J. T. Crop rotational diversity increases disease suppressive capacity of soil microbiomes. Ecosphere 9, e02235 (2018).Article 

Google Scholar 
Cloutier, M. L. et al. Fungal community shifts in soils with varied cover crop treatments and edaphic properties. Sci. Rep. 10, 1–15 (2020).Article 
CAS 

Google Scholar 
Finney, D. M., Buyer, J. S. & Kaye, J. P. Living cover crops have immediate impacts on soil microbial community structure and function. J. Soil Water Conserv. 72, 361–373 (2017).Article 

Google Scholar 
Calderón, F. J., Nielsen, D., Acosta-Martínez, V., Vigil, M. F. & Lyon, D. Cover crop and irrigation effects on soil microbial communities and enzymes in semiarid agroecosystems of the central great plains of North America. Pedosphere 26, 192–205 (2016).Article 
CAS 

Google Scholar 
Romdhane, S. et al. Cover crop management practices rather than composition of cover crop mixtures affect bacterial communities in no-till agroecosystems. Front. Microbiol. 10, 1–11 (2019).Article 

Google Scholar 
Blanco-Canqui, H. & Lal, R. Crop residue removal impacts on soil productivity and environmental quality. CRC. Crit. Rev. Plant Sci. 28, 139–163 (2009).CAS 
Article 

Google Scholar 
Turmel, M. S., Speratti, A., Baudron, F., Verhulst, N. & Govaerts, B. Crop residue management and soil health: A systems analysis. Agric. Syst. 134, 6–16 (2015).Article 

Google Scholar 
Yang, Q., Wang, X. & Shen, Y. Comparison of soil microbial community catabolic diversity between rhizosphere and bulk soil induced by tillage or residue retention. J. Soil Sci. Plant Nutr. https://doi.org/10.4067/S0718-95162013005000017 (2013).Article 

Google Scholar 
Tang, H. et al. Tillage and crop residue incorporation effects on soil bacterial diversity in the double-cropping paddy field of southern China. Arch. Agron. Soil Sci. 67, 435–446 (2021).CAS 
Article 

Google Scholar 
Zhang, Y. et al. Long-term harvest residue retention could decrease soil bacterial diversities probably due to favouring oligotrophic lineages. Microb. Ecol. 76, 771–781 (2018).CAS 
PubMed 
Article 

Google Scholar 
Zhang, C. et al. Straw retention efficiently improves fungal communities and functions in the fallow ecosystem. BMC Microbiol. 21, 52 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chahal, I. & Van Eerd, L. L. Cover crop and crop residue removal effects on temporal dynamics of soil carbon and nitrogen in a temperate, humid climate. PLoS ONE 15, e0235665 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chahal, I. & Van Eerd, L. L. Evaluation of commercial soil health tests using a medium-term cover crop experiment in a humid, temperate climate. Plant Soil 427, 351–367 (2018).CAS 
Article 

Google Scholar 
Ruis, S. J. & Blanco-Canqui, H. Cover crops could offset crop residue removal effects on soil carbon and other properties: A review. Agron. J. 109, 1785–1805 (2017).CAS 
Article 

Google Scholar 
Zhao, M. et al. Intercropping affects genetic potential for inorganic nitrogen cycling by root-associated microorganisms in Medicago sativa and Dactylis glomerata. Appl. Soil Ecol. 119, 260–266 (2017).ADS 
Article 

Google Scholar 
Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science (80-). 304, 1629–1633 (2004).ADS 
CAS 
Article 

Google Scholar 
Xiong, C. et al. Host selection shapes crop microbiome assembly and network complexity. New Phytol. 229, 1091–1104 (2021).CAS 
PubMed 
Article 

Google Scholar 
McDaniel, M. D., Grandy, A. S., Tiemann, L. K. & Weintraub, M. N. Eleven years of crop diversification alters decomposition dynamics of litter mixtures incubated with soil. Ecosphere 7, e01426 (2016).Article 

Google Scholar 
Buyer, J. S., Teasdale, J. R., Roberts, D. P., Zasada, I. A. & Maul, J. E. Factors affecting soil microbial community structure in tomato cropping systems. Soil Biol. Biochem. 42, 831–841 (2010).CAS 
Article 

Google Scholar 
Fernandez-Gnecco, G. et al. Microbial community analysis of soils under different soybean cropping regimes in the Argentinean south-eastern Humid Pampas. FEMS Microbiol. Ecol. 97, 1–14 (2021).Article 
CAS 

Google Scholar 
Semenov, M. V., Krasnov, G. S., Semenov, V. M. & van Bruggen, A. H. C. Long-term fertilization rather than plant species shapes rhizosphere and bulk soil prokaryotic communities in agroecosystems. Appl. Soil Ecol. 154, 103641 (2020).Article 

Google Scholar 
White, C. M. & Weil, R. R. Forage radish cover crops increase soil test phosphorus surrounding radish taproot holes. Soil Sci. Soc. Am. J. 75, 121–130 (2011).ADS 
CAS 
Article 

Google Scholar 
Schulz, M., Marocco, A., Tabaglio, V., Macias, F. A. & Molinillo, J. M. G. Benzoxazinoids in rye allelopathy—From discovery to application in sustainable weed control and organic farming. J. Chem. Ecol. 39, 154–174 (2013).CAS 
PubMed 
Article 

Google Scholar 
Cheng, F. & Cheng, Z. Research progress on the use of plant allelopathy in agriculture and the physiological and ecological mechanisms of allelopathy. Front. Plant Sci. 6, 1020 (2015).PubMed 
PubMed Central 

Google Scholar 
Thapa, V. R., Ghimire, R., Acosta-Martínez, V., Marsalis, M. A. & Schipanski, M. E. Cover crop biomass and species composition affect soil microbial community structure and enzyme activities in semiarid cropping systems. Appl. Soil Ecol. 157, 103735 (2021).Article 

Google Scholar 
Drost, S. M., Rutgers, M., Wouterse, M., de Boer, W. & Bodelier, P. L. E. Decomposition of mixtures of cover crop residues increases microbial functional diversity. Geoderma 361, 114060 (2020).ADS 
CAS 
Article 

Google Scholar 
Di Rauso Simeone, G., Müller, M., Felgentreu, C. & Glaser, B. Soil microbial biomass and community composition as affected by cover crop diversity in a short-term field experiment on a podzolized Stagnosol-Cambisol. J. Plant Nutr. Soil Sci. 183, 539–549 (2020).Article 
CAS 

