Philippa Kaur

The modelConsider a unit-mass population of agents who repeatedly interact in pairs to play a symmetric stage game. The set of strategies available to each agent is finite and denoted by (S equiv {1, ldots , n}). A population state is a vector (x in X equiv {x in {mathbb{R}}^n_+: sum _{i in S} x_i = 1}), with (x_i) the fraction of the population playing strategy (i in S). Payoffs are described by a function (F: S times S rightarrow {mathbb{R}}), where F(i, j) is the payoff received by an agent playing strategy i when the opponent plays strategy j. As a shorthand, we refer to an undirected pair of individuals, one playing i and the other playing j, as an ij pair. The set of all possible undirected pairs is denoted by (mathscr {P}).The interaction structure is modeled as a function (p : X times mathscr {P} rightarrow left[ 0, 1/2 right] ) subject to (sum _{ij in mathscr {P}} p_{ij}(x)=1/2) (since the mass of pairs is half the mass of agents), with (p_{ij}(x)) indicating the mass of ij pairs formed in state x. Note that the mass of ij pairs can never exceed (min {x_i,x_j}), that is, (p_{ij}(x) le min {x_i,x_j}) for all x. We assume that p is continuous in X, and that (p_{ij}(x) > 0) if and only if (x_i > 0) and (x_j > 0 )—meaning that the probability of an ij pair being formed is strictly positive if and only if strategies i and j are played by someone. In the case of uniform random matching, (p_{ii} = x_i^2/2) and (p_{ij} = x_i x_j) for any i and (j ne i).The revision protocol is modeled as a function (phi : X times S times S rightarrow [-1,1]), where (phi _{ij}(x) in [-1,1]) is the probability that an ij pair will turn into an ii pair minus the probability that it will turn into a jj pair, conditional on the population state being x and an ij pair being formed. We assume that (phi ) is continuous in X. We note that by construction (phi _{ij}=-phi _{ji}) for all (i,j in S), and hence (phi _{ii}=0) for all (i in S). Our main assumption on the revision protocol is the following, which is met, among others, by pairwise proportional imitative and imitate-if-better rules22.
Assumption 1

For every (x in X), (phi _{ij}(x) > 0) if (F(i,j) > F(j,i)).
In what follows we consider a dynamical system in continuous time with state space X, characterized by the following equation of motion.

Definition 1

(Pairwise interact-and-imitate dynamics—PIID) For every (x in X) and every (i in S):$$begin{aligned} dot{x}_i = sum _{j in S} p_{ij}(x) phi _{ij}(x). end{aligned}$$
(1)

Main findingsGlobal asymptotic convergenceIn any purely imitative dynamics, if (x_i(t)=0), then (x_i(t^{prime})=0) for every (t^{prime} > t). This implies that we cannot hope for global asymptotic convergence in a strict sense. Thus, to assess convergence towards a certain state x in a meaningful way, we restrict our attention to those states where all strategies that have positive frequency in x have positive frequency as well. We denote by (X_x) the set of states whose support contains the support of x.

Definition 2

(Supremacy) Strategy (iin S) is supreme if (F(i,j) >F(j,i)) for every (j in S setminus {i}).
We note that under PIID, the concept of supremacy is closely related to that of asymmetry33,34, in that (F(i,j) > F(j,i)) implies that agents can only switch from strategy j to strategy i.

Proposition 1

If (i in S) is a supreme strategy, then state (x^* equiv left{ x in X : x_i = 1 right} ) is globally asymptotically stable for the dynamical system with state space (X_{x^*}) and PIID as equation of motion.
Relation to replicator dynamicsTo further characterize the dynamics induced by the pairwise interact-and-imitate protocol, we make two additional assumptions. First, matching is uniformly random, meaning that everyone in the population has the same probability of interacting with everyone else; formally, (p_{ii} = x_i^2/2) and (p_{ij} = x_i x_j) for all i and (j ne i). Second, the probability that an agent has to imitate the opponent is proportional to the difference in their payoffs if the opponent’s payoff exceeds her own, and is zero otherwise. As a consequence, (phi _{ij} = F(i,j) – F(j,i)) up to a proportionality factor. Let

(F left( i, x right) :=sum _j x_j F left( i, j right) ),

(F left( x, i right) :=sum _j x_j F left( j, i right) ), and

( F left( x, x right) :=sum _i sum _j x_i x_j F left( i, j right) ).

Under these assumptions, at any point in time, the motion of (x_i) is described by:$$begin{aligned} dot{x}_i&= sum _{j ne i} x_j x_i left[ F left( i, j right) – F left( j, i right) right] = x_i sum _{j} x_j left[ F left( i, j right) – F left( j, i right) right] nonumber \&= x_i left[ F left( i, x right) – F left( x, i right) right] , end{aligned}$$
(2)
which is a modified replicator equation. According to (2), for every strategy i chosen by one or more agents in the population, the rate of growth of the fraction of i-players, (dot{x}_i / x_i), equals the difference between the expected payoff from playing i in state x and the average payoff received by those who are matched against an agent playing i. In contrast, under standard replicator dynamics35, the fraction of agents playing i varies depending on the excess payoff of i with respect to the current average payoff in the whole population, i.e., (dot{x}_i = x_i left[ F left( i, x right) – F left( x, x right) right] ).A noteworthy feature of replicator dynamics is that they are always payoff monotone: for any (i,j in S), the proportions of agents playing i and j grow at rates that are ordered in the same way as the expected payoffs from the two strategies36. In the case of PIID, this result fails.

Proposition 2

Pairwise-Interact-and-Imitate dynamics need not satisfy payoff monotonicity.
To verify this, it is sufficient to consider any symmetric (2 times 2) game where (F left( i, j right) > F left( j, i right) ) but (F left( j, x right) > F left( i, x right) ) for some (x in X), meaning that i is the supreme strategy but j yields a higher expected payoff in state x. See Fig. 1 for an example where, in the case of uniform random matching, the above inequalities hold for any x; if strategies are updated according to the interact-and-imitate protocol, then this game only admits switches from i to j, therefore violating payoff monotonicity. Proposition 2 can have important consequences, including the survival of pure strategies that are strictly dominated.Survival of strictly dominated strategiesAn recurring topic in evolutionary game theory is to what extent does support exist for the idea that strictly dominated strategies will not be played. It has been shown that if strategy i does not survive the iterated elimination of pure strategies strictly dominated by other pure strategies, then the fraction of the population playing i will converge to zero in all payoff monotone dynamics37,38. This result does not hold in our case, as PIID is not payoff monotone.More precisely, under PIID, a strictly dominated strategy may be supreme and, therefore, not only survive but even end up being adopted by the whole population. This suggests that from an evolutionary perspective, support for the elimination of dominated strategies may be weaker than is often thought. Our result contributes to the literature on the conditions under which evolutionary dynamics fail to eliminate strictly dominated strategies in some games, examining a case which has not yet been studied39.To see that a strictly dominated strategy may be supreme, consider the simple example shown in Fig. 1. Here each agent has a strictly dominant strategy to play A; however, since the payoff from playing B against A exceeds that from playing A against B, strategy B is supreme. Thus, by Proposition 1, the population state in which all agents choose B is globally asymptotically stable.Figure 1A game where the supreme strategy is strictly dominated.Full size imageFigure 1 can also be used to comment on the relation between a supreme strategy and an evolutionary stable strategy, which is a widely used concept in evolutionary game theory40,41. Indeed, while B is the supreme strategy, A is the unique evolutionary stable strategy because it is strictly dominant. However, if F(B, A) were reduced below 2, holding everything else constant, then B would become both supreme and evolutionary stable. We therefore conclude that no particular relation holds between evolutionary stability and supremacy: neither one property implies the other, nor are they incompatible.ApplicationsHaving obtained general results for the class of finite symmetric games, we now restrict the discussion to the evolution of behavior in social dilemmas. We show that if the conditions of Proposition 1 are met, then inefficient conventions emerge in the Prisoner’s Dilemma, Stag Hunt, Minimum Effort, and Hawk–Dove games. Furthermore, this result holds both without and with the assumption that agents interact assortatively.Ineffectiveness of assortmentConsider the (2 times 2) game represented in Fig. 2. If (c > a > d > b), then mutual cooperation is Pareto superior to mutual defection but agents have a dominant strategy to defect. The resulting stage game is the Prisoner’s Dilemma, whose unique Nash equilibrium is (B, B). Moreover, since (F (B,A) > F(A,B)), B is the supreme strategy and the population state in which all agents defect is globally asymptotically stable.We stress that defection emerges in the long run for every matching rule satisfying our assumptions, and therefore also in the case of assortative interactions. Assortment reflects the tendency of similar people to clump together, and can play an important role in the evolution of cooperation42,43,44,45. Intuitively, when agents meet assortatively, the risk of cooperating in a social dilemma may be offset by a higher probability of playing against other cooperators. However, under PIID, this is not the case: the decision whether to adopt a strategy or not is independent of expected payoffs, and like-with-like interactions have no effect except to reduce the frequency of switches from A to B.Figure 2A (2 times 2) stage game.Full size imageEmergence of the maximin conventionIf (a > c > b), (a > d) and (d > b), then the game in Fig. 2 becomes a Stag Hunt game, which contrasts risky cooperation and safe individualism. The payoffs are such that both (left( A, Aright) ) and (left( B, Bright) ) are strict Nash equilibria, that (left( A, Aright) ) is Pareto superior to (left( B, Bright) ), and that B is the maximin strategy, i.e., the strategy which maximizes the minimum payoff an agent could possibly receive. We also assume that (a + c ne c + d), so that one of A and B is risk dominant46. If (a + b > c + d), then A (Stag) is both payoff and risk dominant. When the opposite inequality holds, the risk dominant strategy is B (Hare).Since (F (B,A) > F(A,B)), B is supreme independently of whether or not it is risk dominant to cooperate. This can result in large inefficiencies because, in the long run, the process will converge to the state in which all agents play the riskless strategy regardless of how rewarding social coordination is. As in the case of the Prisoner’s Dilemma, this holds for all matching rules satisfying our assumptions.Evolution of effort exertionIn a minimum effort game, agents simultaneously choose a strategy i, usually interpreted as a costly effort level, from a finite subset S of ({mathbb{R}}). An agent’s payoff depends on her own effort and on the minimum effort in the pair:$$begin{aligned} F left( i, j right) = alpha min left{ i, j right} – beta i , end{aligned}$$where (beta > 0) and (alpha > beta ) are the cost and benefit of effort, respectively. From a strategic viewpoint, this game can be seen as an extension of the Stag Hunt to cases where there are more than two actions. The best response to a choice of j by the opponent is to choose j as well, and coordinating on any common effort level gives a Nash equilibrium. Nash outcomes can be Pareto-ranked, with the highest-effort equilibrium being the best possible outcome for all agents. Thus, choosing a high i is rationalizable and potentially rewarding but may also result in a waste of effort.Under PIID, any (i > j) implies (phi _{ij} < 0) by Assumption 1, meaning that agents will tend to imitate the opponent when the opponent’s effort is lower than their own. The supreme strategy is therefore to exert as little effort as possible, and the population state in which all agents choose the minimum effort level is the unique globally asymptotically stable state.Emergence of aggressive behaviorConsider again the payoff matrix shown in Fig. 2. If (c > a > b > d), then the stage game is a Hawk–Dove game, which is often used to model the evolution of aggressive and sharing behaviors. Interactions can be framed as disputes over a contested resource. When two Doves (who play A) meet, they share the resource equally, whereas two Hawks (who play B) engage in a fight and suffer a cost. Moreover, when a Dove meets a Hawk, the latter takes the entire prize. Again we have that (F (A,B) < F(B,A)), implying that B is the supreme strategy and that the state where all agents play Hawk is the sole asymptotically stable state.The inefficiency that characterizes the (B, B) equilibrium in the Hawk–Dove game arises from the cost that Hawks impose on one another. This can be viewed as stemming from the fact that neither agent owns the resource prior to the interaction or cares about property. A way to overcome this problem may be to introduce a strategy associated with respect for ownership rights, the Bourgeois, who behaves as a Dove or Hawk depending on whether or not the opponent owns the resource41. If we make the standard assumption that each member of a pair has a probability of 1/2 to be an owner, then in all interactions where a Bourgeois is involved there is a 50 percent chance that she will behave hawkishly (i.e., fight for control over the resource) and a 50 percent chance that she will act as a Dove.Let R and C denote the agent chosen as row and column player, respectively, and let (omega _R) and (omega _C) be the states of the world in which R and C owns the resource. The payoffs of the resulting Hawk–Dove–Bourgeois game are shown in Fig. 3. If agents behave as expected payoff maximizers, then All Bourgeois can be singled out as the unique asymptotically stable state. Under PIID, this is not so; depending on who owns the resource, an agent playing C against an opponent playing B may either fight or avoid conflict and let the opponent have the prize. It is easy to see that (F left( C, B mid omega _R right) = F left( B,C mid omega _C right) = d), meaning that the payoff from playing C against B, conditional on owning the resource, equals the payoff from playing B against C conditional on not being an owner. In contrast, the payoff from playing C against B, conditional on not owning the resource, is always worse than that of the opponent, i.e., (F left( C, B mid omega _C right) = b < c = F left( B, C mid omega _R right) ). Thus, in every state of the world, B (Hawk) yields a payoff that is greater or equal to that from C (Bourgeois). Moreover, since (F left( B,A right) > F left( A, B right) ) in both states of the world, strategy B is weakly supreme by Definition 4, and play unfolds as an escalation of hawkishness and fights.Figure 3The Hawk–Dove–Bourgeois game.Full size image More

