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    The evolution of biogeochemical recycling by persistence-based selection

    Model descriptionThe model involves a discrete time, discrete valued stochastic Markov process. Model variables and parameters are given in Tables 1 and 2 respectively. Both time and the number of individuals of each type are constrained to be integer valued. Death and reproductive mutation are stochastic processes derived from sampling from binomial distributions given by the relevant probabilities. All ensemble results give the 100-replicate average for the parameter choices in question.Growth of individuals from species ({S}_{1}) and ({S}_{2}) is proportional to the bio-available level of environmental substances ({R}_{1}) and ({R}_{2}) respectively. At time (t) (where time is in units of biological generations) the change in the number ({N}_{q,j}) of individuals of genotype (j) (non-producer, producer, plastic) within species (q) (({S}_{1}) or ({S}_{2})) can be written as a function of the state of the variables at the previous time-step:$${N}_{q,j}left(t+1right)=left(left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)cdot {G}_{q,j}left(tright)-{{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)-{delta }_{q,j}left(tright)+{{{{{{rm{{Upsilon }}}}}}}}_{q,xne j}left(tright)right)cdot left(1-frac{{S}_{q}left(tright)}{K}right)$$
    (1)
    The leftmost bracket on the right-hand side represents the number of individuals escaping starvation (death due to insufficient environmental substance) at the previous time-step and ({G}_{q,j}left(tright)) is the per capita reproductive growth rate. ({{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)) gives the number of mutant offspring individuals produced during reproduction from parent individuals of genotype (j). ({{{{{{rm{{Upsilon }}}}}}}}_{q,xne j}left(tright)) represents the number of (j) genotype individuals derived from mutation in parent individuals of other genotypes. ({delta }_{q,j}left(tright)) is the number of individuals of genotype (j) lost to random death, and the rightmost bracket relates the total number ({S}_{q}left(tright)) of individuals of species (q) to carrying capacity (K), which represents limitation of growth by any factor other than the relevant environmental substance, e.g. space. (The steady state population size in all simulations shown is below (K) and limited by the environmental substance influx. The carrying capacity is included in the model for computational reasons and as a “crash preventer” but has no qualitative effect on the results).The total number of individuals ({S}_{q}left(tright)) in species (q) is the sum of the number of individuals of each genotype (producer, non-producer and plastic, as discussed in the main text):$${S}_{q}left(tright)=mathop{sum }limits_{j=1}^{{j}_{{total}}}{N}_{q,j}left(tright)={N}_{q,{prod}}left(tright)+{N}_{q,{non}-{prod}}left(tright)+{N}_{q,{plast}}left(tright)$$
    (2)
    The genotype-specific reproductive growth rate ({G}_{q,j}left(tright)) (again for genotype (j) within species (q), time (t)), gives the number of offspring individuals produced per parent individual, per time-step. Growth rate is an increasing function of the bio-available level of environmental substance ({R}_{q,{BIOAVAIL}{ABLE}}) (the subscript (q) being identical because species ({S}_{1}) and ({S}_{2}) assimilate substances ({R}_{1}) and ({R}_{2}) respectively). Growth rate also includes a substance-to-biomass conversion efficiency parameter ({f}_{{conv}}) and a genotype-specific per capita term ({G}_{q,j,{PR}}) (number of offspring per parent, per unit environmental substance assimilated, per unit time). In the absence of growth-limitation by environmental substance levels, growth rate is capped at a genotype-specific maximum ({G}_{q,{jMAX}}):$${G}_{q,j}left(tright)={MIN}[{G}_{q,j,{PR}}cdot {R}_{q,{BIOAVAILABLE}}(t)cdot {f}_{{conv}},{G}_{q,{jMAX}}]$$
    (3)
    $${G}_{q,{non}-{prod},{PR}}={G}_{0}$$
    (4)
    $${G}_{q,{non}-{prod},{MAX}}={G}_{0}cdot {R}_{{assimMAX}}$$