Google Scholar 
Maul, J. E. et al. Microbial community structure and abundance in the rhizosphere and bulk soil of a tomato cropping system that includes cover crops. Appl. Soil Ecol. 77, 42–50 (2014).Article 

Google Scholar 
Huang, J. et al. Allocation and turnover of rhizodeposited carbon in different soil microbial groups. Soil Biol. Biochem. 150, 107973 (2020).CAS 
Article 

Google Scholar 
Strickland, M. S. & Rousk, J. Considering fungal:bacterial dominance in soils—Methods, controls, and ecosystem implications. Soil Biol. Biochem. 42, 1385–1395 (2010).CAS 
Article 

Google Scholar 
Leff, J. W. et al. Predicting the structure of soil communities from plant community taxonomy, phylogeny, and traits. ISME J. 12, 1794–1805 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Milcu, A. et al. Functionally and phylogenetically diverse plant communities key to soil biota. Ecology 94, 1878–1885 (2013).PubMed 
Article 

Google Scholar 
Lozupone, C. A., Hamady, M., Kelley, S. T. & Knight, R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. 73, 1576–1585 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lay, C.-Y., Hamel, C. & St-Arnaud, M. Taxonomy and pathogenicity of Olpidium brassicae and its allied species. Fungal Biol. 122, 837–846 (2018).PubMed 
Article 

Google Scholar 
Liu, L., Zhu, K., Wurzburger, N. & Zhang, J. Relationships between plant diversity and soil microbial diversity vary across taxonomic groups and spatial scales. Ecosphere 11, e02999 (2020).
Google Scholar 
Hartwright, L. M., Hunter, P. J. & Walsh, J. A. A comparison of Olpidium isolates from a range of host plants using internal transcribed spacer sequence analysis and host range studies. Fungal Biol. 114, 26–33 (2010).CAS 
PubMed 
Article 

Google Scholar 
Barel, J. M. et al. Winter cover crop legacy effects on litter decomposition act through litter quality and microbial community changes. J. Appl. Ecol. 56, 132–143 (2019).CAS 
Article 

Google Scholar 
Austin, E. E., Wickings, K., McDaniel, M. D., Robertson, G. P. & Grandy, A. S. Cover crop root contributions to soil carbon in a no-till corn bioenergy cropping system. GCB Bioenergy 9, 1252–1263 (2017).CAS 
Article 

Google Scholar 
Bai, Z., Liang, C., Bodé, S., Huygens, D. & Boeckx, P. Phospholipid 13C stable isotopic probing during decomposition of wheat residues. Appl. Soil Ecol. 98, 65–74 (2016).Article 

Google Scholar 
Põlme, S. et al. FungalTraits: A user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).Article 

Google Scholar 
Pepe, A., Giovannetti, M. & Sbrana, C. Lifespan and functionality of mycorrhizal fungal mycelium are uncoupled from host plant lifespan. Sci. Rep. 8, 1–10 (2018).
Google Scholar 
Frey, S. D. Mycorrhizal fungi as mediators of soil organic matter dynamics. Annu. Rev. Ecol. Evol. Syst. 50, 237–259 (2019).Article 

Google Scholar 
Saleem, M., Hu, J. & Jousset, A. More than the sum of its parts: Microbiome biodiversity as a driver of plant growth and soil health. Annu. Rev. Ecol. Evol. Syst. 50, 145–168 (2019).Article 

Google Scholar 
Wei, Z. et al. Initial soil microbiome composition and functioning predetermine future plant health. Sci. Adv. 5, 1–12 (2019).
Google Scholar 
Ozimek, E. & Hanaka, A. Mortierella species as the plant growth-promoting fungi present in the agricultural soils. Agriculture 11, 7 (2020).Article 
CAS 

Google Scholar 
Li, F. et al. Mortierella elongata’s roles in organic agriculture and crop growth promotion in a mineral soil. L. Degrad. Dev. 29, 1642–1651 (2018).Article 

Google Scholar 
Sansinenea, E. Bacillus spp.: As plant growth-promoting bacteria. in Secondary Metabolites of Plant Growth Promoting Rhizomicroorganisms: Discovery and Applications 225–237 (Springer, 2019). https://doi.org/10.1007/978-981-13-5862-3_11.Palaniyandi, S. A., Yang, S. H., Zhang, L. & Suh, J.-W. Effects of actinobacteria on plant disease suppression and growth promotion. Appl. Microbiol. Biotechnol. 97, 9621–9636 (2013).CAS 
PubMed 
Article 

Google Scholar 
Jung, M.-Y. et al. Ammonia-oxidizing archaea possess a wide range of cellular ammonia affinities. ISME J. 16, 272–283 (2022).CAS 
PubMed 
Article 

Google Scholar 
Zhong, Y. et al. Microbial community assembly and metabolic function during wheat straw decomposition under different nitrogen fertilization treatments. Biol. Fertil. Soils 56, 697–710 (2020).CAS 
Article 

Google Scholar 
Liu, X. et al. Decomposing cover crops modify root-associated microbiome composition and disease tolerance of cash crop seedlings. Soil Biol. Biochem. 160, 108343 (2021).CAS 
Article 

Google Scholar 
Larkin, R. P., Griffin, T. S. & Honeycutt, C. W. Rotation and cover crop effects on soilborne potato diseases, tuber yield, and soil microbial communities. Plant Dis. 94, 1491–1502 (2010).PubMed 
Article 

Google Scholar 
van der Putten, W. H., Bradford, M. A., Brinkman, E. P., van de Voorde, T. F. J. & Veen, G. F. Where, when and how plant–soil feedback matters in a changing world. Funct. Ecol. 30, 1109–1121 (2016).Article 

Google Scholar 
Menalled, U. D., Seipel, T. & Menalled, F. D. Farming system effects on biologically mediated plant–soil feedbacks. Renew. Agric. Food Syst. 36, 1–7 (2021).Article 

Google Scholar 
Fierer, N. & Jackson, J. Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl. Environ. Microbiol. 71, 4117 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vainio, E. J. & Hantula, J. Direct analysis of wood-inhabiting fungi using denaturing gradient gel electrophoresis of amplified ribosomal DNA. Mycol. Res. 104, 927–936 (2000).CAS 
Article 

Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
White, T. J., Bruns, T., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, 1990).