1.Igwe, J. C. & Onyegbado, C. C. A review of palm oil mill effluent (pome) water treatment. Glob. J. Environ. Res. 1, 54–62 (2007).
Google Scholar 
2.World Wild Fund (WWF). Overview WWF Statement on the 2020 Palm Oil Buyers Scorecard. https://www.worldwildlife.org/industries/palm-oil (2020). Accessed 22 Feb 2021.3.CNUCED. Huile de palme. New York. https://www.surunctad.org/commodities (2016). Accessed 10 Jan 2020.4.Hassan, M. A., Njeshu, G., Raji, A., Zhengwuvi, L. & Salisu, J. Small-Scale Palm Oil Processing in West and Central Africa: Development and Challenges. J. Appl. Sci. Environ. Sust. 2, 102–114 (2016).
Google Scholar 
5.Bala, J. D., Lalung, J., Al-Gheethi, A. A. S., Kaizar, H. & Ismail, N. Reduction of organic load and biodegradation of palm oil mill effluent by aerobic indigenous mixed microbial consortium isolated from palm oil mill effluent (POME). Water Conserv. Sci. Eng. 3, 139. https://doi.org/10.1007/s41101-018-0043-9 (2018).Article 

Google Scholar 
6.Nwoko, O. C., Ogunyemi, S. & Nkwocha, E. E. Effect of pre-treatment of palm oil mill effluent (POME) and cassava mill effluent (CME) on the growth of tomato (Lycopersicum esculentum). J. Appl. Sci. Environ. 14, 67. https://doi.org/10.4314/JASEM.V14I1.56493 (2010).Article 

Google Scholar 
7.Singh, G., Huan, L. K., Leng, T. & Kow D. L. Oil Palm and the Environment: A Malaysian Perspective. (Kuala Lumpur,
Malaysia, Malaysian Oil Palm Growers’ Council, 1999).8.Poku, K. Small-Scale Palm Oil Processing in Africa. Fao Agricultural Services Bulletin 148. http://www.fao.org/3/Y4355E/y4355e00.htm (2002) (ISSN 1010-1365). Accessed 22 Feb 2021.9.Ibekwe, A. M., Grieve, C. M. & Lyon, S. R. Characterization of microbial communities and composition in constructed dairy wetland wastewater effluent. Appl. Environ. Microbiol. 69, 5060. https://doi.org/10.1128/AEM.69.9.5060-5069.2003 (2003).CAS 
Article 
PubMed 

Google Scholar 
10.Sharuddin, S. S. et al. Bacterial community shift revealed Chromatiaceae and Alcaligenaceae as potential bioindicators in the receiving river due to palm oil mill effluent final discharge. Ecol. Indic. 82, 526–529. https://doi.org/10.1016/j.ecolind.2017.07.038 (2017).CAS 
Article 

Google Scholar 
11.CIAPOL. Arrêté N°011264/MINEEF/CIAPOL/SDIIC du 04 Nov.2008 portant réglementation des rejets et emissions des installations classées pour la protection de l’environnement, 11 (2008).
12.Soleimaninanadegani, M. & Manshad, S. Enhancement of biodegradation of palm oil mill effluents by local isolated microorganisms. Int. Sch. Res. Notices. 2014, Article ID 727049. https://doi.org/10.1155/2014/727049 (2014).Article 

Google Scholar 
13.Nwachukwu, J. N., Njoku, U. O., Agu, C. V., Okonkwo, C. C. & Obidiegwu, C. J. Impact of palm oil mill effluent (POME) contamination on soil enzyme activities and physicochemical properties. Res. J. Environ. Toxicol. 12, 34–41. https://doi.org/10.3923/rjet.2018.34.41 (2018).CAS 
Article 

Google Scholar 
14.Hii, K. L., Yeap, S. P. & Mashitah, M. D. Cellulase production from palm oil mill effluent in Malaysia: Economical and technical perspectives. Eng. Life Sci. 12, 7–28. https://doi.org/10.1002/elsc.201000228 (2012).CAS 
Article 

Google Scholar 
15.Ma, Q. et al. Identification of the microbial community composition and structure of coal-mine wastewater treatment plants. Microbiol. Res. 175, 1–5. https://doi.org/10.1016/j.micres.2014.12.013 (2015).CAS 
Article 
PubMed 

Google Scholar 
16.Wang, X., Hu, M., Xia, Y., Wen, X. & Kun, D. K. Pyrosequencing analysis of bacterial diversity in 14 wastewater treatment systems in China. Bioresour. Technol. 78, 7042–7047. https://doi.org/10.1128/AEM.01617-12 (2012).CAS 
Article 

Google Scholar 
17.Wang, Z. et al. Abundance and diversity of bacterial nitrifiers and denitrifiers and their functional genes in tannery wastewater treatment plants revealed by high-throughput sequencing. PLoS One 9, e113603. https://doi.org/10.1371/journal.pone.0113603 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
18.Caporaso, J. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624. https://doi.org/10.1038/ismej.2012.8 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Rana, S., Singh, L., Wahid, Z. & Liu, H. A recent overview of palm oil mill effluent management via bioreactor configurations. Curr. Pollut. Rep. 3, 254–267. https://doi.org/10.1007/s40726-017-0068-2 (2017).CAS 
Article 

Google Scholar 
20.Vuono, D. C. et al. Disturbance and temporal partitioning of the activated sludge metacommunity. ISME J. 9, 425–435. https://doi.org/10.1038/ismej.2014.139 (2015).CAS 
Article 
PubMed 

Google Scholar 
21.Jang, H. M., Kim, J. H., Ha, J. H. & Park, J. M. Bacterial and methanogenic archaeal communities during the single-stage anaerobic digestion of high-strength food wastewater. Bioresour. Technol. 165, 174–182. https://doi.org/10.1016/j.biortech.2014.02.028 (2014).CAS 
Article 
PubMed 

Google Scholar 
22.Mohd-Nor, D. et al. Dynamics of microbial populations responsible for biodegradation during the full-scale treatment of palm oil mill effluent. Microbes Environ. 34, 121. https://doi.org/10.1264/jsme2.ME18104 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
23.Sun, Z. et al. Identification and characterization of the dominant lactic acid bacteria from kurut: The naturally fermented yak milk in Qinghai, China. J. Gen. Appl. Microbiol. 56, 1–10. https://doi.org/10.2323/jgam.56.1 (2010).Article 
PubMed 

Google Scholar 
24.Webster, N. S. & Taylor, M. W. Marine sponges and their microbial symbionts: Love and other relationships. Environ. Microbiol. 14, 335–346 (2012).CAS 
Article 

Google Scholar 
25.Morrow, K. M., Fiore, C. L. & Lesser, M. P. Environmental drivers of microbial community shifts in the giant barrel sponge, Xestospongia muta, over a shallow to mesophotic depth gradient. Environ. Microbiol. 18, 2025–2038. https://doi.org/10.1111/1462-2920.13226 (2016).CAS 
Article 
PubMed 

Google Scholar 
26.Parman, A., Isa, M. N. M., Farah, F. B., Noorbatcha, B. A. & Salleh, H. M. Comparative metagenomics analysis of palm oil mill effluent (pome) using three different bioinformatics pipelines. IIUM Eng. J. 20, 1–11. https://doi.org/10.31436/iiumej.v20i1.909 (2019).Article 

Google Scholar 
27.Mwaikono, K. S. et al. High-throughput sequencing of 16S rRNa gene reveals substantial bacterial diversity on the municipal dumpsite. BMC Microbiol. 16, 145. https://doi.org/10.1186/s12866-016-0758-8 (2016).Article 
PubMed 

Google Scholar 
28.Silva-Bedoya, L. M., Sánchez-Pinzón, M. S., Cadavid-Restrepo, G. E. & Moreno-Herrera, C. X. Bacterial community analysis of an industrial wastewater treatment plant in Colombia with screening for lipid-degrading microorganisms. Microbiol. Res. 192, 313. https://doi.org/10.1016/j.micres.2016.08.006 (2016).CAS 
Article 
PubMed 

Google Scholar 
29.Lam, M. K. & Lee, K. T. Renewable and sustainable bioenergies production from palm oil mill effluent (POME): Win–win strategies toward better environmental protection. Biotechnol. Adv. 29, 124–141. https://doi.org/10.1016/j.biotechadv.2010.10.001 (2011).CAS 
Article 
PubMed 

Google Scholar 
30.Baharuddin, A. S., Wakisaka, M., Shirai, A.-A.Y.S., Abdul, R. & Hassan, M. A. Co-composting of empty fruit bunches and partially treated palm oil mill effluents in pilot scale. Int. J. Agric. Res. 4, 69–78. https://doi.org/10.3923/ijar.2009.69.78 (2009).CAS 
Article 

Google Scholar 
31.Morikawa-Sakura, M. S. et al. Application of Lactobacillus plantarum ATCC 8014 for wastewater treatment in fisheries industry processing. Jpn. J. Water Treat. Biol. 49, 1–10. https://doi.org/10.2521/jswtb.49.1 (2013).Article 

Google Scholar 
32.Ren, Z., You, W., Wu, S., Poetsch, A. & Xu, C. Secretomic analyses of Ruminiclostridium papyrosolvens reveal its enzymatic basis for lignocellulose degradation. Biotechnol. Biofuels 12, 183. https://doi.org/10.1186/s13068-019-1522-8 (2019).CAS 
Article 
PubMed 

Google Scholar 
33.Lee, J. Z., Logan, A., Terry, S. & Spear, J. R. Microbial response to single-cell protein production and brewery wastewater treatment. Microb. Biotechnol. 8, 65. https://doi.org/10.1111/1751-7915.12128 (2015).CAS 
Article 
PubMed 

Google Scholar 
34.Ye, L. & Zhang, T. Bacterial communities in different sections of a municipal wastewater treatment plant revealed by 16S rDNA 454 pyrosequencing. Appl. Microbiol. Biotechnol. 97, 2681–2690 (2013).CAS 
Article 

Google Scholar 
35.Stubbs, S., Mao, L., Waddington, R. J. & Embery, G. Hydrolytic and depolymerising enzyme activity of Prevotella intermedia and Prevotella nigrescens. Oral Dis. 2, 272. https://doi.org/10.1111/j.1601-0825.1996.tb00237.x (1996).CAS 
Article 
PubMed 

Google Scholar 
36.Komagata, K., Iino, T. & Yamada, Y. The family Acetobacteraceae. In The Prokaryotes (eds Rosenberg, E. et al.) 3–78 (Springer, 2014).Chapter 

Google Scholar 
37.Pires, J. F., Cardoso, L. S., Schwan, R. F. & Silva, C. F. Diversity of microbiota found in coffee processing wastewater treatment plant. World J. Microbiol. Biotechnol. 33, 211. https://doi.org/10.1007/s11274-017-2372-9 (2017).Article 
PubMed 

Google Scholar 
38.Song, Z. Q., Wang, F. P. & Zhi, X. Y. Bacterial and archaeal diversities in Yunnan and Tibetan hot springs, China. Environ. Microbiol. 15, 1160–1175 (2013).CAS 
Article 

Google Scholar 
39.Li, J., Liu, R., Tao, Y. & Li, G. Archaea in wastewater treatment: Current research and emerging technology. Archaea 2018, 1. https://doi.org/10.1155/2018/6973294 (2018).CAS 
Article 

Google Scholar 
40.Khan, M. A., Khan, S. T. & Sequeira, M. C. Comparative analysis of bacterial and archaeal population structure by illumina sequencing of 16S rRNA genes in three municipal anaerobic sludge digesters. Res. Sq. https://doi.org/10.21203/rs.3.rs-60183/v1 (2020).Article 

Google Scholar 
41.Mladenovska, Z., Dabrowski, S. & Ahring, B. K. Anaerobic digestion of manure and mixture of manure with lipids: Biogas reactor performance and microbial community analysis. Water Sci. Technol. 48, 271–278 (2013).Article 

Google Scholar 
42.Gerardi, M. H. Wastewater Bacteria (Wiley, 2006).Book 

Google Scholar 
43.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414. https://doi.org/10.1111/1462-2920.13023 (2016).CAS 
Article 
PubMed 

Google Scholar 
44.Andrews, S. FastQC: a quality control tool for high throughput sequence data (Online). https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010). Accessed 15 Sept 2019.45.R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019). Accessed 8 Jan 2020.46.Callahan, B. J. et al. DADA2: High-resolution sample inference from illumina amplicon data. Nat. Methods 13, 581. https://doi.org/10.1038/nmeth.3869 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590. https://doi.org/10.1093/nar/gks1219 (2012).CAS 
Article 

Google Scholar 
48.Paradis, E., Julien, C. & Korbinian, S. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289. https://doi.org/10.1093/bioinformatics/btg412 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, 61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
CAS 
Article 

Google Scholar 
50.Oksanen, J. F. et al. vegan: Community Ecology Package. R package version 2.4-0. https://CRAN.R-project.org/package=vegan (2018). Accessed 8 Jan 2020.51.Lahti, L. & Sudarshan, S. Tools for microbiome analysis in R. Version 1.10.0. https://www.microbiome.github.com/microbiome (2017). Accessed 8 Jan 2020.52.Kenkel, N. C. & Orloci, L. Applying metric and nonmetric multidimensional scaling to ecological studies: Some new results. Ecology 67, 919. https://doi.org/10.2307/1939814 (1986).Article 

Google Scholar 
53.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar  More

1.Weiner, J. Plant Reproductive Ecology: Patterns and Strategies (Oxford Univ. Press, 1988).2.Ashman, T. L. & Schoen, D. J. How long should flowers live? Nature 371, 788–791 (1994).CAS 
Article 

Google Scholar 
3.Donald, C. M. The breeding of crop ideotypes. Euphytica 17, 385–403 (1968).Article 

Google Scholar 
4.Unkovich, M., Baldock, J. & Forbes, M. Variability in harvest index of grain crops and potential significance for carbon accounting: examples from Australian agriculture. Adv. Agron. 105, 173–219 (2010).Article 

Google Scholar 
5.Tamagno, S., Sadras, V. O., Ortez, O. A. & Ciampitti, I. A. Allometric analysis reveals enhanced reproductive allocation in historical set of soybean varieties. Field Crop Res. 248, 107717 (2020).Article 

Google Scholar 
6.Hector, A. et al. Plant diversity and productivity experiments in European grasslands. Science 286, 1123–1127 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Huang, Y. et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Letourneau, D. K. et al. Does plant diversity benefit agroecosystems? A synthetic review. Ecol. Appl. 21, 9–21 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Li, C. et al. Syndromes of production in intercropping impact yield gains. Nat. Plants 6, 653–660 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
11.McConnaughay, K. D. M. & Coleman, J. S. Biomass allocation in plants: ontogeny or optimality? A test along three resource gradients. Ecology 80, 2581–2593 (1999).Article 