    (5)
    ({G}_{0}) is the baseline number of offspring, per parent, per unit substance assimilated. ({R}_{{assimMAX}}) is a universal maximum potential number of units of environmental substance that can be assimilated by a single individual per time-step (i.e. representing basic physiological constraints on growth). The producer genotype incurs a per capita reproductive growth rate cost ({kappa }_{{prod}}) relative to the non-producer:$${G}_{q,{prod},{PR}}={G}_{0}cdot (1-{kappa }_{{prod}})$$
    (6)
    $${G}_{q,{prod},{MAX}}={G}_{0}cdot (1-{kappa }_{{prod}})cdot {R}_{{assimMAX}}$$
    (7)
    This growth rate formulation is therefore a highly simplified linearization of the Michaelis-Menten kinetics normally used in models of resource and nutrient assimilation.The plastic genotype switches phenotype depending upon the level of environmental substance relative to a fixed threshold ({{R}_{q,{BIOAVAILABLE}}}_{{crit}}), in effect becoming a second non-producer genotype below this threshold and a second producer genotype above it:$${IF}[{R}_{q,{BIOAVAILABLE}}(t)ge {{R}_{q,{BIOAVAILABLE}}}_{{crit}}],{G}_{q,{plast}}left(tright)={G}_{q,{prod}}left(tright)$$$${ELSEIF}[{R}_{q,{BIOAVAILA}{BLE}}left(tright) , < , {{R}_{q,{BIOAVAILABLE}}}_{{crit}}],{G}_{q,{plast}}left(tright)={G}_{q,{non}-{prod}}left(tright)$$ (8) There is no spatial structure whatsoever, thus access to environmental substance is uniform across individuals. The bioavailable quantity of each environmental substance is simply the total amount ({R}_{q,{NET}}(t)) divided by the total number of individuals assimilating it:$${R}_{q,{BIOAVAILABLE}}left(tright)=frac{{R}_{q,{NET}}(t)}{{S}_{q}left(tright)}$$ (9) We allow the per capita reproductive growth rate to fall below ({G}_{q,j}left(tright)=1), which, if interpreted deterministically at the individual level would correspond to an individual failing to sustain its biomass to the next time-step and thus dying. However, a population-level average ({G}_{q,j}left(tright) , < , 1) is interpretable in terms of a thinning factor that maps between discretized individuals and continuously distributed environmental substance. Thus, a thinning factor of (left(1-{G}_{q,j}left(tright)right)) is used to calculate the total number of individuals dying of starvation ({rho }_{q,j}) (again genotype (j), species (q)). This represents pre-reproduction deaths, corresponding to the difference between the actual population size and the population size that the environmental substance pool is capable of supporting. ({rho }_{q,j}left(tright)) is constrained to be an integer and is zero for ({G}_{q,j}left(tright) , > , 1):$${rho }_{q,j}left(tright)={N}_{q,j}left(tright)cdot {MAX}left[0,left(1-{G}_{q,j}left(tright)right)right]$$
    (10)
    A subset of offspring are a different genotype from their parent via mutation. For parent genotype (j), the number ({{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)) of mutant offspring with genotype (ne j) is calculated using baseline mutation probability per reproductive event ({mu }_{0}), with the total number of new individuals produced by the parent individuals surviving starvation ({G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)). The total number of mutant offspring ({{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)) is thus a binomially distributed random variable with success probability ({mu }_{0}) and number of trials ({G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)). The expected value (Eleft[{{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)right]) is the product of these two numbers:$$ {{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright) sim Bleft({G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right),{mu }_{0}right), \ Eleft[{{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)right]={G}_{q,j}left(tright)cdot left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)cdot {mu }_{0}$$
    (11)