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rivers, A. R., Weber, K. C., Gardner, T. G., Liu, S. & Armstrong, S. D. ITSxpress: Software to rapidly trim internally transcribed spacer sequences with quality scores for marker gene analysis. F1000Research 7, 1418 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
Abarenkov, K. et al. UNITE QIIME release for Fungi. https://doi.org/10.15156/bio/786385 (2020).R Core Team. R: A Language and Environment for Statistical Computing. (2020).Oksanen, J. et al. vegan: Community Ecology Package. (2020).Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. (PRIMER-E, 2008).Anderson, M. J. & Willis, T. J. Canonical analysis of principal coordinates: A useful method of constrained ordination for ecology. Ecology 84, 511–525 (2003).Article 

Google Scholar 
Cáceres, M. D. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574 (2009).PubMed 
Article 

Google Scholar 
Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: Characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).PubMed 
PubMed Central 
Article 

Google Scholar  More

Woese, C. R., Kandler, O. & Wheelis, M. L. Proc. Natl Acad. Sci. USA 87, 4576–4579 (1990).CAS 
Article 

Google Scholar 
Sapp, J. Microbiol. Mol. Biol. Rev. 69, 292–305 (2005).CAS 
Article 

Google Scholar 
Chatton, É. Ann. Sci. Nat. Zool. 8, 1–84 (1925).
Google Scholar 
Soyer-Gobillard, M.-O. & Schrevel, J. The Discoveries and Artistic Talents of Édouard Chatton and André Lwoff, Famous Biologists (Cambridge Scholars Publishing, 2020).Macfarlane, J. M. The Causes and Course of Organic Evolution: A Study in Bioenergics (Macmillan, 1918).Haeckel, E. Systematische Phylogenie. Erster Theil (Verlag von Georg Reimer, 1894).Stanier, R. Y., Douderoff, M. & Adelberg, E. The Microbial World 2nd edn (Prentice Hall, 1963).Williams, T. A., Cox, C. J., Foster, P. G., Szöllősi, G. J. & Embley, T. M. Nat. Ecol. Evol. 4, 138–147 (2020).Article 

Google Scholar 
Steckbeck, W. Science 98, 487–488 (1943).CAS 
Article 

Google Scholar 
Creese, M. R. S. & Creese, T. M. Ladies in the Laboratory III (Scarecrow Press, 2010). More

Swedish environment minister Annika Strandhäll before the start of the Stockholm +50 Climate Summit. Few world leaders will be attending.Credit: Fredrik Persson/TT News Agency/AFP/Getty

Sustainability is now a household term, but it wasn’t always so.Fifty years ago, the United Nations held its Conference on the Human Environment in Stockholm. This landmark event gave the concept of sustainable development its first international recognition. Sweden and the UN are marking the occasion this week with Stockholm+50, an international meeting that serves as both commemoration and call to action.The world is deep in planetary and human crises, with the UN’s Sustainable Development Goals off track and multilateral agreements on climate change and biodiversity behind schedule. Governments need to integrate sustainability into economic planning — and listen to researchers, who are ready with evidence-based arguments and tools to help them do so.Fifty years ago, the time was ripe for an environmental agenda to enter the world stage. Optimistic ideas of economic growth as a driver of progress, propelled by the Industrial Revolution, needed to accommodate concerns over damage to the natural environment. Books such as Rachel Carson’s Silent Spring (1962) — which raised awareness about harms caused by pesticides — brought scientific information about environmental risks into the mainstream.In March 1972, a team of researchers and policymakers sounded another alarm in The Limits to Growth, one of the first reports to forecast catastrophic consequences if humans kept exploiting Earth’s limited supply of natural resources. The conference in Stockholm followed a few months later, steered to success by its secretary-general, Canadian industrialist Maurice Strong. That set crucial institutions in motion, starting with the establishment of the UN Environment Programme (UNEP), based in Nairobi — the first UN body to be headquartered in a developing country. UNEP went on to facilitate a new international law — the 1987 Montreal Protocol to phase out ozone-depleting substances — and co-founded the Intergovernmental Panel on Climate Change (IPCC). It assisted in establishing the first action plans for sustainable development through landmark international agreements on biodiversity, climate and desertification.But there were mistakes and missed opportunities. The establishment of multiple agencies and policy instruments created a disjointed governance system. Newly created environment ministers wielded little power. In national budgets, environmental protection was siloed away from economic development and social concerns. For a long time, action on climate change remained unfocused. And the economic drivers of environmental change were overlooked.And so, 50 years after that momentous conference, the world remains in crisis. With impending climate and biodiversity crises, the warnings issued by visionaries now hit even closer.Stockholm+50 promises “clear and concrete recommendations and messages for action at all levels”. More than 90 ministers are expected to attend, but only 10 heads of government. That’s a missed opportunity for high-level action. World leaders are needed because their presence signals that sustainability remains at the top of their agendas.Awareness of the need to embed sustainability into policymaking has broken into the mainstream, although much of it is still talk. City governments around the world are implementing ambitious climate action plans through the C40 Cities network. Some companies, too, are adopting sustainability principles, from reporting (and reducing) their carbon footprints to ensuring that investments, as far as possible, do not harm the environment.But this urgency has not ascended to heads of state and government. With a handful of exceptions — such as Finland, Iceland, New Zealand, Scotland and Wales — most nations seem unwilling to systemically integrate their economic, environmental and social policymaking.Doing so is not only good for the environment; it is also sound economics and good for well-being. The food and energy crisis driving poverty and diminishing living standards around the world might have been triggered by the shocks of a pandemic and war on Ukraine — but it is driven just as much by the depletion of natural resources.Ahead of the 1972 conference, 2,200 environmental scientists signed a letter — called the Menton Message — to then UN secretary-general U Thant. The signatories had a sense that the world was moving towards multiple crises. They urged “massive research into the problems that threaten the survival of mankind”, such as hunger, wars, environmental degradation and natural-resource depletion. The UN system went on to play a big part in building the body of knowledge that has shown why sustainability is necessary, and in creating the policy architecture to make it happen. But to do the Stockholm vision justice, there must be bolder action from heads of government and from the UN system. The planned creation of a board of science advisers to UN secretary-general António Guterres needs to be accelerated. Once established, the board must find a way to bring joined-up action on sustainability closer to world leaders.Researchers can now join a successor to the Menton Message that has been organized by the International Science Council, the global science network Future Earth and the Stockholm Environment Institute. In an open letter addressed to world citizens, the authors write: “After 50 years, pro-environmental action seems like one step forward and two back. The world produces more food than needed, yet many people still go hungry. We continue to subsidize and invest in fossil fuels, even though renewable energy is increasingly cost-effective. We extract resources where the price is lowest, often in direct disregard of local rights and values.”World leaders must listen to the research community, and accept the evidence and narrative offered to help them to navigate meaningful change. Environmental sustainability does not impede prosperity and well-being — in fact, it is vital to them. People in power need to sit up and take notice. More