Google Scholar 
12.Bonser, S. P. & Aarssen, L. W. Allometry and plasticity of meristem allocation throughout development in Arabidopsis thaliana. J. Ecol. 89, 72–79 (2001).Article 

Google Scholar 
13.Reekie, E. G. & Bazzaz, F. A. Reproductive Allocation in Plants (Elsevier Academic Press, 2005).14.Wang, T. H., Zhou, D. W., Wang, P. & Zhang, H. X. Size-dependent reproductive effort in Amaranthus retroflexus: the influence of planting density and sowing date. Can. J. Bot. 84, 485–492 (2006).Article 

Google Scholar 
15.Gurr, G. M. et al. Multi-country evidence that crop diversification promotes ecological intensification of agriculture. Nat. Plants 2, 16014 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Li, C. et al. Yield gain, complementarity and competitive dominance in intercropping in China: a meta-analysis of drivers of yield gain using additive partitioning. Eur. J. Agron. 113, 125987 (2020).CAS 
Article 

Google Scholar 
17.Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Li, L., Tilman, D., Lambers, H. & Zhang, F. S. Plant diversity and overyielding: insights from belowground facilitation of intercropping in agriculture. New Phytol. 203, 63–69 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
20.Brooker, R. W. et al. Improving intercropping: a synthesis of research in agronomy, plant physiology and ecology. New Phytol. 206, 107–117 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Martin-Guay, M. O., Paquette, A., Dupras, J. & Rivest, D. The new green revolution: sustainable intensification of agriculture by intercropping. Sci. Total Environ. 615, 767–772 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Bazzaz, F. A., Chiariello, N. R., Coley, P. D. & Pitelka, L. F. Allocating resources to reproduction and defense. Bioscience 37, 58–67 (1987).Article 

Google Scholar 
23.Hartnett, D. C. Size-dependent allocation to sexual and vegetative reproduction in 4 clonal composites. Oecologia 84, 254–259 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Vega, C. R. C., Sadras, V. O., Andrade, F. H. & Uhart, S. A. Reproductive allometry in soybean, maize and sunflower. Ann. Bot. 85, 461–468 (2000).Article 

Google Scholar 
25.Gifford, R. M., Thorne, J. H., Hitz, W. D. & Giaquinta, R. T. Crop productivity and photoassimilate partitioning. Science 225, 801–808 (1984).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Andrade, F. H. et al. Kernel number determination in maize. Crop Sci. 39, 453–459 (1999).Article 

Google Scholar 
27.Milla, R., Osborne, C. P., Turcotte, M. M. & Violle, C. Plant domestication through an ecological lens. Trends Ecol. Evol. 30, 463–469 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Niklas, K. J. Plant Allometry: The Scaling of Form and Process (Univ. of Chicago Press, 1994).29.Echarte, L. & Andrade, F. H. Harvest index stability of Argentinean maize hybrids released between 1965 and 1993. Field Crop Res. 82, 1–12 (2003).Article 

Google Scholar 
30.Weiner, J., Campbell, L. G., Pino, J. & Echarte, L. The allometry of reproduction within plant populations. J. Ecol. 97, 1220–1233 (2009).Article 

Google Scholar 
31.Sugiyama, S. & Bazzaz, F. A. Size dependence of reproductive allocation: the influence of resource availability, competition and genetic identity. Funct. Ecol. 12, 280–288 (1998).Article 

Google Scholar 
32.Weiner, J. Allocation, plasticity and allometry in plants. Perspect. Plant Ecol. 6, 207–215 (2004).Article 

Google Scholar 
33.Weiner, J. et al. Is reproductive allocation in Senecio vulgaris plastic? Botany 87, 475–481 (2009).Article 

Google Scholar 
34.Schmid, B. & Weiner, J. Plastic relationships between reproductive and vegetative mass in Solidago altissima. Evolution 47, 61–74 (1993).PubMed 
Article 
PubMed Central 

Google Scholar 
35.Schmid, B. & Pfisterer, A. B. Species vs community perspectives in biodiversity experiments. Oikos 100, 620–621 (2003).Article 

Google Scholar 
36.Lipowsky, A. et al. Plasticity of functional traits of forb species in response to biodiversity. Perspect. Plant Ecol. Evol. Syst. 17, 66–77 (2015).Article 

Google Scholar 
37.Abakumova, M., Zobel, K., Lepik, A. & Semchenko, M. Plasticity in plant functional traits is shaped by variability in neighbourhood species composition. New Phytol. 211, 455–463 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Zhu, J. Q., van der Werf, W., Anten, N. P. R., Vos, J. & Evers, J. B. The contribution of phenotypic plasticity to complementary light capture in plant mixtures. New Phytol. 207, 1213–1222 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Niklaus, P. A., Baruffol, M., He, J. S., Ma, K. P. & Schmid, B. Can niche plasticity promote biodiversity-productivity relationships through increased complementarity? Ecology 98, 1104–1116 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
40.Eziz, A. et al. Drought effect on plant biomass allocation: a meta-analysis. Ecol. Evol. 7, 11002–11010.41.Joshi, J. et al. Local adaptation enhances performance of common plant species. Ecol. Lett. 4, 536–544 (2001).Article 

Google Scholar 
42.Li, J. et al. Variations in maize dry matter, harvest index, and grain yield with plant density. Agron. J. 107, 829–834 (2015).Article 

Google Scholar 
43.Gou, F., van Ittersum, M. K., Wang, G. Y., van der Putten, P. E. L. & van der Werf, W. Yield and yield components of wheat and maize in wheat-maize intercropping in the Netherlands. Eur. J. Agron. 76, 17–27.44.Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. Ecol. Lett. 21, 763–778.45.Roscher, C. & Schumacher, J. Positive diversity effects on productivity in mixtures of arable weed species as related to density–size relationships. J. Plant Ecol. 9, 792–804 (2016).Article 

Google Scholar 
46.Roscher, C. et al. Overyielding in experimental grassland communities – irrespective of species pool or spatial scale. Ecol. Lett. 8, 419–429.47.Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Schmid, B., Baruffol, M., Wang, Z. & Niklaus, P. A. A guide to analyzing biodiversity experiments. J. Plant Ecol. 10, 91–110.49.Rosenthal, R. & Rosnow, R. L. Contrast Analysis: Focused Comparisons in the Analysis of Variance (Cambridge Univ. Press, 2010).50.Díaz-Sierra, R., Verwijmeren, M., Rietkerk, M., de Dios, V. R. & Baudena, M. A new family of standardized and symmetric indices for measuring the intensity and importance of plant neighbour effects. Methods Ecol. Evol. 8, 580–591 (2017).Article 

Google Scholar 
51.Poorter, H. & Garnier, E. in Handbook of Functional Plant Ecology (eds Pugnaire, F. I. & Valladares, F.) 81–120 (Marcel Dekker, 1999).52.Grime, J. P. Evidence for existence of 3 primary strategies in plants and its relevance to ecological and evolutionary theory. Am. Nat. 111, 1169–1194 (1977).Article 

Google Scholar 
53.Wilson, P. J., Thompson, K. & Hodgson, J. G. Specific leaf area and leaf dry matter content as alternative predictors of plant strategies. New Phytol. 143, 155–162 (1999).Article 

Google Scholar 
54.Poorter, H., Niinemets, U., Poorter, L., Wright, I. J. & Villar, R. Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis. New Phytol. 182, 565–588 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
55.Lavorel, S. & Grigulis, K. How fundamental plant functional trait relationships scale-up to trade-offs and synergies in ecosystem services. J. Ecol. 100, 128–140 (2012).Article 

Google Scholar 
56.Conti, G. & Díaz, S. Plant functional diversity and carbon storage – an empirical test in semi‐arid forest ecosystems. J. Ecol. 101, 18–28 (2013).CAS 
Article 

Google Scholar 
57.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); https://www.r-project.org/58.Lüdecke, D. ggeffects: Tidy data frames of marginal effects from regression models. J. Open Source Softw. 3, 772 (2018).Article 

Google Scholar 
59.Lüdecke, D. sjPlot: data visualization for statistics in social science. Zenodo https://doi.org/10.5281/zenodo.1308157 (2018). More

Green and red lighting might be good for migratory birds and sea turtles, but could have undesirable effects if marine algae are present. Credit: Getty

Ecology
24 June 2021
Red light, green light: both signal ‘go’ to deadly algae

Artificial lighting thought to be more wildlife-friendly than white light could encourage algal blooms.

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Green or red lights in seaside areas have been proposed as alternatives to white light to protect wildlife. But new experiments show that exposure to red or green light at night boosts the growth of some ocean algae — including species known to rob waters of oxygen.Little is known about the impact of artificial light on marine life, even though many brightly lit cities are coastal. To address that knowledge gap, Sofie Spatharis at the University of Glasgow, UK, and her colleagues exposed a mix of microscopic marine algae collected from Scottish waters to standard white light. They also exposed the mixture to red and green lights, which have been proposed to minimize impacts on sea turtles and migratory seabirds, respectively.The team found that all light colours enhanced growth of the microalgae mix. Red light had the most pronounced effect, doubling the number of cells produced. The proportions of species in the mixture also shifted: both red and green light especially favoured growth of harmful species in the Skeletonema genus, which form dense blooms that are deadly to fish.

Proc. R. Soc. B (2021)

Ecology More

1.Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

Google Scholar 
2.Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).PubMed 

Google Scholar 
3.Updated Zero Draft of the Post-2020 Global Biodiversity Framework (Convention on Biological Diversity, 2020); https://www.cbd.int/doc/c/3064/749a/0f65ac7f9def86707f4eaefa/post2020-prep-02-01-en.pdf4.Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).CAS 
Article 

Google Scholar 
5.Loh, J. et al. The Living Planet Index: using species population time series to track trends in biodiversity. Philos. Trans. R. Soc. B 360, 289–295 (2005).Article 

Google Scholar 
6.Collen, B. et al. Monitoring change in vertebrate abundance: the Living Planet Index. Conserv. Biol. 23, 317–327 (2009).Article 

Google Scholar 
7.McRae, L., Deinet, S. & Freeman, R. The diversity-weighted Living Planet Index: controlling for taxonomic bias in a global biodiversity indicator. PLoS ONE 12, e0169156 (2017).Article 

Google Scholar 
8.Almond, R.E.A., Grooten M. & Petersen, T. (eds) Living Planet Report 2020—Bending the Curve of Biodiversity Loss (WWF, 2020).9.Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).10.Global Biodiversity Outlook 5 (Convention on Biological Diversity, 2020).11.Jaspers, A. Can a single index track the state of global biodiversity? Biol. Conserv. 246, 108524 (2020).Article 

Google Scholar 
12.Leung, B. et al. Clustered versus catastrophic global vertebrate declines. Nature 588, 267–271 (2020).CAS 
Article 

Google Scholar 
13.Buckland, S. T., Studeny, A. C., Magurran, A. E., Illian, J. & Newson, S. E. The geometric mean of relative abundance indices: a biodiversity measure with a difference. Ecosphere 2, 100 (2011).14.de Valpine, P. & Hastings, A. Fitting population models incorporating process noise and observation error. Ecol. Monogr. 72, 57–76.15.Daskalova, G. N., Myers-Smith, I. H. & Godlee, J. L. Rare and common vertebrates span a wide spectrum of population trends. Nat. Commun. 11, 4394 (2020).CAS 
Article 

Google Scholar 
16.Living Planet Report 2020. Technical Supplement: Living Planet Index (WWF, 2020); https://f.hubspotusercontent20.net/hubfs/4783129/LPR/PDFs/ENGLISH%20-%20TECH%20SUPPLIMENT.pdf17.Vellend, M. Conceptual synthesis in community ecology. Quart. Rev. Biol. 85, 183–206 (2010).Article 

Google Scholar 
18.Vellend, M. et al. Assessing the relative importance of neutral stochasticity in ecological communities. Oikos 123, 1420–1430 (2014).Article 

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

Google Scholar 
20.Gravel, D., Guichard, F. & Hochberg, M. E. Species coexistence in a variable world. Ecol. Lett. 14, 828–839 (2011).Article 

Google Scholar 
21.Kotze, D. J., O’Hara, R. B. & Lehvävirta, S. Dealing with varying detection probability, unequal sample sizes and clumped distributions in count data. PLoS ONE 7, e40923 (2012).CAS 
Article 

Google Scholar 
22.Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: a quantitative review. PLoS ONE 9, e111436 (2014).Article 

Google Scholar 
23.Di Fonzo, M., Collen, B. & Mace, G. M. A new method for identifying rapid decline dynamics in wild vertebrate populations. Ecol. Evol. 3, 2378–2391 (2013).Article 

Google Scholar 
24.Maxwell, S. L. et al. Being smart about SMART environmental targets. Science 347, 1075–1076 (2015).CAS 
Article 

Google Scholar 
25.Butchart, S. H. M., Di Marco, M. & Watson, J. E. M. Formulating SMART commitments on biodiversity: lessons from the Aichi Targets. Conserv Lett. 9, 457–468 (2016).Article 

Google Scholar 
26.Green, E. J. et al. Relating characteristics of global biodiversity targets to reported progress. Conserv. Biol. 33, 1360–1369 (2019).Article 

Google Scholar 
27.Dornelas, M. et al. A balance of winners and losers in the Anthropocene. Ecol. Lett. 22, 847–854 (2019).Article 

Google Scholar 
28.Fournier, A. M. V., White, E. R. & Heard, S. B. Site‐selection bias and apparent population declines in long‐term studies. Conserv. Biol. 33, 1370–1379 (2019).Article 

Google Scholar 
29.Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
Article 

Google Scholar 
30.Papworth, S. K., Rist, J., Coad, L. & Milner-Gulland, E. J. Evidence for shifting baseline syndrome in conservation. Conserv Lett. 2, 93–100 (2009).
Google Scholar 
31.Barnosky, A. D. et al. Approaching a state shift in Earth’s biosphere. Nature 486, 52–58 (2012).CAS 
Article 

Google Scholar 
32.Nicholson, E. et al. Scenarios and models to support global conservation targets. Trends Ecol. Evol. 34, 57–68 (2019).Article 