    Mutation to genotype (j) from the other genotypes is calculated in exactly the same way using the number and reproductive growth rates of the relevant (other) genotypes. Any particular mutant offspring is randomly allocated to one of the other genotypes with equal probability ({p}_{kto j}=frac{1}{{j}_{{total}}-1}=0.5) (where ({j}_{{total}}=3) is the total number of genotypes per species). The expected number of offspring with genotype (j) produced by mutation within parent offspring of other genotypes (kne j) is therefore:$$E[{{{{{{rm{{Upsilon }}}}}}}}_{q,x , ne , j}left(tright)]={left(mathop{sum }limits_{k=1}^{{k}_{{to}{tal}}}{{{{{{rm{{Upsilon }}}}}}}}_{q,k}left(tright)right)}_{kne j}cdot frac{1}{{j}_{{total}}-1}$$
    (12)
    Independently of reproduction and assimilation of environmental substance, any given individual has a probability ({delta }_{0}) at each time point of death due to stochastic factors. The genotype/species specific number of such deaths is again a random sample from a binomial distribution, with success probability ({delta }_{0}):$${delta }_{q,j}left(tright) sim Bleft({N}_{q,j}left(tright),{delta }_{0}right),Eleft[{delta }_{q,j}left(tright)right]={N}_{q,j}left(tright)cdot {delta }_{0}$$
    (13)
    The net quantity of growth-limiting environmental substance at each time-step is given by the difference between total biotic assimilation ({A}_{{R}_{q}}) and the production ({P}_{{R}_{q}}) and abiotic input ({varphi }_{{R}_{q}}) fluxes:$${R}_{q,{NET}}(t+1)={varphi }_{{R}_{q}}(t)+{P}_{{R}_{q}}(t)-{A}_{{R}_{q}}(t)$$
    (14)
    The abiotic net influx is the sum of two fluxes. First, an input term that is the product of a baseline scaling factor ({{varphi }_{0}}_{{R}_{q}}) and a model forcing (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}) representing the mapping between abiotic-geological and biotic-evolutionary timescales. In practice (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}(t)) was set to either (1) or (0) or (in fluctuation runs) a time-dependent switching between the two. (More sophisticated implementations of (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}(t)), e.g. sinusoidal oscillations and stochastic time dependence, were attempted but made little qualitative difference to the results). Second, an abiotic removal term that scales linearly with the quantity of environmental substance:$${varphi }_{{R}_{q}}(t)={{varphi }_{0}}_{{R}_{q}}cdot frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}left(tright)-frac{{R}_{q,{NET}(t)}}{{R}_{q,{NET}0}}$$
    (15)
    where ({R}_{q,{NET}0}) is a normalization factor representing the sensitivity of the abiotic efflux to the influx. In the absence of any biota and for (frac{partial {t}_{{geo}}}{partial {t}_{{bio}}}=1) the steady state environmental substance level is immediately given by (15) as ({R}_{q,{NET}(t)}={R}_{q,{NET}0}cdot {{varphi }_{0}}_{{R}_{q}}), thus the numerical value of ({R}_{q,{NET}0}) corresponds to the abiotic steady state residence time.Total biotic assimilation ({A}_{R}) of each environmental substance is given by:$${A}_{{R}_{q}}left(tright)=mathop{sum }limits_{k=1}^{{j}_{{total}}}frac{{G}_{q,k}left(tright)cdot left({N}_{q,k}left(tright)-{rho }_{q,k}left(tright)right)}{{G}_{q,{kPR}}}$$
    (16)
    The numerator gives the total number of individuals produced as a result of biological assimilation of environmental substance ({R}_{q}) and the denominator is the genotype specific number of individuals produced per unit substance assimilated, dividing through by which therefore converts to total units of substance assimilated by the population as a whole.Net biotic ({P}_{{R}_{q}{NET}}) production of substance ({R}_{q}) by the producer genotype in the other species (p) is calculated equivalently, via the product of the per capita production rate ({P}_{q,{prod}}) and the total number of reproducing individuals:$${P}_{q,{prod}}left(tright)=frac{{MIN}[{G}_{q,{prod}}left(tright)cdot {f}_{{convprod}},{G}_{q,{prodMAX}}]}{{G}_{p,{PRODUCER;PR}}}$$
    (17)
    $${P}_{{R}_{q}{NET}}left(tright)={P}_{q,{prod}}left(tright)cdot left({N}_{p,{PRODUCER}}left(tright)-{rho }_{p,P{RODUCER}}left(tright)right)$$