Houborg, R., Fisher, J. B. & Skidmore, A. K. Advances in remote sensing of vegetation function and traits. Int. J. Appl. Earth Obs. Geoinf. 43, 1–6 (2015).
Google Scholar 
Bannari, A., Morin, D., Bonn, F. & Huete, A. A review of vegetation indices. Remote Sens. Rev. 13, 95–120 (1995).Article 

Google Scholar 
Gao, X., Huete, A. R., Ni, W. & Miura, T. Optical–biophysical relationships of vegetation spectra without background contamination. Remote Sens. Environ. 74, 609–620 (2000).Article 

Google Scholar 
Huete, A. R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25, 295–309 (1988).Article 

Google Scholar 
Badgley, G., Field, C. B. & Berry, J. A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv. 3, e1602244 (2017).Article 

Google Scholar 
Gamon, J. A. et al. A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers. Proc. Natl Acad. Sci. USA 113, 13087–13092 (2016).Article 

Google Scholar 
Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).Article 

Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 

Google Scholar 
Tian, F. et al. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sens. Environ. 163, 326–340 (2015).Article 

Google Scholar 
Fan, X. & Liu, Y. A global study of NDVI difference among moderate-resolution satellite sensors. ISPRS J. Photogramm. Remote Sens. 121, 177–191 (2016).Article 

Google Scholar 
AghaKouchak, A. et al. Remote sensing of drought: progress, challenges and opportunities. Rev. Geophys. 53, 452–480 (2015).Article 

Google Scholar 
Anyamba, A. & Tucker, in Remote Sensing of Drought: Innovative Monitoring Approaches Ch. 2 (eds Wardlow, B. D., Anderson, M. C. & Verdin, J. P.) (Taylor & Francis, 2012).Veraverbeke, S. et al. Hyperspectral remote sensing of fire: state-of-the-art and future perspectives. Remote Sens. Environ. 216, 105–121 (2018).Article 

Google Scholar 
Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).Article 

Google Scholar 
Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ. 351, 309 (1974).
Google Scholar 
Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W. & Harlan, J. C. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFCT Type III Final Report, 371 (NASA, 1974).Gutman, G., Skakun, S. & Gitelson, A. Revisiting the use of red and near-infrared reflectances in vegetation studies and numerical climate models. Sci. Remote Sens. 4, 100025 (2021).Article 

Google Scholar 
Jackson, R. D. & Huete, A. R. Interpreting vegetation indices. Prev. Vet. Med. 11, 185–200 (1991).Article 

Google Scholar 
Richardson, A. J. & Wiegand, C. Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens. 43, 1541–1552 (1977).
Google Scholar 
Baret, F., Guyot, G. & Major, D. in 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium 1355–1358 (IEEE, 1989).Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. & Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 48, 119–126 (1994).Article 

Google Scholar 
Chen, J. M. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens. 22, 229–242 (1996).Article 

Google Scholar 
Brown, L., Chen, J. M., Leblanc, S. G. & Cihlar, J. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis. Remote Sens. Environ. 71, 16–25 (2000).Article 

Google Scholar 
Pinty, B. & Verstraete, M. GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio 101, 15–20 (1992).Article 

Google Scholar 
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).Article 

Google Scholar 
Kaufman, Y. J. & Tanre, D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30, 261–270 (1992).Article 

Google Scholar 
Jiang, Z., Huete, A. R., Didan, K. & Miura, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 112, 3833–3845 (2008).Article 

Google Scholar 
Jin, H. & Eklundh, L. A physically based vegetation index for improved monitoring of plant phenology. Remote Sens. Environ. 152, 512–525 (2014).Article 

Google Scholar 
Yang, P., van der Tol, C., Campbell, P. K. & Middleton, E. M. Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence. Remote Sens. Environ. 240, 111676 (2020).Article 

Google Scholar 
Badgley, G., Anderegg, L. D., Berry, J. A. & Field, C. B. Terrestrial gross primary production: Using NIRV to scale from site to globe. Glob. Change Biol. 25, 3731–3740 (2019).Article 

Google Scholar 
Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7, eabc7447 (2021).Article 

Google Scholar 
Roberts, D. A., Roth, K. L. & Perroy, R. L. in Hyperspectral Remote Sensing of Vegetation Ch. 14 (eds Thenkabail, P. S., Lyon, J. G. & Huete, A.) (CRC, 2016).Gitelson, A. A., Vina, A., Ciganda, V., Rundquist, D. C. & Arkebauer, T. J. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32, L08403 (2005).Article 

Google Scholar 
Gitelson, A. & Merzlyak, M. N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 143, 286–292 (1994).Article 

Google Scholar 
Dash, J. & Curran, P. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 25, 5403–5413 (2004).Article 

Google Scholar 
Penuelas, J., Baret, F. & Filella, I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 31, 221–230 (1995).
Google Scholar 
Peñuelas, J., Gamon, J., Fredeen, A., Merino, J. & Field, C. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ. 48, 135–146 (1994).Article 

Google Scholar 
Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B. & Rakitin, V. Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 106, 135–141 (1999).Article 

Google Scholar 
Gitelson, A. A., Merzlyak, M. N. & Chivkunova, O. B. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem. Photobiol. 74, 38–45 (2001).Article 

Google Scholar 
van den Berg, A. K. & Perkins, T. D. Nondestructive estimation of anthocyanin content in autumn sugar maple leaves. HortScience 40, 685–686 (2005).Article 

Google Scholar 
Gamon, J. & Surfus, J. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 143, 105–117 (1999).Article 

Google Scholar 
Gao, B.-C. NDWI — a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266 (1996).Article 

Google Scholar 
Xiao, X., Boles, S., Liu, J., Zhuang, D. & Liu, M. Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sens. Environ. 82, 335–348 (2002).Article 

Google Scholar 
Xiao, X. et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 89, 519–534 (2004).Article 

Google Scholar 
Yilmaz, M. T., Hunt, E. R. Jr & Jackson, T. J. Remote sensing of vegetation water content from equivalent water thickness using satellite imagery. Remote Sens. Environ. 112, 2514–2522 (2008).Article 

Google Scholar 
Cheng, Y.-B., Ustin, S. L., Riaño, D. & Vanderbilt, V. C. Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona. Remote Sens. Environ. 112, 363–374 (2008).Article 

Google Scholar 
Serrano, L., Penuelas, J. & Ustin, S. L. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sens. Environ. 81, 355–364 (2002).Article 