Google Scholar 
33.Bull, J. W., Strange, N., Smith, R. J. & Gordon, A. Reconciling multiple counterfactuals when evaluating biodiversity conservation impact in social-ecological systems. Conserv. Biol. 35, 510–521 (2021).Article 

Google Scholar 
34.van Strien, A. J. et al. Modest recovery of biodiversity in a western European country: The Living Planet Index for the Netherlands. Biol. Conserv. 200, 44–50 (2016).Article 

Google Scholar 
35.Wauchope, H. S., Amano, T., Sutherland, W. J. & Johnston, A. When can we trust population trends? A method for quantifying the effects of sampling interval and duration. Methods Ecol. Evol. 10, 2067–2078 (2019).Article 

Google Scholar 
36.Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2020.11.001 (2020).37.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).38.Buschke, F. T. Biodiversity trajectories and the time needed to achieve no net loss through averted-loss biodiversity offsets. Ecol. Model 352, 54–57 (2017).Article 

Google Scholar  More

1.De’Ath G, Fabricius KE, Sweatman H, Puotinen M. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc Natl Acad Sci U.S.A. 2012;109:17995–9.PubMed 
PubMed Central 
Article 

Google Scholar 
2.Randall CJ, van Woesik R. Contemporary white-band disease in Caribbean corals driven by climate change. Nat Clim Chang. 2015;5:375–9.Article 

Google Scholar 
3.Maynard J, van Hooidonk R, Eakin CM, Puotinen M, Garren M, Williams G, et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nat Clim Chang. 2015;5:688–95.Article 

Google Scholar 
4.Cziesielski MJ, Schmidt-Roach S, Aranda M. The past, present, and future of coral heat stress studies. Ecol Evol. 2019;9:10055–66.PubMed 
PubMed Central 
Article 

Google Scholar 
5.Bourne D, Iida Y, Uthicke S, Smith-Keune C. Changes in coral-associated microbial communities during a bleaching event. ISME J. 2008;2:350–63.CAS 
PubMed 
Article 

Google Scholar 
6.van de Water JAJM, Chaib De Mares M, Dixon GB, Raina JB, Willis BL, Bourne DG, et al. Antimicrobial and stress responses to increased temperature and bacterial pathogen challenge in the holobiont of a reef-building coral. Mol Ecol. 2018;27:1065–80.PubMed 
Article 
CAS 

Google Scholar 
7.Sussman M, Mieog JC, Doyle J, Victor S, Willis BL, Bourne DG. Vibrio zinc-metalloprotease causes photoinactivation of coral endosymbionts and coral tissue lesions. PLoS ONE. 2009;4:1–14.8.Ben-Haim Y, Zicherman-Keren M, Rosenberg E. Temperature-regulated bleaching and lysis of the coral Pocillopora damicornis by the novel pathogen Vibrio coralliilyticus. Appl Environ Microbiol. 2003;69:4236–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Garren M, Son K, Raina J-B, Rusconi R, Menolascina F, Shapiro OH, et al. A bacterial pathogen uses dimethylsulfoniopropionate as a cue to target heat-stressed corals. ISME J. 2014;8:999–1007.CAS 
PubMed 
Article 

Google Scholar 
10.Garren M, Son K, Tout J, Seymour JR, Stocker R. Temperature-induced behavioral switches in a bacterial coral pathogen. ISME J. 2016;10:1363–72.CAS 
PubMed 
Article 

Google Scholar 
11.Barbara GM, Mitchell JG. Marine bacterial organisation around point-like sources of amino acids. FEMS Microbiol Ecol. 2003;43:99–109.CAS 
PubMed 
Article 

Google Scholar 
12.Seymour JR, Marcos, Stocker R. Resource patch formation and exploitation throughout the marine microbial food web. Am Nat. 2009;173:E15–29.CAS 
PubMed 
Article 

Google Scholar 
13.Son K, Menolascina F, Stocker R. Speed-dependent chemotactic precision in marine bacteria. Proc Natl Acad Sci U.S.A. 2016;113:8624–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Meron D, Efrony R, Johnson WR, Schaefer AL, Morris PJ, Rosenberg E, et al. Role of Flagella in virulence of the coral pathogen Vibrio coralliilyticus. Appl Environ Microbiol. 2009;75:5704–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Ushijima B, Häse CC. Influence of chemotaxis and swimming patterns on the virulence of the coral pathogen Vibrio coralliilyticus. J Bacteriol. 2018;200:1–16.Article 

Google Scholar 
16.Crossland CJ, Barnes DJ, Borowitzka MA. Diurnal lipid and mucus production in the staghorn coral Acropora acuminata. Mar Biol. 1980;60:81–90.17.Davies PS. The role of zooxanthellae in the nutritional energy requirements of Pocillopora eydouxi. Coral Reefs. 1984;2:181–6.18.Rix L, de Goeij JM, Mueller CE, Struck U, Middelburg JJ, van Duyl FC, et al. Coral mucus fuels the sponge loop in warm-and cold-water coral reef ecosystems. Sci Rep. 2016;6:1–11.Article 
CAS 

Google Scholar 
19.Naumann MS, Haas A, Struck U, Mayr C, El-Zibdah M, Wild C. Organic matter release by dominant hermatypic corals of the Northern Red Sea. Coral Reefs. 2010;29:649–59.Article 

Google Scholar 
20.Wild C, Huettel M, Klueter A, Kremb SG, Rasheed MYM, Jørgensen BB. Coral mucus functions as an energy carrier and particle trap in the reef ecosystem. Nature. 2004;428:66–70.CAS 
PubMed 
Article 

Google Scholar 
21.Bythell JC, Wild C. Biology and ecology of coral mucus release. J Exp Mar Bio Ecol. 2011;408:88–93.Article 

Google Scholar 
22.Bakshani CR, Morales-Garcia AL, Althaus M, Wilcox MD, Pearson JP, Bythell JC, et al. Evolutionary conservation of the antimicrobial function of mucus: a first defence against infection. NPJ Biofilms Microbiomes. 2018;14:1–12.
Google Scholar 
23.Gibbin E, Gavish A, Krueger T, Kramarsky-Winter E, Shapiro O, Guiet R, et al. Vibrio coralliilyticus infection triggers a behavioural response and perturbs nutritional exchange and tissue integrity in a symbiotic coral. ISME J. 2019;13:989–1003.24.Gavish AR, Shapiro OH, Kramarsky-Winter E, Vardi A. Microscale tracking of coral-vibrio interactions. ISME Communications. 2021;1:1–18.25.Shapiro OH, Fernandez VI, Garren M, Guasto JS, Debaillon-Vesque FP, Kramarsky-Winter E, et al. Vortical ciliary flows actively enhance mass transport in reef corals. Proc Natl Acad Sci U.S.A. 2014;111:13391–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Seymour JR, Ahmed T, Stocker R. A microfluidic chemotaxis assay to study microbial behavior in diffusing nutrient patches. Limnol Oceanogr Methods. 2008;6:477–88.CAS 
Article 

Google Scholar 
27.Penn K, Wang J, Fernando SC, Thompson JR. Secondary metabolite gene expression and interplay of bacterial functions in a tropical freshwater cyanobacterial bloom. ISME J. 2014;8:1866–78.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.Article 
CAS 

Google Scholar 
29.Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:1–12.30.Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U.S.A. 2005;102:15545–50.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34:267–73.CAS 
PubMed 
Article 

Google Scholar 
32.Schneider WR, Doetsch RN. Effect of viscosity on bacterial motility. J Bacteriol. 1974;117:696–701.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Martinez VA, Schwarz-Linek J, Reufer M, Wilson LG, Morozov AN, Poon WCK. Flagellated bacterial motility in polymer solutions. Proc Natl Acad Sci U.S.A. 2014;111:17771–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Kimes NE, Grim CJ, Johnson WR, Hasan NA, Tall BD, Kothary MH, et al. Temperature regulation of virulence factors in the pathogen Vibrio coralliilyticus. ISME J. 2012;6:835–46.CAS 
PubMed 
Article 

Google Scholar 
35.Kojima S, Yamamoto K, Kawagishi I, Homma M. The polar flagellar motor of Vibrio cholerae is driven by an Na+ motive force. J Bacteriol. 1999;181:1927–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Sowa Y, Hotta H, Homma M, Ishijima A. Torque-speed relationship of the Na+-driven flagellar motor of Vibrio alginolyticus. J Mol Biol. 2003;327:1043–51.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Milo R, Phillips R. Cell biology by the numbers. 1st ed. New York, NY: Garland Science; 2016.38.Crossland CJ. In situ release of mucus and DOC-lipid from the corals Acropora variabilis and Stylophora pistillata in different light regimes. Coral Reefs. 1987;6:35–42.CAS 
Article 

Google Scholar 
39.Wild C, Woyt H, Huettel M. Influence of coral mucus on nutrient fluxes in carbonate sands. Mar Ecol Prog Ser. 2005;287:87–98.40.Ducklow HW, Mitchell R. Composition of mucus released by coral reef coelenterates. Limnol Oceanogr. 1979;24:706–14.CAS 
Article 

Google Scholar 
41.Meikle P, Richards GN, Yellowlees D. Structural determination of the oligosaccharide side chains from a glycoprotein isolated from the mucus of the coral Acropora formosa. J Biol Chem. 1987;262:16941–7.CAS 
PubMed 
Article 

Google Scholar 
42.Coddeville B, Maes E, Ferrier-Pagès C, Guerardel Y. Glycan profiling of gel forming mucus layer from the scleractinian symbiotic coral Oculina arbuscula. Biomacromolecules. 2011;12:2064–73.CAS 
PubMed 
Article 

Google Scholar 
43.Hasegawa H, Häse CC. TetR-type transcriptional regulator VtpR functions as a global regulator in Vibrio tubiashii. Appl Environ Microbiol. 2009;75:7602–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Ball AS, Chaparian RR, van Kessel JC. Quorum sensing gene regulation by LuxR/HapR master regulators in Vibrios. J Bacteriol. 2017;199:1–13.45.Rutherford ST, Van Kessel JC, Shao Y, Bassler BL. AphA and LuxR/HapR reciprocally control quorum sensing in vibrios. Genes Dev. 2011;25:397–408.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Hammer BK, Bassler BL. Quorum sensing controls biofilm formation in Vibrio cholerae. Mol Microbiol. 2003;50:101–4.CAS 
PubMed 
Article 

Google Scholar 
47.Waters CM, Lu W, Rabinowitz JD, Bassler BL. Quorum sensing controls biofilm formation in Vibrio cholerae through modulation of cyclic Di-GMP levels and repression of vpsT. J Bacteriol. 2008;190:2527–36.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Burger AH. Quorum Sensing in the Hawai’ian Coral Pathogen Vibrio coralliilyticus strain OCN008. University of Hawaii at Manoa; 2017.49.Yildiz FH, Schoolnik GK. Vibrio cholerae O1 El Tor: identification of a gene cluster required for the rugose colony type, exopolysaccharide production, chlorine resistance, and biofilm formation. Proc Natl Acad Sci U.S.A. 1999;96:4028–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Fong JCN, Syed KA, Klose KE, Yildiz FH. Role of Vibrio polysaccharide (vps) genes in VPS production, biofilm formation and Vibrio cholerae pathogenesis. Microbiology. 2010;156:2757–69.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Fong JCN, Karplus K, Schoolnik GK, Yildiz FH. Identification and characterization of RbmA, a novel protein required for the development of rugose colony morphology and biofilm structure in Vibrio cholerae. J Bacteriol. 2006;188:1049–59.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Fong JCN, Yildiz FH. The rbmBCDEF gene cluster modulates development of rugose colony morphology and biofilm formation in Vibrio cholerae. J Bacteriol. 2007;189:2319–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.DiRita VJ, Mekalanos JJ. Periplasmic interaction between two membrane regulatory proteins, ToxR and ToxS, results in signal transduction and transcriptional activation. Cell. 1991;64:29–37.CAS 
PubMed 
Article 

Google Scholar 
54.Almagro-Moreno S, Root MZ, Taylor RK. Role of ToxS in the proteolytic cascade of virulence regulator ToxR in Vibrio cholerae. Mol Microbiol. 2015;98:963–76.CAS 
PubMed 
Article 

Google Scholar 
55.Lee SE, Ryu PY, Kim SY, Kim YR, Koh JT, Kim OJ, et al. Production of Vibrio vulnificus hemolysin in vivo and its pathogenic significance. Biochem Biophys Res Commun. 2004;324:86–91.56.Senoh M, Okita Y, Shinoda S, Miyoshi S. The crucial amino acid residue related to inactivation of Vibrio vulnificus hemolysin. Micro Pathog. 2008;44:78–83.CAS 
Article 

Google Scholar 
57.Bröms JE, Ishikawa T, Wai SN, Sjöstedt A. A functional VipA-VipB interaction is required for the type VI secretion system activity of Vibrio cholerae O1 strain A1552. BMC Microbiol. 2013;13:1–12.Article 
CAS 

Google Scholar 
58.Vizcaino MI, Johnson WR, Kimes NE, Williams K, Torralba M, Nelson KE, et al. Antimicrobial resistance of the coral pathogen Vibrio coralliilyticus and Caribbean sister phylotypes isolated from a diseased octocoral. Micro Ecol. 2010;59:646–57.Article 

Google Scholar 
59.Ritchie KB. Regulation of microbial populations by coral surface mucus and mucus-associated bacteria. Mar Ecol Prog Ser. 2006;322:1–14.CAS 
Article 

Google Scholar 
60.Nissimov J, Rosenberg E, Munn CB. Antimicrobial properties of resident coral mucus bacteria of Oculina patagonica. FEMS Microbiol Lett. 2009;292:210–5.CAS 
PubMed 
Article 

Google Scholar 
61.Shnit-Orland M, Kushmaro A. Coral mucus-associated bacteria: a possible first line of defense. FEMS Microbiol Ecol. 2009;67:371–80.CAS 
PubMed 
Article 

Google Scholar 
62.Rypien KL, Ward JR, Azam F. Antagonistic interactions among coral-associated bacteria. Environ Microbiol. 2010;12:28–39.CAS 
PubMed 
Article 