    (18)
    Where ({f}_{{conv},{PROD}}) is the per capita efficiency by which producers convert the environmental substance that they assimilate into the by-product they produce (note that the equivalent conversion efficiency for assimilation ({f}_{{conv}}) already appears in the growth functions of each genotype, therefore does not appear in Eq. (17)).The residence time ({T}_{{R}_{q}})of each environmental substance is given by the net quantity of this substance divided by the influx, to give units of the average number of biological generations a unit of environmental substance spends in the relevant pool before being removed. In those simulations in which the abiotic influx ({varphi }_{{R}_{q}}(t)) was set to zero (i.e. during the shut-off intervals) production ({P}_{{R}_{q}{NE}T}left(tright)) was used as an alternative denominator:$${IF}left[{varphi }_{{R}_{q}}left(tright) , > , 0,{T}_{{R}_{q}}left(tright)=frac{{R}_{q,{NET}}left(tright)}{{varphi }_{{R}_{q}}left(tright)}right]!,{IF}left[left(left({varphi }_{{R}_{q}}left(tright)=0right){& }left({P}_{{R}_{q}{NET}}left(tright) , > ,0right)right)!,{T}_{{R}_{q}}left(tright)=frac{{R}_{q,{NET}}left(tright)}{{P}_{{R}_{q}{NET}}left(tright)}right]{ELSE}[{T}_{{R}_{q}}left(tright)=0]$$
    (19)
    The cycling ratio ({{CR}}_{{R}_{q}}) of each substance is given by the ratio between net biotic assimilation of that substance ({A}_{{R}_{q}}left(tright)) and the abiotic influx of that substance ({varphi }_{{R}_{q}}left(tright)). As with the residence time, when the abiotic influx was zero, the input from biological production was used as an alternative denominator:$${IF}left[{varphi }_{{R}_{q}}left(tright) , > , 0,{{CR}}_{{R}_{q}}left(tright)=frac{{A}_{{R}_{q}}left(tright)}{{varphi }_{{R}_{q}}left(tright)}right]{IF}left[left(left({varphi }_{{R}_{q}}left(tright)=0right){{& }}left({P}_{{R}_{q}{NET}}left(tright) , > ,0right)right),{{CR}}_{{R}_{q}}left(tright)=frac{{A}_{{R}_{q}}left(tright)}{{P}_{{R}_{q}{NET}}left(tright)}right]{ELSE}[{{CR}}_{{R}_{q}}left(tright)=0]$$
    (20)
    Deterministic approximation to steady stateAssume that at steady state substance assimilation will reach a maximal state such that the level of environmental substance is limiting to population size. Assume that such a state is below the level ({{R}_{q,{BIOAVAILABLE}}}_{{crit}}) at which the plastic genotype effectively becomes a second non-producer genotype and can thus be subsumed into non-producer frequency, such that (2) becomes ({S}_{q}left(tright)=mathop{sum }nolimits_{j=1}^{{j}_{{total}}}{N}_{q,j}left(tright)={N}_{q,{prod}}left(tright)+{N}_{q,{non}-{prod}}left(tright)). Assume that there are non-zero starvations at each time-step for all genotypes, which implies growth rate ({G}_{q,j}left(tright) , < , 1,ll {G}_{q,{jMAX}},forall j,q), which gives by (3)({G}_{q,j}left(tright)={G}_{q,j,{PR}}cdot {R}_{q,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}}). Substituting this into (10), then the first bracketed term in (1), then labeling the post-starvation number of individuals as ({({N}_{q,j}left(tright))}_{{NET}}):$${({N}_{q,j}left(tright))}_{{NET}}=left({N}_{q,j}left(tright)-{rho }_{q,j}left(tright)right)={N}_{q,j}left(tright)cdot left(1-left(1-{G}_{q,j}left(tright)right)right)={N}_{q,j}left(tright)cdot {G}_{q,j}left(tright)$$ (21) Approximate (14) deterministically by a fixed fractional parameter corresponding to the baseline random death rate:$${delta }_{q,j}left(tright)approx {N}_{q,j}left(tright)cdot {delta }_{0}$$ (22) Doing the same for mutation:$${{{{{{rm{{Upsilon }}}}}}}}_{q,j}left(tright)={G}_{q,j}left(tright)cdot {left({N}_{q,j}left(tright)right)}_{{NET}}cdot {mu }_{0}={N}_{q,j}left(tright)cdot {{G}_{q,j}left(tright)}^{2}cdot {mu }_{0}$$ (23) The term in (12) for mutation to (j) from other genotypes simplifies to$${{{{{{rm{{Upsilon }}}}}}}}_{q,x ,ne , j}left(tright)={sum }_{k,=,1}^{{j}_{{total}}}frac{{G}_{q,k}left(tright)cdot {left({N}_{q,k}left(tright)right)}_{{NET}}cdot {mu }_{0}}{{j}_{{total}}-1}={N}_{q,k}left(tright)cdot {{G}_{q,k}left(tright)}^{2}cdot {mu }_{0}$$Substituting Eqs. (21–23) into (1):$${N}_{q,j}left(tright)cdot {{G}_{q,j}left(tright)}^{2}cdot (1-{mu }_{0})-{N}_{q,j}left(tright)cdot {delta }_{0}+{N}_{q,k}left(tright)cdot {{G}_{q,k}left(tright)}^{2}cdot {mu }_{0}=0$$ (24) Noting that by Eqs. (3)–(5) combined with the above assumptions, the growth rate of the producer can be written as:$${G}_{q,{prod}}left(tright)={G}_{q,{cheat}}left(tright)cdot (1-{kappa }_{{prod},q})$$ (25) Writing (24) explicitly for each genotype:$${N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot (1-{mu }_{0})-{N}_{q,{non}-{prod}}left(tright)cdot {delta }_{0}+{N}_{q,{prod}}left(tright)cdot {{G}_{q,{prod}}left(tright)}^{2}cdot {mu }_{0}=0$$$${N}_{q,{prod}}left(tright)cdot {{G}_{q,{prod}}left(tright)}^{2}cdot (1-{mu }_{0})-{N}_{q,{prod}}left(tright)cdot {delta }_{0}+{N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot {mu }_{0}=0$$Adding:$${N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot left(1-{mu }_{0}right)-{N}_{q,{non}-{prod}}left(tright)cdot {delta }_{0}$$$$+{N}_{q,{prod}}left(tright)cdot {left({G}_{q,{non}-{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)}^{2}cdot {mu }_{0}+{N}_{q,{prod}}left(tright)cdot {left({G}_{q,{non}-{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)}^{2}cdot left(1-{mu }_{0}right)$$$$-{N}_{q,{prod}}left(tright)cdot {delta }_{0}+{N}_{q,{non}-{prod}}left(tright)cdot {{G}_{q,{non}-{prod}}left(tright)}^{2}cdot {mu }_{0}=0$$Because the mutation terms cancel:$$left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot left({{G}_{q,{non}-{prod}}left(tright)}^{2}-{delta }_{0}right)=0$$ (26) Substituting in for the growth rate terms (3–7) gives:$$left({N}_{q,{non}-{prod}}(t)+{N}_{q,{prod}}(t)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot left({left({G}_{0}cdot {R}_{q,{BIOAVAILABLE}}(t)cdot {f}_{{conv}}right)}^{2}-{delta }_{0}right)=0$$ (27) By (16), (4), (6) and the above, total steady state assimilation of the growth limiting environmental substance by species (q) is:$${A}_{{R}_{q}}left(tright)=mathop{sum }limits_{k=1}^{{j}_{{total}}}frac{{G}_{q,k}left(tright)cdot left({N}_{q,k}left(tright)-{rho }_{q,k}left(tright)right)}{{G}_{q,{kPR}}}$$$$kern2.4pc=frac{{N}_{q,{non}-{prod}}left(tright)cdot {left({G}_{q,{non}-{prod}}left(tright)right)}^{2}}{{G}_{0}}+frac{left({N}_{q,{prod}}left(tright)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot {left({G}_{q,{non}-{prod}}left(tright)right)}^{2}}{{G}_{0}cdot left(1-{kappa }_{{prod}}right)}$$$$=left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {{G}_{0}cdot ({R}_{q,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}$$ (28) By (16–18), production of this substance by the producer allele in the other species (p , ne , q), assuming the various arguments above simultaneously apply to this species, is:$${P}_{{R}_{q}}left(tright)= frac{{G}_{p,{prod}}left(tright)cdot left({N}_{p,{prod}}left(tright)-{rho }_{p,{PRODUCER}}left(tright)right)}{{G}_{p,{prodPR}}}cdot {f}_{{conv},{PROD}}\ = {N}_{p,{prod}}left(tright)cdot left(1-{kappa }_{{prod},p}right) cdot {G}_{0}cdot {({R}_{p,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}cdot {f}_{{conv},{PROD}}$$ (29) Balance between input and output fluxes of each environmental substance requires ({varphi }_{{R}_{q}}left(tright)+{P}_{{R}_{q}}left(tright)={A}_{{R}_{q}}left(tright)), meaning that by substituting in ({A}_{{R}_{q}}left(tright)) from (28) it is possible to solve for bioavailable substance level, then substitute in the production flux of this substance derived from the producer allele in the other species (p,ne, q):$${R}_{q,{BIO}{AVAILABLE}}left(tright) =frac{1}{{f}!_{{conv}}}sqrt{frac{{varphi }_{{R}_{q}}left(tright)+{P}_{{R}_{q}}left(tright)}{left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {G}_{0}}}\ =frac{1}{{f}!