Google Scholar 
Filella, I. et al. PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle. Int. J. Remote Sens. 30, 4443–4455 (2009).Article 

Google Scholar 
Gamon, J., Penuelas, J. & Field, C. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 41, 35–44 (1992).Article 

Google Scholar 
Cheng, R. et al. Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest. Biogeosciences 17, 4523–4544 (2020).Article 

Google Scholar 
Seyednasrollah, B. et al. Seasonal variation in the canopy color of temperate evergreen conifer forests. New Phytol. 229, 2586–2600 (2021).Article 

Google Scholar 
Merton, R. in Proceedings of the Seventh Annual JPL Airborne Earth Science Workshop 12–16 (NASA, 2004).Naidu, R. A., Perry, E. M., Pierce, F. J. & Mekuria, T. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Comput. Electron. Agric. 66, 38–45 (2009).Article 

Google Scholar 
Chen, Y. et al. Generation and evaluation of LAI and FPAR products from Himawari-8 Advanced Himawari imager (AHI) data. Remote Sens. 11, 1517 (2019).Article 

Google Scholar 
Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).Article 

Google Scholar 
Liu, Y., Liu, R. & Chen, J. M. Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data. J. Geophys. Res. 117, G04003 (2012).
Google Scholar 
Croft, H. et al. The global distribution of leaf chlorophyll content. Remote Sens. Environ. 236, 111479 (2020).Article 

Google Scholar 
Bayat, B. et al. Toward operational validation systems for global satellite-based terrestrial essential climate variables. Int. J. Appl. Earth Obs. Geoinf. 95, 102240 (2021).
Google Scholar 
Cui, Y., Song, L. & Fan, W. Generation of spatio-temporally continuous evapotranspiration and its components by coupling a two-source energy balance model and a deep neural network over the Heihe River Basin. J. Hydrol. 597, 126176 (2021).Article 

Google Scholar 
Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M. & Notarnicola, C. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sens. 7, 16398–16421 (2015).Article 

Google Scholar 
Gitelson, A. A. et al. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett. 30, 1248 (2003).Article 

Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).Article 

Google Scholar 
Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).Article 

Google Scholar 
Morton, D. C. et al. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 506, 221–224 (2014).Article 

Google Scholar 
Jiang, Z. et al. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens. Environ. 101, 366–378 (2006).Article 

Google Scholar 
Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. & Strachan, I. B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens. Environ. 90, 337–352 (2004).Article 

Google Scholar 
Wu, C., Wang, L., Niu, Z., Gao, S. & Wu, M. Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images. Int. J. Remote Sens. 31, 2159–2167 (2010).Article 

Google Scholar 
Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).Article 

Google Scholar 
Ustin, S. L. & Gamon, J. A. Remote sensing of plant functional types. New Phytol. 186, 795–816 (2010).Article 

Google Scholar 
Hilker, T. et al. Vegetation dynamics and rainfall sensitivity of the Amazon. Proc. Natl Acad. Sci. USA 111, 16041–16046 (2014).Article 

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

Google Scholar 
Weber, M. et al. Exploring the use of DSCOVR/EPIC satellite observations to monitor vegetation phenology. Remote Sens. 12, 2384 (2020).Article 

Google Scholar 
Ganguly, S., Friedl, M. A., Tan, B., Zhang, X. & Verma, M. Land surface phenology from MODIS: characterization of the Collection 5 global land cover dynamics product. Remote Sens. Environ. 114, 1805–1816 (2010).Article 

Google Scholar 
Gray, J., Sulla-Menashe, D. & Friedl, M. A. User Guide to Collection 6 MODIS Land Cover Dynamics Product (MCD12Q2) (NASA, 2019).Wang, S., Zhang, Y., Ju, W., Qiu, B. & Zhang, Z. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. Sci. Total Environ. 755, 142569 (2021).Article 

Google Scholar 
Tian, F. et al. Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe. Remote Sens. Environ. 260, 112456 (2021).Article 

Google Scholar 
Yin, G., Verger, A., Filella, I., Descals, A. & Peñuelas, J. Divergent estimates of forest photosynthetic phenology using structural and physiological vegetation indices. Geophys. Res. Lett. 47, e2020GL089167 (2020).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 
Samanta, A. et al. Amazon forests did not green-up during the 2005 drought. Geophys. Res. Lett. 37, L05401 (2010).Article 

Google Scholar 
Shi, Y., Huang, W., Luo, J., Huang, L. & Zhou, X. Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis. Comput. Electron. Agric. 141, 171–180 (2017).Article 

Google Scholar 
Zhang, Z., Liu, M., Liu, X. & Zhou, G. A new vegetation index based on multitemporal Sentinel-2 images for discriminating heavy metal stress levels in rice. Sensors 18, 2172 (2018).Article 

Google Scholar 
Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E. & Tucker III, C. J. Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations (Springer, 2015).Potter, C. S. et al. Terrestrial ecosystem production: a process model based on global satellite and surface data. Glob. Biogeochem. Cycles 7, 811–841 (1993).Article 

Google Scholar 
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).Article 

Google Scholar 
Yuan, W. et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agric. For. Meteorol. 143, 189–207 (2007).Article 

Google Scholar 
Chen, M. et al. Quantification of terrestrial ecosystem carbon dynamics in the conterminous United States combining a process-based biogeochemical model and MODIS and AmeriFlux data. Biogeosciences 8, 2665–2688 (2011).Article 

Google Scholar 
Xiao, J. et al. A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data. Remote Sens. Environ. 114, 576–591 (2010).Article 

Google Scholar 
Jiang, C., Guan, K., Wu, G., Peng, B. & Wang, S. A daily, 250 m, and real-time gross primary productivity product (2000–present) covering the contiguous United States. Earth Syst. Sci. Data Discuss. 2020, 1–28 (2020).
Google Scholar 
Schubert, P. et al. Modeling GPP in the Nordic forest landscape with MODIS time series data — comparison with the MODIS GPP product. Remote Sens. Environ. 126, 136–147 (2012).Article 

Google Scholar 
Zeng, Y. et al. A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence. Remote Sens. Environ. 232, 111209 (2019).Article 

Google Scholar 
Baldocchi, D. D. et al. Outgoing near infrared radiation from vegetation scales with canopy photosynthesis across a spectrum of function, structure, physiological capacity and weather. J. Geophys. Res. 125, e2019JG005534 (2020).
Google Scholar 
Dechant, B. et al. Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops. Remote Sens. Environ. 241, 111733 (2020).Article 