Google Scholar 
63.Alagely A, Krediet CJ, Ritchie KB, Teplitski M. Signaling-mediated cross-talk modulates swarming and biofilm formation in a coral pathogen Serratia marcescens. ISME J. 2011;5:1609–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Stocker R, Seymour JR, Samadani A, Hunt DE, Polz MF. Rapid chemotactic response enables marine bacteria to exploit ephemeral microscale nutrient patches. Proc Natl Acad Sci U.S.A. 2008;105:4209–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Polz MF, Hunt DE, Preheim SP, Weinreich DM. Patterns and mechanisms of genetic and phenotypic differentiation in marine microbes. Philos Trans R Soc B Biol Sci. 2006;361:2009–21.Article 

Google Scholar 
66.Taylor JR, Stocker R. Trade-offs of chemotactic foraging in turbulent water. Science. 2012;338:675–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Krediet CJ, Ritchie KB, Cohen M, Lipp EK, Patterson Sutherland K, Teplitski M. Utilization of mucus from the coral Acropora palmata by the pathogen Serratia marcescens and by environmental and coral commensal bacteria. Appl Environ Microbiol. 2009;75:3851–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Krediet CJ, Ritchie KB, Alagely A, Teplitski M. Members of native coral microbiota inhibit glycosidases and thwart colonization of coral mucus by an opportunistic pathogen. ISME J. 2013;7:980–90.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Packer HL, Armitage JP. The chemokinetic and chemotactic behavior of Rhodobacter sphaeroides: two independent responses. J Bacteriol. 1994;176:206–12.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Deepika D, Karmakar R, Tirumkudulu MS, Venkatesh KV. Variation in swimming speed of Escherichia coli in response to attractant. Arch Microbiol. 2015;197:211–22.CAS 
PubMed 
Article 

Google Scholar 
71.Zhulin IB, Armitage JP. Motility, chemokinesis, and methylation-independent chemotaxis in Azospirillum brasilense. J Bacteriol. 1993;175:952–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Ramos HC, Rumbo M, Sirard J-C. Bacterial flagellins: mediators of pathogenicity and host immune responses in mucosa. Trends Microbiol. 2004;12:509–17.CAS 
PubMed 
Article 

Google Scholar 
73.Reed KC, Muller EM, van Woesik R. Coral immunology and resistance to disease. Dis Aquat Organ. 2010;90:85–92.CAS 
PubMed 
Article 

Google Scholar 
74.Ushijima B, Videau P, Poscablo D, Stengel JW, Beurmann S, Burger AH, et al. Mutation of the toxR or mshA genes from Vibrio coralliilyticus strain OCN014 reduces infection of the coral Acropora cytherea. Environ Microbiol. 2016;18:4055–67.CAS 
PubMed 
Article 

Google Scholar 
75.Ushijima B, Richards GP, Watson MA, Schubiger CB, Häse CC. Factors affecting infection of corals and larval oysters by Vibrio coralliilyticus. PLoS ONE. 2018;13:e0199475.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
76.Peterson KM, Mekalanos JJ. Characterization of the Vibrio cholerae ToxR regulon: identification of novel genes involved in intestinal colonization. Infect Immun. 1988;56:2822–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Provenzano D, Klose KE. Altered expression of the ToxR-regulated porins OmpU and OmpT diminishes Vibrio cholerae bile resistance, virulence factor expression, and intestinal colonization. Proc Natl Acad Sci U.S.A. 2000;97:10220–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Waters CM, Bassler BL. The Vibrio harveyi quorum-sensing system uses shared regulatory components to discriminate between multiple autoinducers. Genes Dev. 2006;20:2754–67.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Mukherjee S, Bassler BL. Bacterial quorum sensing in complex and dynamically changing environments. Nat Rev Microbiol. 2019;17:371–82.80.Sikora AE, Zielke RA, Lawrence DA, Andrews PC, Sandkvist M. Proteomic analysis of the Vibrio cholerae type II secretome reveals new proteins, including three related serine proteases. J Biol Chem. 2011;286:16555–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Korotkov KV, Sandkvist M, Hol WGJ. The type II secretion system: biogenesis, molecular architecture and mechanism. Nat Rev Microbiol. 2012;10:336–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Stathopoulos C, Hendrixson DR, Thanassi DG, Hultgren SJ, St. Geme III JW, Curtiss III R. Secretion of virulence determinants by the general secretory pathway in Gram-negative pathogens: an evolving story. Microbes Infect. 2000;2:1061–72.83.Hood RD, Singh P, Hsu FS, Güvener T, Carl MA, Trinidad RRS, et al. A Type VI secretion system of Pseudomonas aeruginosa targets a toxin to bacteria. Cell Host Microbe. 2010;7:25–37.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Zheng J, Ho B, Mekalanos JJ. Genetic analysis of anti-amoebae and anti-bacterial activities of the Type VI secretion system in Vibrio cholerae. PLoS ONE. 2011;6:e23876.85.MacIntyre DL, Miyata ST, Kitaoka M, Pukatzki S. The Vibrio cholerae type VI secretion system displays antimicrobial properties. Proc Natl Acad Sci U.S.A. 2010;107:19520–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Lee SH, Hava DL, Waldor MK, Camilli A. Regulation and temporal expression patterns of Vibrio cholerae virulence genes during infection. Cell. 1999;99:625–34.87.Pennetzdorfer N, Lembke M, Pressler K, Matson JS, Reidl J, Schild S. Regulated proteolysis in Vibrio cholerae allowing rapid adaptation to stress conditions. Front Cell Infect Microbiol. 2019;9:1–9.Article 
CAS 

Google Scholar 
88.Liu R, Chen H, Zhang R, Zhou Z, Hou Z, Gao D, et al. Comparative transcriptome analysis of Vibrio splendidus JZ6 reveals the mechanism of its pathogenicity at low temperatures. Appl Environ Microbiol. 2016;82:2050–61.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Hughes TP, Anderson KD, Connolly SR, Heron SF, Kerry JT, Lough JM, et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science. 2018;359:80–3.90.Vezzulli L, Previati M, Pruzzo C, Marchese A, Bourne DG, Cerrano C, et al. Vibrio infections triggering mass mortality events in a warming Mediterranean Sea. Environ Microbiol. 2010;12:2007–19.91.Zaneveld JR, Burkepile DE, Shantz AA, Pritchard CE, McMinds R, Payet JP, et al. Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales. Nat Commun. 2016;7:1–12.Article 
CAS 

Google Scholar  More

1.Erb, K. H. et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553, 73–76 (2018).CAS 
PubMed 
Article 

Google Scholar 
2.Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).CAS 
PubMed 
Article 

Google Scholar 
3.Drake, J. B. et al. Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Glob. Ecol. Biogeogr. 12, 147–159 (2003).Article 

Google Scholar 
4.Lefsky, M. A. et al. Lidar remote sensing of above-ground biomass in three biomes. Glob. Ecol. Biogeogr. 11, 393–399 (2002).Article 

Google Scholar 
5.Duncanson, L. et al. The importance of consistent global forest aboveground biomass product validation. Surv. Geophys. 40, 979–999 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Spawn, S. A., Sullivan, C. C., Lark, T. J. & Gibbs, H. K. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci. Data 7, 112 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Ottaviani, G. et al. The neglected belowground dimension of plant dominance. Trends Ecol. Evol. 35, 763–766 (2020).PubMed 
Article 

Google Scholar 
8.Jackson, L. E., Burger, M. & Cavagnaro, T. R. Roots, nitrogen transformations, and ecosystem services. Annu. Rev. Plant Biol. 59, 341–363 (2008).CAS 
PubMed 
Article 

Google Scholar 
9.Gill, R. A. & Jackson, R. B. Global patterns of root turnover for terrestrial ecosystems. New Phytol. 147, 13–31 (2000).Article 

Google Scholar 
10.Robinson, D. Implications of a large global root biomass for carbon sink estimates and for soil carbon dynamics. Proc. R. Soc. Lond. B 274, 2753–2759 (2007).CAS 

Google Scholar 
11.Bardgett, R. D., Mommer, L. & De Vries, F. T. Going underground: root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699 (2014).PubMed 
Article 

Google Scholar 
12.Ribeiro, S. C. et al. Above- and belowground biomass in a Brazilian Cerrado. For. Ecol. Manage. 262, 491–499 (2011).Article 

Google Scholar 
13.Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root:shoot ratios in terrestrial biomes. Glob. Chang. Biol. 12, 84–96 (2006).Article 

Google Scholar 
14.Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).CAS 
PubMed 
Article 

Google Scholar 
15.Ruesch, A. S. & Gibbs, H. H. K. New IPCC Tier-1 Global Biomass Carbon Map for the Year 2000 (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, 2008).16.Chen, J. L. & Reynolds, J. F. A coordination model of whole-plant carbon allocation in relation to water stress. Ann. Bot. 80, 45–55 (1997).CAS 
Article 

Google Scholar 
17.Franklin, O. et al. Modeling carbon allocation in trees: a search for principles. Tree Physiol. 32, 648–666 (2012).CAS 
PubMed 
Article 

Google Scholar 
18.Bloom, A. J., Chapin, F. S. & Mooney, H. A. Resource limitation in plants—an economic analogy. Annu. Rev. Ecol. Syst. 16, 363–392 (1985).Article 

Google Scholar 
19.Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).CAS 
PubMed 
Article 

Google Scholar 
20.Reich, P. in Plant Roots: The Hidden Half (eds. Waisel, Y. et al.) 205–220 (Marcel Dekker, 2006).21.Ledo, A. et al. Tree size and climatic water deficit control root to shoot ratio in individual trees globally. New Phytol. 217, 8–11 (2018).PubMed 
Article 

Google Scholar 
22.Qi, Y., Wei, W., Chen, C. & Chen, L. Plant root-shoot biomass allocation over diverse biomes: a global synthesis. Glob. Ecol. Conserv. 18, e00606 (2019).Article 

Google Scholar 
23.Reich, P. B. et al. Temperature drives global patterns in forest biomass distribution in leaves, stems, and roots. Proc. Natl Acad. Sci. USA 111, 13721–13726 (2014).CAS 
PubMed 
Article 

Google Scholar 
24.De Frenne, P. et al. Latitudinal gradients as natural laboratories to infer species’ responses to temperature. J. Ecol. 101, 784–795 (2013).Article 

Google Scholar 
25.Luo, Y. Terrestrial carbon-cycle feedback to climate warming. Annu. Rev. Ecol. Evol. Syst. 38, 683–712 (2007).Article 

Google Scholar 
26.Jackson, R. B. et al. A global analysis of root distributions for terrestrial biomes. Oecologia 108, 389–411 (1996).CAS 
PubMed 
Article 

Google Scholar 
27.Malhi, Y., Doughty, C. & Galbraith, D. The allocation of ecosystem net primary productivity in tropical forests. Philos. Trans. R. Soc. Lond. B 366, 3225–3245 (2011).CAS 
Article 

Google Scholar 
28.Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).Article 

Google Scholar 
29.Cairns, M. A., Brown, S., Helmer, E. H. & Baumgardner, G. A. Root biomass allocation in the world’s upland forests. Oecologia 111, 1–11 (1997).PubMed 
Article 

Google Scholar 
30.McCarthy, M. C. & Enquist, B. J. Consistency between an allometric approach and optimal partitioning theory in global patterns of plant biomass allocation. Funct. Ecol. 21, 713–720 (2007).Article 

Google Scholar 
31.Barton, C. V. M. & Montagu, K. D. Effect of spacing and water availability on root:shoot ratio in Eucalyptus camaldulensis. For. Ecol. Manage. 221, 52–62 (2006).Article 

Google Scholar 
32.Enquist, B. J. & Niklas, K. J. Global allocation rules for patterns of biomass partitioning in seed plants. Science 295, 1517–1520 (2002).CAS 
PubMed 
Article 

Google Scholar 
33.Goward, S. N., Tucker, C. J. & Dye, D. G. North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Vegetatio 64, 3–14 (1985).Article 

Google Scholar 
34.Manzoni, S., Jackson, R. B., Trofymow, J. A. & Porporato, A. The global stoichiometry of litter nitrogen mineralization. Science 321, 684–686 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Kaiser, C., Franklin, O., Dieckmann, U. & Richter, A. Microbial community dynamics alleviate stoichiometric constraints during litter decay. Ecol. Lett. 17, 680–690 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Jiao, F., Shi, X. R., Han, F. P. & Yuan, Z. Y. Increasing aridity, temperature and soil pH induce soil C-N-P imbalance in grasslands. Sci. Rep. 6, 19601 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).Article 

Google Scholar 
38.De Deyn, G. B., Cornelissen, J. H. C. & Bardgett, R. D. Plant functional traits and soil carbon sequestration in contrasting biomes. Ecol. Lett. 11, 516–531 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Tjoelker, M. G., Craine, J. M., Wedin, D., Reich, P. B. & Tilman, D. Linking leaf and root trait syndromes among 39 grassland and savannah species. New Phytol. 167, 493–508 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Personeni, E. & Loiseau, P. How does the nature of living and dead roots affect the residence time of carbon in the root litter continuum? Plant Soil 267, 129–141 (2004).CAS 
Article 

Google Scholar 
41.Tuanmu, M. N. & Jetz, W. A global 1-km consensus land-cover product for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 23, 1031–1045 (2014).Article 

Google Scholar 
42.Pan, Y., Birdsey, R. A., Phillips, O. L. & Jackson, R. B. The structure, distribution, and biomass of the world’s forests. Annu. Rev. Ecol. Evol. Syst. 44, 593–622 (2013).Article 

Google Scholar 
43.Jackson, R. B., Mooney, H. A. & Schulze, E. D. A global budget for fine root biomass, surface area, and nutrient contents. Proc. Natl Acad. Sci. USA 94, 7362–7366 (1997).CAS 
PubMed 
Article 

Google Scholar 
44.Genet, H., Bréda, N. & Dufrêne, E. Age-related variation in carbon allocation at tree and stand scales in beech (Fagus sylvatica L.) and sessile oak (Quercus petraea (Matt.) Liebl.) using a chronosequence approach. Tree Physiol. 30, 177–192 (2009).PubMed 
Article 

Google Scholar 
45.De Castro, E. A. & Kauffman, J. B. Ecosystem structure in the Brazilian Cerrado: a vegetation gradient of aboveground biomass, root mass and consumption by fire. J. Trop. Ecol. 14, 263–283 (1998).Article 