_{{conv}}}sqrt{frac{{varphi }_{{R}_{q}}left(tright)+{N}_{p,{prod}}left(tright)cdot left(1-{kappa }_{{prod},p}right)cdot {G}_{0}cdot {({R}_{p,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}cdot {f}_{{conv},{PROD}}}{left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {G}_{0}}}$$ (30) Substituting this into (27) gives a symmetrical condition for steady state genotype frequencies and substance levels across the system:$$ left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot {left(1-{kappa }_{{prod},q}right)}^{2}right)cdot\ left(frac{{varphi }_{{R}_{q}}left(tright)+{N}_{p,{prod}}left(tright)cdot left(1-{kappa }_{{prod},p}right)cdot {G}_{0}cdot {({R}_{p,{BIOAVAILABLE}}left(tright)cdot {f}_{{conv}})}^{2}cdot {f}_{{conv},{PROD}}}{left({N}_{q,{non}-{prod}}left(tright)+{N}_{q,{prod}}left(tright)cdot left(1-{kappa }_{{prod},q}right)right)cdot {G}_{0}}-{delta }_{0}right)=0$$ (31) This solution illustrates the intuitive ideas that growth and reproduction balance random death at steady state and that the associated producer frequency is lower than that of the non-producer by a factor of the cost. (This factor is of second order because the growth rate is used both directly and (by (10)) in the calculation of starvations). Because our model is a discrete stochastic process, (31) can be viewed as an approximation to a steady state condition, subject to the above assumptions combined with the continuous generation of producers by mutation at a sufficient rate to preclude their extinction. The key point is that over long timescales in the finite populations with which we deal, organism-level selection unavoidably favors the non-producer, with no possibility for multi-level fecundity selection. The producer’s stable presence is thus attributable to the combination of mutation and cycle-level selection. More

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    Rapid remote monitoring reveals spatial and temporal hotspots of carbon loss in Africa’s rainforests

    Continental, regional, and local spatiotemporal patterns of carbon lossFor Africa’s primary tropical humid forest, carbon losses due to forest disturbances reached 42.2 ± 5.1 MtC yr−1 (mean ± standard deviation, where MtC yr−1 is one million metric tons of carbon loss per year) in 2019 and 53.4 ± 6.5 MtC yr−1 in 2020. Just 9 countries out of the 23 analyzed accounted for 95.0% of total gross losses in 2019 and 94.3% in 2020. These countries contain about 95.7% of all primary tropical humid forests of Africa, with the DRC accounting for 52.8%, Gabon 11.8%, the Republic of the Congo 11.0%, and Cameroon 9.8%. Of these, DRC and Cameroon were responsible for 49.3% and 19.1% of losses in 2019 and 44.7% and 20.6% in 2020. DRC and Cameroon had an annual increase of 15.0% and 36.5% respectively, between 2019 and 2020. From countries with at least 1 MtC emitted in the two years analyzed, Madagascar had the highest annual increase in carbon loss (+153.9%), while Equatorial Guinea is the only country with a decrease in carbon loss (−20.1%). Extending the carbon loss analysis for both past and future will help to better understand these variations and whether the COVID-19 global pandemic had any influence on the general increase between 2019 and 202019. While the absolute numbers for carbon loss estimates should be treated carefully and a sample-based approach should be preferred for an unbiased estimate of absolute numbers20, we focused our analysis on the trends of carbon loss at the continental, country, and local scale (Fig. 1 and Supplementary Fig. 1).Fig. 1: Carbon loss across Africa’s rainforests.We analyzed 23 countries containing primary moist forest. The aboveground carbon stock (green palette) underlies the carbon loss estimations (red palette). Several hotspots can be seen across these regions. The uncertainties of the carbon loss estimations are expressed as standard deviations and shown in Supplementary Fig. 1.Full size imageThe high temporal detail of the analysis revealed various monthly patterns of carbon losses for countries, highly related to local rainfall patterns18 (Fig. 2). Countries like Cameroon, Liberia, Nigeria, Central African Republic (CAR), and Madagascar showed a clear dry-wet seasonal variation in carbon loss per year, while the Republic of the Congo and the DRC, due