Google Scholar 
Rahman, A. F., Gamon, J. A., Fuentes, D. A., Roberts, D. A. & Prentiss, D. Modeling spatially distributed ecosystem flux of boreal forest using hyperspectral indices from AVIRIS imagery. J. Geophys. Res. Atmos. 106, 33579–33591 (2001).Article 

Google Scholar 
Zhu, Z. et al. Comment on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”. Science 373, eabg5673 (2021).Article 

Google Scholar 
Doughty, R. et al. Small anomalies in dry-season greenness and chlorophyll fluorescence for Amazon moist tropical forests during El Niño and La Niña. Remote Sens. Environ. 253, 112196 (2021).Article 

Google Scholar 
Huang, N. et al. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv. 6, eabb8508 (2020).Article 

Google Scholar 
Huang, N., He, J.-S. & Niu, Z. Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data. Ecol. Indic. 26, 117–125 (2013).Article 

Google Scholar 
Neale, C. M., Gonzalez-Dugo, M. P., Serrano-Perez, A., Campos, I. & Mateos, L. Cotton canopy reflectance under variable solar zenith angles: implications of use in evapotranspiration models. Hydrol. Process. 35, e14162 (2021).Article 

Google Scholar 
Chen, J. M. & Liu, J. Evolution of evapotranspiration models using thermal and shortwave remote sensing data. Remote Sens. Environ. 237, 111594 (2020).Article 

Google Scholar 
Glenn, E. P., Huete, A. R., Nagler, P. L. & Nelson, S. G. Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8, 2136–2160 (2008).Article 

Google Scholar 
Cui, Y., Jia, L. & Fan, W. Estimation of actual evapotranspiration and its components in an irrigated area by integrating the Shuttleworth-Wallace and surface temperature-vegetation index schemes using the particle swarm optimization algorithm. Agric. For. Meteorol. 307, 108488 (2021).Article 

Google Scholar 
Glenn, E. P., Neale, C. M., Hunsaker, D. J. & Nagler, P. L. Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems. Hydrol. Process. 25, 4050–4062 (2011).Article 

Google Scholar 
French, A. N. et al. Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest. Agric. Water Manag. 239, 106266 (2020).Article 

Google Scholar 
Lotsch, A., Friedl, M. A., Anderson, B. T. & Tucker, C. J. Coupled vegetation-precipitation variability observed from satellite and climate records. Geophys. Res. Lett. 30, 1774 (2003).Article 

Google Scholar 
Nezlin, N. P., Kostianoy, A. G. & Li, B.-L. Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region. J. Arid Environ. 62, 677–700 (2005).Article 

Google Scholar 
Notaro, M., Liu, Z. & Williams, J. W. Observed vegetation–climate feedbacks in the United States. J. Clim. 19, 763–786 (2006).Article 

Google Scholar 
Fensholt, R. & Proud, S. R. Evaluation of earth observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series. Remote Sens. Environ. 119, 131–147 (2012).Article 

Google Scholar 
Trishchenko, A. P., Cihlar, J. & Li, Z. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sens. Environ. 81, 1–18 (2002).Article 

Google Scholar 
Ustin, S. L. & Middleton, E. M. Current and near-term advances in Earth observation for ecological applications. Ecol. Process. 10, 1 (2021).Article 

Google Scholar 
Wang, D. et al. Impact of sensor degradation on the MODIS NDVI time series. Remote Sens. Environ. 119, 55–61 (2012).Article 

Google Scholar 
Zhang, Y., Song, C., Band, L. E., Sun, G. & Li, J. Reanalysis of global terrestrial vegetation trends from MODIS products: browning or greening? Remote Sens. Environ. 191, 145–155 (2017).Article 

Google Scholar 
Bhatt, R. et al. A consistent AVHRR visible calibration record based on multiple methods applicable for the NOAA degrading orbits. Part I: Methodology. J. Atmos. Ocean. Technol. 33, 2499–2515 (2016).Article 

Google Scholar 
Frankenberg, C., Yin, Y., Byrne, B., He, L. & Gentine, P. Comment on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”. Science 373, eabg2947 (2021).Article 

Google Scholar 
Los, S. O. Estimation of the ratio of sensor degradation between NOAA AVHRR channels 1 and 2 from monthly NDVI composites. IEEE Trans. Geosci. Remote Sens. 36, 206–213 (1998).Article 

Google Scholar 
Jiang, C. et al. Inconsistencies of interannual variability and trends in long-term satellite leaf area index products. Glob. Change Biol. 23, 4133–4146 (2017).Article 

Google Scholar 
de Beurs, K. M. & Henebry, G. M. Trend analysis of the Pathfinder AVHRR Land (PAL) NDVI data for the deserts of Central Asia. IEEE Geosci. Remote Sens. Lett. 1, 282–286 (2004).Article 

Google Scholar 
Wang, Z. et al. Large discrepancies of global greening: indication of multi-source remote sensing data. Global Ecol. Conserv. 34, e02016 (2022).Article 

Google Scholar 
Miura, T., Huete, A. R. & Yoshioka, H. Evaluation of sensor calibration uncertainties on vegetation indices for MODIS. IEEE Trans Geosci. Remote Sens. 38, 1399–1409 (2000).Article 

Google Scholar 
Lyapustin, A. et al. Scientific impact of MODIS C5 calibration degradation and C6+ improvements. Atmos. Meas. Tech. 7, 4353–4365 (2014).Article 

Google Scholar 
Buchhorn, M., Raynolds, M. K. & Walker, D. A. Influence of BRDF on NDVI and biomass estimations of Alaska Arctic tundra. Environ. Res. Lett. 11, 125002 (2016).Article 

Google Scholar 
Fensholt, R., Sandholt, I., Proud, S. R., Stisen, S. & Rasmussen, M. O. Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data. Int. J. Remote Sens. 31, 6163–6187 (2010).Article 

Google Scholar 
Saleska, S. R. et al. Dry-season greening of Amazon forests. Nature 531, E4–E5 (2016).Article 

Google Scholar 
Lyapustin, A. I. et al. Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction. Remote Sens. Environ. 127, 385–393 (2012).Article 

Google Scholar 
Norris, J. R. & Walker, J. J. Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States. Remote Sens. Environ. 249, 112013 (2020).Article 

Google Scholar 
Roy, D. P. et al. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sens. Environ. 176, 255–271 (2016).Article 

Google Scholar 
Schaaf, C. B. et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 83, 135–148 (2002).Article 