Google Scholar 
46.Ding, B. & Sun, J. Study on biomass of Korean pine plantation in east mountain areas of northeast China. Bull. Bot. Res. 9, 149–157 (1989).
Google Scholar 
47.Ding, B., Liu, S. & Cai, T. Studies on biological productivity of artificial forests of Dahurian larches. Chin. J. Plant Ecol. 14, 226–236 (1990).
Google Scholar 
48.Ding, B. & Sun, J. Accumulation and distribution of productivity and nutrient element in natural Manchurian ash. J. Northeast For. Univ. 4, 1–9 (1989).
Google Scholar 
49.Dossa, E. L., Fernandes, E. C. M., Reid, W. S. & Ezui, K. Above- and belowground biomass, nutrient and carbon stocks contrasting an open-grown and a shaded coffee plantation. Agrofor. Syst. 72, 103–115 (2008).Article 

Google Scholar 
50.Epron, D. et al. Do changes in carbon allocation account for the growth response to potassium and sodium applications in tropical Eucalyptus plantations? Tree Physiol. 32, 667–679 (2012).CAS 
PubMed 
Article 

Google Scholar 
51.Fonseca, W., Rey Benayas, J. M. & Alice, F. E. Carbon accumulation in the biomass and soil of different aged secondary forests in the humid tropics of Costa Rica. For. Ecol. Manage. 262, 1400–1408 (2011).Article 

Google Scholar 
52.Goodman, R. C. et al. Amazon palm biomass and allometry. For. Ecol. Manage. 310, 994–1004 (2013).Article 

Google Scholar 
53.Greenland, D. J. & Kowal, J. M. L. Nutrient content of the moist tropical forest of Ghana. Plant Soil 12, 154–173 (1960).CAS 
Article 

Google Scholar 
54.He, Y. et al. Carbon storage capacity of monoculture and mixed-species plantations in subtropical China. For. Ecol. Manage. 295, 193–198 (2013).Article 

Google Scholar 
55.Aiba, M. & Nakashizuka, T. Variation in juvenile survival and related physiological traits among dipterocarp species co‐existing in a Bornean forest. J. Veg. Sci. 18, 379–388 (2007).Article 

Google Scholar 
56.Jha, K. K. Carbon storage and sequestration rate assessment and allometric model development in young teak plantations of tropical moist deciduous forest, India. J. For. Res. 26, 589–604 (2015).CAS 
Article 

Google Scholar 
57.Kalita, R. M., Das, A. K. & Nath, A. J. Allometric equations for estimating above- and belowground biomass in Tea (Camellia sinensis (L.) O. Kuntze) agroforestry system of Barak Valley, Assam, northeast India. Biomass Bioenergy 83, 42–49 (2015).Article 

Google Scholar 
58.Kenzo, T. et al. Development of allometric relationships for accurate estimation of above- and below-ground biomass in tropical secondary forests in Sarawak, Malaysia. J. Trop. Ecol. 25, 371–386 (2009).Article 

Google Scholar 
59.Kenzo, T. et al. Allometric equations for accurate estimation of above-ground biomass in logged-over tropical rainforests in Sarawak, Malaysia. J. For. Res. 14, 365–372 (2009).CAS 
Article 

Google Scholar 
60.Kraenzel, M., Castillo, A., Moore, T. & Potvin, C. Carbon storage of harvest-age teak (Tectona grandis) plantations, Panama. For. Ecol. Manage. 173, 213–225 (2003).Article 

Google Scholar 
61.Kuyah, S., Dietz, J., Muthuri, C., van Noordwijk, M. & Neufeldt, H. Allometry and partitioning of above- and below-ground biomass in farmed eucalyptus species dominant in Western Kenyan agricultural landscapes. Biomass Bioenergy 55, 276–284 (2013).Article 

Google Scholar 
62.Liu, S., Cai, Y. & Cai, T. in Long-term Research on Forest Ecosystems (ed. Zhou, X.) 419–427 (Northeast Forestry Univ. Press, 1991).63.Luo, T. et al. Root biomass along subtropical to alpine gradients: global implication from Tibetan transect studies. For. Ecol. Manage. 206, 349–363 (2005).Article 

Google Scholar 
64.Markesteijn, L. & Poorter, L. Seedling root morphology and biomass allocation of 62 tropical tree species in relation to drought- and shade-tolerance. J. Ecol. 97, 311–325 (2009).Article 

Google Scholar 
65.McNicol, I. M. et al. Development of allometric models for above and belowground biomass in swidden cultivation fallows of northern Laos. For. Ecol. Manage. 357, 104–116 (2015).Article 

Google Scholar 
66.Aiba, M. & Nakashizuka, T. Sapling structure and regeneration strategy in 18 Shorea species co-occurring in a tropical rainforest. Ann. Bot. 96, 313–321 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
67.Menaut, J. C. & Cesar, J. Structure and primary productivity of Lamto savannas, Ivory Coast. Ecology 60, 1197–1210 (1979).Article 

Google Scholar 
68.Morais, V. A. et al. Estoques de carbono e biomassa de um fragmento de cerradão em Minas Gerais, Brasil. Cerne 19, 237–245 (2013).Article 

Google Scholar 
69.Mugasha, W. A. et al. Allometric models for prediction of above- and belowground biomass of trees in the miombo woodlands of Tanzania. For. Ecol. Manage. 310, 87–101 (2013).Article 

Google Scholar 
70.Návar, J. Plasticity of biomass component allocation patterns in semiarid Tamaulipan thornscrub and dry temperate pine species of northeastern Mexico. Polibotánica 31, 121–141 (2011).
Google Scholar 
71.Njana, M. A., Eid, T., Zahabu, E. & Malimbwi, R. Procedures for quantification of belowground biomass of three mangrove tree species. Wetl. Ecol. Manage. 23, 749–764 (2015).Article 

Google Scholar 
72.Nogueira Junior, L. R., Engel, V. L., Parrotta, J. A., de Melo, A. C. G. & Ré, D. S. Equações alométricas para estimativa da biomassa arbórea em plantios mistos com espécies nativas na restauração da Mata Atlântica. Biota Neotrop. 14, 1–9 (2014).Article 

Google Scholar 
73.Peichl, M. & Arain, M. A. Above- and belowground ecosystem biomass and carbon pools in an age-sequence of temperate pine plantation forests. Agric. For. Meteorol. 140, e20130084 (2006).Article 

Google Scholar 
74.Battles, J. J. et al. Vegetation composition, structure, and biomass of two unpolluted watersheds in the Cordillera de Piuchué, Chiloé Island, Chile. Plant Ecol. 158, 5–19 (2002).Article 

Google Scholar 
75.Ryan, C. M., Williams, M. & Grace, J. Above- and belowground carbon stocks in a miombo woodland landscape of Mozambique. Biotropica 43, 423–432 (2011).Article 

Google Scholar 
76.Saint-André, L. et al. Age-related equations for above- and below-ground biomass of a Eucalyptus hybrid in Congo. For. Ecol. Manage. 205, 199–214 (2005).Article 

Google Scholar 
77.Aryal, D. R., De Jong, B. H. J., Ochoa-Gaona, S., Esparza-Olguin, L. & Mendoza-Vega, J. Carbon stocks and changes in tropical secondary forests of southern Mexico. Agric. Ecosyst. Environ. 195, 220–230 (2014).Article 

Google Scholar 
78.Schepaschenko, D. et al. A dataset of forest biomass structure for Eurasia. Sci. Data 4, 170070 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Schroth, G., D’Angelo, S. A., Teixeira, W. G., Haag, D. & Lieberei, R. Conversion of secondary forest into agroforestry and monoculture plantations in Amazonia: consequences for biomass, litter and soil carbon stocks after 7 years. For. Ecol. Manage. 163, 131–150 (2002).Article 

Google Scholar 
80.Schulze, E. D. et al. Rooting depth, water availability, and vegetation cover along an aridity gradient in Patagonia. Oecologia 108, 503–511 (1996).Article 

Google Scholar 
81.Stolbovoi, V. & McCallum, I. Land resources of Russia [CD] (International Institute for Applied Systems Analysis and the Russian Academy of Science, 2002); http://www.iiasa.ac.at/Research/FOR/russia_cd/guide.htm82.Wang, L. et al. Biomass allocation patterns across China’s terrestrial biomes. PLoS ONE 9, e93566 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
83.Wauters, J. B., Coudert, S., Grallien, E., Jonard, M. & Ponette, Q. Carbon stock in rubber tree plantations in Western Ghana and Mato Grosso (Brazil). For. Ecol. Manage. 255, 2347–2361 (2008).Article 

Google Scholar 
84.Williams-Linera, G. Biomass and nutrient content in two successional stages of tropical wet forest in Uxpanapa, Mexico. Biotropica 15, 275–284 (1983).Article 

Google Scholar 
85.Xu, Y. et al. Improving allometry models to estimate the above- and belowground biomass of subtropical forest, China. Ecosphere 6, 289 (2015).Article 

Google Scholar 
86.Youkhana, A. H. & Idol, T. W. Allometric models for predicting above- and belowground biomass of Leucaena-KX2 in a shaded coffee agroecosystem in Hawaii. Agrofor. Syst. 83, 331–345 (2011).Article 

Google Scholar 
87.Zhang, H. et al. Biogeographical patterns of biomass allocation in leaves, stems, and roots in China’s forests. Sci. Rep. 5, 15997 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Castellanos, J., Maass, M. & Kummerow, J. Root biomass of a dry deciduous tropical forest in Mexico. Plant Soil 131, 225–228 (1991).Article 

Google Scholar 
89.Zheng, Z., Feng, Z., Cao, M., Li, Z. & Zhang, J. Forest structure and biomass of a tropical seasonal rain forest in Xishuangbanna, southwest China. Biotropica 38, 318–327 (2006).Article 

Google Scholar 
90.Návar, J. Root stock biomass and productivity assessments of reforested pine stands in northern Mexico. For. Ecol. Manage. 338, 139–147 (2015).Article 

Google Scholar 
91.Wang, X., Fang, J. & Zhu, B. Forest biomass and root–shoot allocation in northeast China. For. Ecol. Manage. 255, 4007–4020 (2008).Article 

Google Scholar 
92.Chen, D. K., Zhou, X. F., Zhao, H. X., Wang, Y. H. & Jing, Y. Y. Study on the structure, function and succession of the four types in natural secondary forest. J. Northeast For. Univ. 2, 1–20 (1982).
Google Scholar 
93.Chidumayo, E. N. Estimating tree biomass and changes in root biomass following clear-cutting of Brachystegia-Julbernardia (miombo) woodland in central Zambia. Environ. Conserv. 41, 54–63 (2014).Article 

Google Scholar 
94.Coll, L., Potvin, C., Messier, C. & Delagrange, S. Root architecture and allocation patterns of eight native tropical species with different successional status used in open-grown mixed plantations in Panama. Trees 22, 585–596 (2008).Article 

Google Scholar 
95.Das, D. K. & Chaturvedi, O. P. Structure and function of Populus deltoides agroforestry systems in eastern India: 1. dry matter dynamics. Agrofor. Syst. 65, 215–221 (2005).Article 

Google Scholar 
96.Ni, J. Estimating net primary productivity of grasslands from field biomass measurements in temperate northern China. Plant Ecol. 174, 217–234 (2011).Article 

Google Scholar 
97.Olson, R. et al. NPP Multi-Biome: Summary Data from Intensive Studies at 125 Sites, 1936–2006 (ORNL DAAC, accessed 19 June 2019); https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=135298.Perez, C. A. & Frangi, J. L. Grassland biomass dynamics along an altitudinal gradient in the pampa. J. Range Manage. 53, 518–528 (2007).Article 

Google Scholar 
99.Perez-Quezada, J. F. F., Delpiano, C. A. A., Snyder, K. A. A., Johnson, D. A. A. & Franck, N. Carbon pools in an arid shrubland in Chile under natural and afforested conditions. J. Arid Environ. 75, 29–37 (2011).Article 

Google Scholar 
100.Pornon, A., Boutin, M. & Lamaze, T. Contribution of plant species to the high N retention capacity of a subalpine meadow undergoing elevated N deposition and warming. Environ. Pollut. 245, 235–242 (2019).CAS 
PubMed 
Article 

Google Scholar 
101.Ramakrishnan, P. S. & Ram, S. C. Vegetation, biomass and productivity of seral grasslands of Cherrapunji in north-east India. Vegetatio 74, 47–53 (1988).Article 

Google Scholar 
102.Shaver, G. R., Laundre, J. A., Giblin, A. E. & Nadelhoffer, K. J. Changes in live plant biomass, primary production, and species composition along a riverside toposequence in Arctic Alaska, USA. Arct. Alp. Res. 28, 363–379 (2006).Article 

Google Scholar 
103.Smith, J. M. B. & Klinger, L. F. Aboveground:belowground phytomass ratios in Venezuelan paramo vegetation and their significance. Arct. Alp. Res. 17, 189–198 (2006).Article 

Google Scholar 
104.Sun, J. et al. Effects of grazing regimes on plant traits and soil nutrients in an alpine steppe, northern Tibetan Plateau. PLoS ONE 9, e108821 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
105.Wang, P. et al. Belowground plant biomass allocation in tundra ecosystems and its relationship with temperature. Environ. Res. Lett. 11, 055003 (2016).Article 
CAS 

Google Scholar 
106.Yang, Y., Fang, J., Ji, C. & Han, W. Above- and belowground biomass allocation in Tibetan grasslands. J. Veg. Sci. 20, 177–184 (2009).Article 

Google Scholar 
107.Yang, Y., Fang, J., Ma, W., Guo, D. & Mohammat, A. Large-scale pattern of biomass partitioning across China’s grasslands. Glob. Ecol. Biogeogr. 19, 268–277 (2010).Article 

Google Scholar 
108.Geng, H. L., Wang, Y. H., Wang, F. Y. & Jia, B. R. The dynamics of root-shoot ratio and its environmental effective factors of recovering Leymus chinensis steppe vegetation in Inner Mongolia, China. Acta Ecol. Sin. 28, 4629–4634 (2008).Article 

Google Scholar 
109.Hui, D. & Jackson, R. B. Geographical and interannual variability in biomass partitioning in grassland ecosystems: a synthesis of field data. New Phytol. 169, 85–93 (2006).CAS 
PubMed 
Article 

Google Scholar 
110.Jouquet, P., Tavernier, V., Abbadie, L. & Lepage, M. Nests of subterranean fungus-growing termites (Isoptera, Macrotermitinae) as nutrient patches for grasses in savannah ecosystems. Afr. J. Ecol. 43, 191–196 (2005).Article 

Google Scholar 
111.Leonid, U. et al. Impact of climate and grazing on biomass components of eastern Russia typical steppe. J. Integr. Agric. 13, 1183–1192 (2014).Article 

Google Scholar 
112.Lucash, M. S., Farnsworth, B. & Winner, W. E. Response of sagebrush steppe species to elevated CO2 and soil temperature. West. N. Am. Nat. 65, 80–86 (2005).
Google Scholar 
113.Luo, W. et al. Patterns of plant biomass allocation in temperate grasslands across a 2500-km transect in northern China. PLoS ONE 8, e71749 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
114.Barbour, M. G. Desert dogma reexamined: root/shoot productivity and plant spacing. Am. Midl. Nat. 89, 41–57 (1973).Article 

Google Scholar 
115.Becker, P., Sharbini, N. & Yahya, R. Root architecture and root:shoot allocation of shrubs and saplings in two lowland tropical forests: implications for life-form composition. Biotropica 31, 93–101 (1999).
Google Scholar 
116.Becker, P. & Castillo, A. Root architecture of shrubs and saplings in the understory of a tropical moist forest in lowland Panama. Biotropica 22, 242–249 (1990).Article 

Google Scholar 
117.Beier, C. et al. Carbon and nitrogen balances for six shrublands across Europe. Glob. Biogeochem. Cycles 23, GB4008 (2009).Article 
CAS 

Google Scholar 
118.Bhatt, Y. D., Rawat, Y. S. & Singh, S. P. Changes in ecosystem functioning after replacement of forest by Lantana shrubland in Kumaun Himalaya. J. Veg. Sci. 5, 67–70 (1994).Article 

Google Scholar 
119.Caldwell, M. M., White, R. S., Moore, R. T. & Camp, L. B. Carbon balance, productivity, and water use of cold-winter desert shrub communities dominated by C3 and C4 species. Oecologia 29, 275–300 (1977).PubMed 
Article 

Google Scholar 
120.De Viñas, I. C. R. et al. Biomass of root and shoot systems of Quercus coccifera shrublands in eastern Spain. Ann. For. Sci. 57, 803–810 (2000).Article 

Google Scholar 
121.Caravaca, F., Figueroa, D., Alguacil, M. M. & Roldán, A. Application of composted urban residue enhanced the performance of afforested shrub species in a degraded semiarid land. Bioresour. Technol. 90, 65–70 (2003).CAS 
PubMed 
Article 

Google Scholar 
122.Caravaca, F., Figueroa, D., Azcón-Aguilar, C., Barea, J. M. & Roldán, A. Medium-term effects of mycorrhizal inoculation and composted municipal waste addition on the establishment of two Mediterranean shrub species under semiarid field conditions. Agric. Ecosyst. Environ. 97, 95–105 (2003).Article 

Google Scholar 
123.Carrasco, L., Azcón, R., Kohler, J., Roldán, A. & Caravaca, F. Comparative effects of native filamentous and arbuscular mycorrhizal fungi in the establishment of an autochthonous, leguminous shrub growing in a metal-contaminated soil. Sci. Total Environ. 409, 1205–1209 (2011).CAS 
PubMed 
Article 

Google Scholar 
124.Carrillo-Garcia, Á., Bashan, Y. & Bethlenfalvay, G. J. Resource-island soils and the survival of the giant cactus, cardon, of Baja California Sur. Plant Soil 218, 207–214 (2000).CAS 
Article 

Google Scholar 
125.Carrión-Prieto, P. et al. Mediterranean shrublands as carbon sinks for climate change mitigation: new root-to-shoot ratios. Carbon Manage. 8, 67–77 (2017).Article 
CAS 

Google Scholar 
126.Deng, L., Han, Q. S., Zhang, C., Tang, Z. S. & Shangguan, Z. P. Above-ground and below-ground ecosystem biomass accumulation and carbon sequestration with Caragana korshinskii Kom plantation development. Land Degrad. Dev. 28, 906–917 (2017).Article 

Google Scholar 
127.Perkins, S. R. & Owens, M. K. Growth and biomass allocation of shrub and grass seedlings in response to predicted changes in precipitation seasonality. Plant Ecol. 168, 107–120 (2003).Article 

Google Scholar 
128.Gargaglione, V., Peri, P. L. & Rubio, G. Allometric relations for biomass partitioning of Nothofagus antarctica trees of different crown classes over a site quality gradient. For. Ecol. Manage. 259, 1118–1126 (2010).Article 

Google Scholar 
129.Hao, H. M. et al. Effects of shrub patch size succession on plant diversity and soil water content in the water-wind erosion crisscross region on the Loess Plateau. Catena 144, 177–183 (2016).Article 

Google Scholar 
130.Herwitz, S. R. & Olsvig-Whittaker, L. Preferential upslope growth of Zygophyllum dumosum Boiss. (Zygophyllaceae) roots into bedrock fissures in the northern Negev desert. J. Biogeogr. 16, 457–460 (1989).Article 

Google Scholar 
131.Hoffmann, A. & Kummerow, J. Root studies in the Chilean matorral. Oecologia 32, 57–69 (1978).PubMed 
Article 

Google Scholar 
132.Holl, K. D. Effects of above- and below-ground competition of shrubs and grass on Calophyllum brasiliense (Camb.) seedling growth in abandoned tropical pasture. For. Ecol. Manage. 109, 187–195 (1998).Article 

Google Scholar 
133.Hollister, R. D. & Flaherty, K. J. Above- and below-ground plant biomass response to experimental warming in northern Alaska. Appl. Veg. Sci. 13, 378–387 (2010).
Google Scholar 
134.Kizito, F. et al. Seasonal soil water variation and root patterns between two semi-arid shrubs co-existing with pearl millet in Senegal, West Africa. J. Arid Environ. 67, 436–455 (2006).Article 

Google Scholar 
135.Kummerow, J., Krause, D. & Jow, W. Root systems of chaparral shrubs. Oecologia 29, 163–177 (1977).PubMed 
Article 

Google Scholar 
136.León, M. F., Squeo, F. A., Gutiérrez, J. R. & Holmgren, M. Rapid root extension during water pulses enhances establishment of shrub seedlings in the Atacama Desert. J. Veg. Sci. 22, 120–129 (2011).Article 

Google Scholar 
137.Li, C. P. & Xiao, C. W. Above- and belowground biomass of Artemisia ordosica communities in three contrasting habitats of the Mu Us Desert, northern China. J. Arid Environ. 70, 195–207 (2007).Article 

Google Scholar 
138.Liang, Y. M., Hazlett, D. L. & Lauenroth, W. K. Biomass dynamics and water use efficiencies of five plant communities in the shortgrass steppe. Oecologia 80, 148–153 (1989).CAS 
PubMed 
Article 

Google Scholar 
139.Zan, Q., Wang, Y., Liao, B. & Zheng, D. Biomass and net productivity of Sonneratia apetala, S. caseolaris mangrove man-made forest. Wuhan Bot. Res. 19, 391–396 (2001).
Google Scholar 
140.Liao, B., Zheng, D. & Zheng, S. Studies on the biomass of Sonneratia caseolaris stand. For. Res. 3, 47–54 (1990).
Google Scholar 
141.Lufafa, A. et al. Allometric relationships and peak-season community biomass stocks of native shrubs in Senegal’s Peanut Basin. J. Arid Environ. 73, 260–266 (2009).Article 

Google Scholar 
142.Lusk, C. H. Leaf area and growth of juvenile temperate evergreens in low light: species of contrasting shade tolerance change rank during ontogeny. Funct. Ecol. 18, 820–828 (2004).Article 

Google Scholar 
143.Marsh, A. S., Arnone, J. A., Bormann, B. T. & Gordon, J. C. The role of Equisetum in nutrient cycling in an Alaskan shrub wetland. J. Ecol. 88, 999–1011 (2000).Article 

Google Scholar 
144.Martínez, F. et al. Belowground structure and production in a Mediterranean sand dune shrub community. Plant Soil 201, 209–216 (1998).Article 

Google Scholar 
145.Marziliano, P. A. et al. Estimating belowground biomass and root/shoot ratio of Phillyrea latifolia L. in the Mediterranean forest landscapes. Ann. For. Sci. 72, 585–593 (2015).Article 

Google Scholar 
146.Mauchamp, A., Montaña, C., Lepart, J., Rambal, S. & Montana, C. Ecotone dependent recruitment of a desert shrub, Flourensia cernua, in vegetation stripes. Oikos 68, 107–116 (1993).Article 

Google Scholar 
147.Mendoza-Ponce, A. & Galicia, L. Aboveground and belowground biomass and carbon pools in highland temperate forest landscape in central Mexico. Forestry 83, 497–506 (2010).Article 

Google Scholar 
148.Miller, P. C. & Ng, E. Root:shoot biomass ratios in shrubs in southern California and central Chile. Madrono 24, 215–223 (1977).
Google Scholar 
149.Mooney, H. A. & Rundel, P. W. Nutrient relations of the evergreen shrub, Adenostoma fasciculatum, in the California chaparral. Bot. Gaz. 140, 109–113 (1979).CAS 
Article 

Google Scholar 
150.Moro, M. J., Pugnaire, F. I., Haase, P. & Puigdefábregas, J. Effect of the canopy of Retama sphaerocarpa on its understorey in a semiarid environment. Funct. Ecol. 11, 425–431 (1997).Article 

Google Scholar 
151.Negreiros, D., Fernandes, G. W., Silveira, F. A. O. & Chalub, C. Seedling growth and biomass allocation of endemic and threatened shrubs of rupestrian fields. Acta Oecol. 35, 301–310 (2009).Article 

Google Scholar 
152.Nie, X., Yang, Y., Yang, L. & Zhou, G. Above- and belowground biomass allocation in shrub biomes across the northeast Tibetan Plateau. PLoS ONE 11, e0154251 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
153.Nobel, P. S., Quero, E. & Linares, H. Root versus shoot biomass: responses to water, nitrogen, and phosphorus applications for Agave lechuguilla. Bot. Gaz. 150, 411–416 (1989).Article 

Google Scholar 
154.Pacaldo, R. S., Volk, T. A. & Briggs, R. D. Greenhouse gas potentials of shrub willow biomass crops based on below- and aboveground biomass inventory along a 19-year chronosequence. Bioenergy Res. 6, 252–262 (2013).CAS 
Article 

Google Scholar 
155.Padilla, F. M., Miranda, J. D., Jorquera, M. J. & Pugnaire, F. I. Variability in amount and frequency of water supply affects roots but not growth of arid shrubs. Plant Ecol. 204, 261–270 (2009).Article 

Google Scholar 
156.Portsmuth, A., Niinemets, Ü., Truus, L. & Pensa, M. Biomass allocation and growth rates in Pinus sylvestris are interactively modified by nitrogen and phosphorus availabilities and by tree size and age. Can. J. For. Res. 35, 2346–2359 (2005).CAS 
Article 

Google Scholar 
157.Roth, G. A., Whitford, W. G. & Steinberger, Y. Jackrabbit (Lepus californicus) herbivory changes dominance in desertified Chihuahuan Desert ecosystems. J. Arid Environ. 70, 418–426 (2007).Article 

Google Scholar 
158.Ruiz-Peinado, R., Moreno, G., Juarez, E., Montero, G. & Roig, S. The contribution of two common shrub species to aboveground and belowground carbon stock in Iberian dehesas. J. Arid Environ. 91, 22–30 (2013).Article 

Google Scholar 
159.Rundel, P. W. Biomass, productivity, and nutrient allocation in subalpine shrublands and meadows of the Emerald Lake Basin, Sequoia National Park, California. Arct. Antarct. Alp. Res. 47, 115–123 (2015).Article 

Google Scholar 
160.Millikin, C. S. & Bledsoe, C. S. Biomass and distribution of fine and coarse roots from blue oak (Quercus douglasii) trees in the northern Sierra Nevada foothills of California. Plant Soil 214, 27–38 (1999).CAS 
Article 

Google Scholar 
161.Saura-Mas, S. & Lloret, F. Adult root structure of Mediterranean shrubs: relationship with post-fire regenerative syndrome. Plant Biol. 16, 147–154 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
162.Schenk, H. J. & Mahall, B. E. Positive and negative plant interactions contribute to a north-south-patterned association between two desert shrub species. Oecologia 132, 402–410 (2002).PubMed 
Article 

Google Scholar 
163.Silva, J. S., Rego, F. C. & Martins-Loução, M. A. Belowground traits of Mediterranean woody plants in a Portuguese shrubland. Ecol. Mediterr. 28, 5–13 (2002).Article 

Google Scholar 
164.Simões, M. P., Madeira, M. & Gazarini, L. Biomass and nutrient dynamics in Mediterranean seasonal dimorphic shrubs: strategies to face environmental constraints. Plant Biosyst. 146, 500–510 (2012).
Google Scholar 
165.Tao, Y., Zhang, Y. M. & Downing, A. Similarity and difference in vegetation structure of three desert shrub communities under the same temperate climate but with different microhabitats. Bot. Stud. 54, 59 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
166.Toscano, S., Scuderi, D., Giuffrida, F. & Romano, D. Responses of Mediterranean ornamental shrubs to drought stress and recovery. Sci. Hortic. 178, 145–153 (2014).Article 

Google Scholar 
167.Trubat, R., Cortina, J. & Vilagrosa, A. Nutrient deprivation improves field performance of woody seedlings in a degraded semi-arid shrubland. Ecol. Eng. 37, 1164–1173 (2011).Article 

Google Scholar 
168.Van Wijk, M. T., Williams, M., Gough, L., Hobbie, S. E. & Shaver, G. R. Luxury consumption of soil nutrients: a possible competitive strategy in above-ground and below-ground biomass allocation and root morphology for slow-growing arctic vegetation? J. Ecol. 91, 664–676 (2003).Article 