Google Scholar 
Didan, K., Munoz, A. B., Solano, R. & Huete, A. MODIS Vegetation Index User’s Guide (MOD13 Series) (Univ. Arizona, 2015).Wang, Z., Schaaf, C. B., Sun, Q., Shuai, Y. & Román, M. O. Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products. Remote Sens. Environ. 207, 50–64 (2018).Article 

Google Scholar 
Saleska, S. R., Didan, K., Huete, A. R. & Da Rocha, H. R. Amazon forests green-up during 2005 drought. Science 318, 612 (2007).Article 

Google Scholar 
Vargas, M., Miura, T., Shabanov, N. & Kato, A. An initial assessment of Suomi NPP VIIRS vegetation index EDR. J. Geophys. Res. Atmos. 118, 12,301–12,316 (2013).Article 

Google Scholar 
Kobayashi, H. & Dye, D. G. Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index. Remote Sens. Environ. 97, 519–525 (2005).Article 

Google Scholar 
Jiang, C. & Fang, H. GSV: a general model for hyperspectral soil reflectance simulation. Int. J. Appl. Earth Obs. Geoinf. 83, 101932 (2019).
Google Scholar 
Verrelst, J., Schaepman, M. E., Malenovský, Z. & Clevers, J. G. Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval. Remote Sens. Environ. 114, 647–656 (2010).Article 

Google Scholar 
Huete, A. & Tucker, C. Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery. Int. J. Remote Sens. 12, 1223–1242 (1991).Article 

Google Scholar 
Farrar, T., Nicholson, S. & Lare, A. The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. II. NDVI response to soil oisture. Remote Sens. Environ. 50, 121–133 (1994).Article 

Google Scholar 
Huete, A. & Warrick, A. Assessment of vegetation and soil water regimes in partial canopies with optical remotely sensed data. Remote Sens. Environ. 32, 155–167 (1990).Article 

Google Scholar 
Wang, C. et al. A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sens. Environ. 196, 1–12 (2017).Article 

Google Scholar 
Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).Article 

Google Scholar 
Shen, M. et al. No evidence of continuously advanced green-up dates in the Tibetan Plateau over the last decade. Proc. Natl Acad. Sci. 110, E2329 (2013).
Google Scholar 
Hao, D. et al. Modeling anisotropic reflectance over composite sloping terrain. IEEE Trans. Geosci. Remote Sens. 56, 3903–3923 (2018).Article 

Google Scholar 
Matsushita, B., Yang, W., Chen, J., Onda, Y. & Qiu, G. Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors 7, 2636–2651 (2007).Article 

Google Scholar 
Wen, J. et al. Characterizing land surface anisotropic reflectance over rugged terrain: a review of concepts and recent developments. Remote Sens. 10, 370 (2018).Article 

Google Scholar 
Friedl, M. A., Davis, F. W., Michaelsen, J. & Moritz, M. Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: an analysis using a scene simulation model and data from FIFE. Remote Sens. Environ. 54, 233–246 (1995).Article 

Google Scholar 
Tan, B. et al. The impact of gridding artifacts on the local spatial properties of MODIS data: implications for validation, compositing, and band-to-band registration across resolutions. Remote Sens. Environ. 105, 98–114 (2006).Article 

Google Scholar 
Wolfe, R. E. et al. Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sens. Environ. 83, 31–49 (2002).Article 

Google Scholar 
Ferreira, M. P. et al. Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy. Remote Sens. Environ. 211, 276–291 (2018).Article 

Google Scholar 
Huete, A. R. et al. Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. 33, L06405 (2006).Article 

Google Scholar 
Herrmann, S. M. & Tappan, G. G. Vegetation impoverishment despite greening: a case study from central Senegal. J. Arid Environ. 90, 55–66 (2013).Article 

Google Scholar 
Wang, X. et al. No consistent evidence for advancing or delaying trends in spring phenology on the Tibetan Plateau. J. Geophys. Res. Biogeosci. 122, 3288–3305 (2017).Article 

Google Scholar 
Donnelly, A., Yu, R. & Liu, L. Comparing in situ spring phenology and satellite-derived start of season at rural and urban sites in Ireland. Int. J. Remote Sens. 42, 7821–7841 (2021).Article 

Google Scholar 
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).Article 

Google Scholar 
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).Article 

Google Scholar 
Chen, X. & Yang, Y. Observed earlier start of the growing season from middle to high latitudes across the Northern Hemisphere snow-covered landmass for the period 2001–2014. Environ. Res. Lett. 15, 034042 (2020).Article 

Google Scholar 
Alatorre, L. C. et al. Temporal changes of NDVI for qualitative environmental assessment of mangroves: shrimp farming impact on the health decline of the arid mangroves in the Gulf of California (1990–2010). J. Arid Environ. 125, 98–109 (2016).Article 

Google Scholar 
Jacquemoud, S. & Baret, F. PROSPECT: a model of leaf optical properties spectra. Remote Sens. Environ. 34, 75–91 (1990).Article 

Google Scholar 
Wu, S. et al. Quantifying leaf optical properties with spectral invariants theory. Remote Sens. Environ. 253, 112131 (2021).Article 

Google Scholar 
Wang, Z. et al. Mapping foliar functional traits and their uncertainties across three years in a grassland experiment. Remote Sens. Environ. 221, 405–416 (2019).Article 

Google Scholar 
Van Leeuwen, W. & Huete, A. Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices. Remote Sens. Environ. 55, 123–138 (1996).Article 

Google Scholar 
Dechant, B. et al. NIRvP: a robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales. Remote Sens. Environ. 268, 112763 (2022).Article 

Google Scholar 
Zeng, Y. et al. Estimating near-infrared reflectance of vegetation from hyperspectral data. Remote Sens. Environ. 267, 112723 (2021).Article 

Google Scholar 
Claverie, M. et al. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens. Environ. 219, 145–161 (2018).Article 

Google Scholar 
Hantson, S. & Chuvieco, E. Evaluation of different topographic correction methods for Landsat imagery. Int. J. Appl. Earth Obs. Geoinf. 13, 691–700 (2011).
Google Scholar 
Zhang, H. K. et al. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sens. Environ. 215, 482–494 (2018).Article 

Google Scholar 
Gao, F., Masek, J., Schwaller, M. & Hall, F. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44, 2207–2218 (2006).Article 

Google Scholar 
Zhu, X. et al. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sens. Environ. 172, 165–177 (2016).Article 

Google Scholar 
Luo, Y., Guan, K. & Peng, J. STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product. Remote Sens. Environ. 214, 87–99 (2018).Article 