Google Scholar 
169.Walker, L. R., Clarkson, B. D., Silvester, W. B. & Clarkson, B. R. Colonization dynamics and facilitative impacts of a nitrogen-fixing shrub in primary succession. J. Veg. Sci. 14, 277–290 (2003).Article 

Google Scholar 
170.Wang, B. & Yang, X. S. Comparison of biomass and species diversity of four typical zonal vegetations. J. Fujian Coll. For. 29, 345–350 (2009).
Google Scholar 
171.Wang, M. & Li, H. Quantitative study on the soil water dynamics of various forest plantations in the Loess Plateau region in northwestern Shanxi. Acta Ecol. Sin. 2, 178–184 (1995).
Google Scholar 
172.Wang, P. et al. Seasonal changes and vertical distribution of root standing biomass of graminoids and shrubs at a Siberian tundra site. Plant Soil 407, 55–65 (2016).CAS 
Article 

Google Scholar 
173.Whittaker, R. H. & Woodwell, G. M. Dimension and production relations of trees and shrubs in the Brookhaven Forest, New York. J. Ecol. 56, 1–25 (1968).Article 

Google Scholar 
174.Xu, H., Li, Y., Xu, G. & Zou, T. Ecophysiological response and morphological adjustment of two Central Asian desert shrubs towards variation in summer precipitation. Plant Cell Environ. 30, 399–409 (2007).CAS 
PubMed 
Article 

Google Scholar 
175.Yan, Z. Biomass and its allocation in a 28-year-old Castanopsis kawakamii plantation. J. Fujian Coll. For. 2, 114–118 (1996).
Google Scholar 
176.Gong, Y. et al. Carbon storage and vertical distribution in three shrubland communities in Gurbantünggüt Desert, Uygur Autonomous Region of Xinjiang, northwest China. Chin. Geogr. Sci. 22, 541–549 (2012).Article 

Google Scholar 
177.Yu, Y., Shi, D., Qiuyi, J., He, L. & Cheng, G. On the biomass of secondary Schima superba forest in Hangzhou. J. Zhejiang For. Coll. 2, 157–161 (1993).
Google Scholar 
178.Kato, T. et al. Carbon dioxide exchange between the atmosphere and an alpine meadow ecosystem on the Qinghai-Tibetan Plateau, China. Agric. Meteorol. 124, 121–134 (2004).Article 

Google Scholar 
179.Li, Z., Zhu, Q. & Li, J. A comparison of photosynthetic carbon sequestration of four shrubs in Ningxia. Pratacultural Sci. 29, 352–357 (2012).CAS 

Google Scholar 
180.Zhu, X., Shi, Q. & Li, Y. A preliminary study on the Qinghai’s treasure house of the forest biomass and shrubs. Sci. Technol. Qinghai Agric. For. 1, 15–20 (1993).
Google Scholar 
181.Liao, B. & Zheng, D. Study on the forest biomass and productivity of olive wood. For. Res. 4, 22–29 (1991).
Google Scholar 
182.Liu, B., Liu, Z., Lü, X., Maestre, F. T. & Wang, L. Sand burial compensates for the negative effects of erosion on the dune-building shrub Artemisia wudanica. Plant Soil 374, 263–273 (2014).CAS 
Article 

Google Scholar 
183.Alguacil, M. M., Hernández, J. A., Caravaca, F., Portillo, B. & Roldán, A. Antioxidant enzyme activities in shoots from three mycorrhizal shrub species afforested in a degraded semi-arid soil. Physiol. Plant. 118, 562–570 (2003).CAS 
Article 

Google Scholar 
184.Axe, M. S., Grange, I. D. & Conway, J. S. Carbon storage in hedge biomass—a case study of actively managed hedges in England. Agric. Ecosyst. Environ. 250, 81–88 (2017).Article 

Google Scholar 
185.van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).PubMed 
Article 
CAS 

Google Scholar 
186.Erin, L. et al. h2o: R Interface for the ‘H2O’ Scalable Machine Learning Platform. R package v.3.32.0.2 (2020); https://github.com/h2oai/h2o-3187.Sagi, O. & Rokach, L. Ensemble learning: a survey. WIREs Data Min. Knowl. Discov. 8, e1249 (2018).
Google Scholar 
188.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).189.Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 

Google Scholar 
190.Heiberger, R. M. HH: Statistical Analysis and Data Display: Heiberger and Holland (2020).191.Hothorn, T. & Zeileis, A. partykit: A modular toolkit for recursive partytioning in R. J. Mach. Learn. Res. 16, 3905–3909 (2015).
Google Scholar 
192.Borkovec, M. & Madin, N. ggparty: ‘ggplot’ visualizations for the ‘partykit’ package (2019).193.Dormann, C. F. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Glob. Ecol. Biogeogr. 16, 129–138 (2007).Article 

Google Scholar 
194.Hutchinson, M., Xu, T., Houlder, D., Nix, H. & McMahon, J. ANUCLIM 6.0 User’s Guide (Australian National Univ., 2009).195.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
196.Global Aridity and PET database (CGIAR-CSI, accessed 15 May 2018); http://www.cgiarcsi.community/data/global-aridity-and-pet-database197.CIESIN Gridded Population of the World, version 4 (GPWv4): Population Density Adjusted to Match 2015 Revision UN WPP Country Totals (NASA SEDAC, 2018); https://doi.org/10.7927/H4HX19NJ198.Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
199.SoilGrids (ISRIC, accessed 15 May 2018); https://www.soilgrids.org200.Entekhabi, D. et al. The soil moisture active passive (SMAP) mission. Proc. IEEE 98, 704–716 (2010).Article 

Google Scholar 
201.Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).CAS 
PubMed 
Article 

Google Scholar 
202.Batjes, N. H. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma 269, 61–68 (2016).CAS 
Article 

Google Scholar 
203.Schaaf, C. & Wang, Z. MCD43A1 MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global – 500m V006 (NASA LP DAAC, 2015); https://doi.org/10.5067/MODIS/MCD43A1C.006204.Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 (NASA LP DAAC, 2015).205.Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).CAS 
PubMed 
Article 

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NEWS AND VIEWS
23 June 2021

Migratory birds aid the redistribution of plants to new climates

Birds that travel long distances can disperse seeds far and wide. An assessment of the timing and direction of European bird migration reveals how these patterns might affect seed dispersal as the planet warms.

Barnabas H. Daru

 ORCID: http://orcid.org/0000-0002-2115-0257

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Barnabas H. Daru

Barnabas H. Daru is in the Department of Life Sciences, Texas A&M University-Corpus Christi, Corpus Christi, Texas 78412, USA.

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The rapid pace of global warming and its effects on habitats raise the question of whether species are able to keep up so that they remain in suitable living conditions. Some animals can move fast to adjust to a swiftly changing climate. Plants, being less mobile, rely on means such as seed dispersal by animals, wind or water to move to new areas, but this redistribution typically occurs within one kilometre of the original plant1. Writing in Nature, González-Varo et al.2 shed light on the potential capacity of migratory birds to aid seed dispersal.When the climate in a plant’s usual range becomes hotter than it can tolerate, it must colonize new, cooler areas that might lie many kilometres away. It is not fully clear how plants distribute their seeds across great distances, let alone how they cross geographical barriers. One explanation for long-distance seed dispersal is through transport by migratory birds. Such birds ingest viable seeds when eating fruit (Fig. 1) and can move them tens or hundreds of kilometres outside the range of a plant species3. In this mode of dispersal, the seeds pass through the bird’s digestive tract unharmed4,5 and are deposited in faeces, which provides fertilizer that aids plant growth. In the case of European migratory birds, for example, the direction of seed dispersal will depend on whether the timing of fruit production coincides with a bird’s southward trip to warmer regions around the Equator, or northward to cooler regions. Many aspects of this process have been a mystery until now.

Figure 1 | A young blackcap bird (Sylvia atricapilla) eating elderberries.Credit: Getty

González-Varo and colleagues report how plants might be able to keep pace with rapid climate change through the help of migrating birds. The authors analysed the fruiting times of plants, patterns of bird migration and the interactions between fruit-eating birds and fleshy-fruited plants across Europe. Plants with fleshy fruits were chosen for this study because most of their seed transport is by migratory birds6, and because fleshy-fruited plants are an important component of the woody-plant community in Europe. The common approach until now has been to predict plant dispersal and colonization using models fitted to abiotic factors, such as the current climate. González-Varo et al. instead analysed an impressive data set of 949 different seed-dispersal interactions between bird and plant communities, together with data on entire fruiting times and migratory patterns of birds across Europe. The researchers also analysed DNA traces from bird faeces to identify the plants and birds responsible for seed dispersal.
Read the paper: Limited potential for bird migration to disperse plants to cooler latitudes
The authors hypothesized that the direction of seed migration depends on how the plants interact with migratory birds, the frequency of these interactions or the number of bird species that might transport seeds from each plant species. González-Varo and colleagues found that 86% of plant species studied might have seeds dispersed by birds during their southward trip towards drier and hotter equatorial regions in autumn, whereas only about one-third of the plant species might be dispersed by birds migrating north in spring. This dispersal trend was more pronounced in temperate plants than in the Mediterranean plant communities examined. These results are in general agreement with well-known patterns of fruiting times and bird migrations. For example, the fruit of most fleshy-fruited plants in Europe ripens at a time that coincides with when birds migrate south towards the Equator7.Perhaps the most striking feature of these inferred seed movements is the observation that 35% of plant species across European communities, which are closely related on the evolutionary tree (phylogenetically related), might benefit from long-distance dispersal by the northward journey of migratory birds. This particular subset of plants tends to fruit over a long period of time, or has fruits that persist over the winter. This means that the ability of plants to keep up with climate change could be shaped by their evolutionary history — implying that future plant communities in the Northern Hemisphere will probably come from plant species that are phylogenetically closely related and that have migrated from the south. Or, to put it another way, the overwhelming majority of plant species that are dispersed south towards drier and hotter regions at the Equator will probably be less able to keep pace with rapid climate change in their new locations than will the few ‘winners’ that are instead dispersed north to cooler climates. This has implications for understanding how plants will respond to climate change, and for assessing ecosystem functions and community assembly at higher levels of the food chain. However, for seeds of a given plant species, more evidence is needed to assess whether passing through the guts of birds affects germination success.To determine which birds might be responsible for the plant redistributions to cooler climates in the north, the authors categorized European bird migrants into Palaearctic (those that fly to southern Europe and northern Africa during their non-breeding season) and Afro-Palaearctic (those that winter in sub-Saharan Africa). Only a few common Palaearctic migrants, such as the blackcap (Sylvia atricapilla; Fig. 1) or blackbird (Turdus merula), provide most of this crucial dispersal service northwards to cooler regions across Europe. Because migratory birds are able to relocate a small, non-random subset of plants, this could well have a strong influence on the types of plant community that will form under climate-change conditions.
A bird’s migration decoded
A major problem, however, is that the role of these birds in dispersing seeds over long distances is already at risk from human pressures and environmental changes8. Understanding these large-scale seed-dispersal interactions offers a way for targeted conservation actions to protect the areas that are most vulnerable to climate change. This could include boosting protection efforts in and around the wintering grounds of migratory birds — locations that are already experiencing a rise in human pressures, such as illegal bird hunting.González-Varo and colleagues’ focus on seed dispersal across a Northern Hemisphere region means that, as with most ecological analyses, the results are dependent on scale, which can cause issues when interpreting data9. Because the Northern Hemisphere has more land area and steeper seasonal temperature gradients than the Southern Hemisphere does, seed-dispersal interactions might have different patterns from those occurring in the Southern Hemisphere or in aquatic systems.For example, seed-eating birds from the genus Quelea migrate from the Southern Hemisphere to spend the dry season in equatorial West Africa, then move southwards again when the rains arrive. Their arrival in southern Africa usually coincides with the end of the wet season in this region, when annual grass seeds are in abundance. It will be worth investigating whether migratory birds in the Southern Hemisphere also influence the redistribution of plant communities during global warming. Likewise, exploring the long-distance dispersal of seeds of aquatic plants, such as seagrasses10 by water birds, is another area for future research that might benefit from González-Varo and colleagues’ methods.This study provides a great example of how migratory birds might assist plant redistribution to new locations that would normally be difficult for them to reach on their own, and which might offer a suitable climate. As the planet warms, understanding how such biological mechanisms reorganize plant communities complements the information available from climate-projection models, which offer predictions of future species distributions.

doi: https://doi.org/10.1038/d41586-021-01547-1

References1.Jordano, P., García, C., Godoy, J. A. & García-Castaño, J. L. Proc. Natl Acad. Sci. USA 104, 3278–3282 (2007).PubMed 
Article 

Google Scholar 
2.González-Varo, J. P. et al. Nature https://doi.org/10.1038/s41586-021-03665-2 (2021).Article 

Google Scholar 
3.Viana, D. S., Santamaría, L. & Figuerola, J. Trends Ecol. Evol. 31, 763–775 (2016).PubMed 
Article 

Google Scholar 
4.Lehouck, V., Spanhove, T. & Lens, L. Plant Ecol. Evol. 144, 96–100 (2011).Article 

Google Scholar 
5.Shikang, S., Fuqin, W. & Yuehua, W. Sci. Rep. 5, 11615 (2015).PubMed 
Article 

Google Scholar 
6.Jordano, P. in Seeds: The Ecology of Regeneration in Plant Communities 3rd edn (ed. Gallagher, R. S.) 18–61 (CAB Int., 2014).
Google Scholar 
7.Dingle, H. Migration: The Biology of Life on the Move (Oxford Univ. Press, 2014).
Google Scholar 
8.Bairlein, F. Science 354, 547–548 (2016).PubMed 
Article 

Google Scholar 
9.Daru, B. H., Farooq, H., Antonelli, A. & Faurby, S. Nature Commun. 11, 2115 (2020).PubMed 
Article 

Google Scholar 
10.Rock, B. M. & Daru B. H. Front. Mar. Sci. 8, 608867 (2021).Article 

Google Scholar 
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Competing Interests
The author declares no competing interests.

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