Google Scholar 
Houborg, R. & McCabe, M. F. Daily retrieval of NDVI and LAI at 3 m resolution via the fusion of CubeSat, Landsat, and MODIS data. Remote Sens. 10, 890 (2018).Article 

Google Scholar 
Kimm, H. et al. Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the US Corn Belt using Planet Labs CubeSat and STAIR fusion data. Remote Sens. Environ. 239, 111615 (2020).Article 

Google Scholar 
Kong, J. et al. Evaluation of four image fusion NDVI products against in-situ spectral-measurements over a heterogeneous rice paddy landscape. Agric. For. Meteorol. 297, 108255 (2021).Article 

Google Scholar 
Köhler, P. et al. Global retrievals of solar-induced chlorophyll fluorescence with TROPOMI: first results and intersensor comparison to OCO-2. Geophys. Res. Lett. 45, 10,456–10,463 (2018).Article 

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

Google Scholar 
Joiner, J., Yoshida, Y., Vasilkov, A. & Middleton, E. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences 8, 637–651 (2011).Article 

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

Google Scholar 
Qiu, B., Ge, J., Guo, W., Pitman, A. J. & Mu, M. Responses of Australian dryland vegetation to the 2019 heat wave at a subdaily scale. Geophys. Res. Lett. 47, e2019GL086569 (2020).
Google Scholar 
Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl Acad. Sci. USA 116, 11640–11645 (2019).Article 

Google Scholar 
Guanter, L. et al. Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence. Atmos. Meas. Tech. 8, 1337–1352 (2015).Article 

Google Scholar 
Knyazikhin, Y. et al. Hyperspectral remote sensing of foliar nitrogen content. Proc. Natl Acad. Sci. USA 110, E185–E192 (2013).
Google Scholar 
Li, X. & Xiao, J. A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sens. 11, 517 (2019).Article 

Google Scholar 
Zeng, Y. et al. Combining near-infrared radiance of vegetation and fluorescence spectroscopy to detect effects of abiotic changes and stresses. Remote Sens. Environ. 270, 112856 (2022).Article 

Google Scholar 
Shi, J. et al. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E. Remote Sens. Environ. 112, 4285–4300 (2008).Article 

Google Scholar 
Talebiesfandarani, S. et al. Microwave vegetation index from multi-angular observations and its application in vegetation properties retrieval: theoretical modelling. Remote Sens. 11, 730 (2019).Article 

Google Scholar 
Wigneron, J.-P. et al. SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives. Remote Sens. Environ. 254, 112238 (2021).Article 

Google Scholar 
Zhang, Y., Zhou, S., Gentine, P. & Xiao, X. Can vegetation optical depth reflect changes in leaf water potential during soil moisture dry-down events? Remote Sens. Environ. 234, 111451 (2019).Article 

Google Scholar 
Frappart, F. et al. Global monitoring of the vegetation dynamics from the vegetation optical depth (VOD): a review. Remote Sens. 12, 2915 (2020).Article 

Google Scholar 
Xiao, J., Fisher, J. B., Hashimoto, H., Ichii, K. & Parazoo, N. C. Emerging satellite observations for diurnal cycling of ecosystem processes. Nat. Plants 7, 877–887 (2021).Article 

Google Scholar 
Hashimoto, H. et al. New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests. Nat. Commun. 12, 684 (2021).Article 

Google Scholar 
Somkuti, P. et al. Solar-induced chlorophyll fluorescence from the Geostationary Carbon Cycle Observatory (GeoCarb): An extensive simulation study. Remote Sens. Environ. 263, 112565 (2021).Article 

Google Scholar 
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 

Google Scholar 
Richardson, A. D., Braswell, B. H., Hollinger, D. Y., Jenkins, J. P. & Ollinger, S. V. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol. Appl. 19, 1417–1428 (2009).Article 

Google Scholar 
Daughtry, C. S. Discriminating crop residues from soil by shortwave infrared reflectance. Agron. J. 93, 125–131 (2001).Article 

Google Scholar  More

Costanza, R. et al. Nature 387, 253–260 (1997).Article 

Google Scholar 
Toman, M. Ecol. Econom. 25, 57–60 (1998).Article 

Google Scholar 
Barbier, E. B., Burgess, J. C. & Folke, C. (eds) Paradise Lost? The Ecological Economics of Biodiversity (Routledge, 1994).
Google Scholar 
Daily, G. C. (ed.) Nature’s Services: Societal Dependence on Natural Ecosystems (Island, 1997).
Google Scholar 
Watson, R. T. et al. (eds) Global Biodiversity Assessment: Summary for Policy-Makers (Cambridge Univ. Press, 1995).
Google Scholar 
Reid, W. V. et al. Ecosystems and Human Well-Being: Synthesis (A Report of the Millennium Ecosystem Assessment) (Island, 2005).
Google Scholar 
Chichilnisky, G. & Heal, G. Nature 391, 629–630 (1998).Article 

Google Scholar 
Umaña Quesada, A. in Green Growth That Works: Natural Capital Policy and Finance Mechanisms from Around the World (eds Mandle, L. et al.) Ch. 13, 195–212 (Island, 2019).
Google Scholar 
Ouyang, Z. et al. in Green Growth That Works: Natural Capital Policy and Finance Mechanisms from Around the World (eds Mandle, L. et al.) Ch. 12, 177–194 (Island, 2019).
Google Scholar 
Salzman, J., Bennett, G., Carroll, N., Goldstein, A. & Jenkins, M. Nature Sustain. 1, 136–144 (2018).Article 

Google Scholar 
Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. The Global Assessment Report on Biodiversity and Ecosystem Services: Summary for Policymakers (eds Díaz, S. et al.) (IPBES, 2019).
Google Scholar 
Chaplin-Kramer, R. et al. Science 366, 255–258 (2019).PubMed 
Article 

Google Scholar 
Hamel, P. et al. npj Nature Urban Sustain. 1, 25 (2021).Article 

Google Scholar 
Mandle, L., Ouyang, Z., Salzman, J. & Daily, G. C. (eds) Green Growth That Works: Natural Capital Policy and Finance Mechanisms from Around the World (Island, 2019).
Google Scholar 
Dasgupta, P. The Economics of Biodiversity: The Dasgupta Review. Abridged Version (HM Treasury, 2021).
Google Scholar 
Arkema, K. K. et al. Proc. Natl Acad. Sci. USA 112, 7390–7395 (2015).PubMed 
Article 

Google Scholar 
Ouyang, Z. et al. Proc. Natl Acad. Sci. USA 117, 14593–14601 (2020).PubMed 
Article 

Google Scholar  More