to their latitudinal extent, exhibited two dry-wet season variations per year with varying intensities (Fig. 2). The seasonal variation can be explained by higher accessibility to forests during the dry months when activities related to smallholder agriculture and logging are more feasible than in the wet season when many roads become inaccessible.Fig. 2: Temporal patterns of carbon loss for the top 10 countries.We show monthly statistics for 2019 and 2020 and the associated uncertainty (black lines). We separate between high (red bars) and low (yellow bars) confidence alerts, the latter showing up for the last 3 months of 2020.Full size imageOne of the highest differences between the months with the most and the least carbon losses was found for Madagascar (72 times more carbon loss in March compared to November 2019). In CAR, the three consecutive months with the highest cumulative carbon loss (January to March 2020) contributed to 75.7% of the total annual loss (between February and April 2020), in Nigeria 73.9% (January to March 2020), Liberia 73.1% (February to April 2020), Madagascar 70.7% (September to November 2020), and Cameroon 62.2% (January to March 2020). Lower percentages were found for countries with mixed seasonality and patterns, like DRC 36.7% (January to March 2020), and the Republic of the Congo 32.8% (January to March 2020) (Fig. 2). For the latter two countries, we expect better-defined peaks of carbon loss at local scales, where climatic conditions are not mixed. The annual cumulative carbon loss (%) per country (Fig. 3) showed that Liberia, Nigeria, CAR, and Cameroon reached between 70-90% of their annual carbon loss in April, while Madagascar reached 60% in October. The DRC, Gabon, Republic of the Congo, Equatorial Guinea, and Ghana have a more gradual monthly increase of cumulative carbon loss with less contrasting seasonality effects. Monthly patterns of carbon losses between the two years analyzed resulted in a correlation coefficient of 0.94 for the CAR, 0.92 for the DRC, 0.91 for Madagascar, 0.90 for Gabon, and 0.83 for Cameroon (Supplementary Fig. 2). For the Republic of the Congo, the two years correlated 0.51. Knowing the peak months of carbon loss for each country and that these patterns are repeatable from one year to another can contribute to better target and prioritize enforcement activities, as well as predicting future patterns and early reporting of annual forest carbon losses.Fig. 3: Annual cumulative carbon loss (%) for both years analyzed, 2019 and 2020.Africa’s total cumulative carbon loss is shown with a black line. The 10 topmost emitting countries out of 23 countries analyzed are shown and represented by distinct colored lines.Full size imageSeveral hotspots of carbon losses can be seen in Fig. 1. The high spatial and temporal details of our analysis are shown in Fig. 4, where several local examples with different drivers of forest disturbances are shown, like logging roads, selective logging, mining, oil palm plantations, urban expansion, and small-holder agriculture. This kind of information, coupled with auxiliary datasets (e.g., legal concessions, protected areas) can identify the legality of forest disturbance21.Fig. 4: Local examples of approx. 10 × 10 km in extent showing different spatiotemporal patterns and drivers of carbon loss.The first column shows the carbon loss, the second column the associated uncertainty, the third column the day-of-the-year when the loss occurred, and the last column shows the monthly distribution of carbon loss and associated uncertainty for each local example. The center coordinates of each location are shown in the third column as latitude and longitude. Exact locations are shown in Supplementary Fig. 3. a Logging roads and selective logging in the Central African Republic, b mining of gold and titanium in the Republic of the Congo, c development of an oil palm plantation in Cameroon, d forest disturbance related to building a new capital city in Equatorial Guinea, and e small-scale agriculture expansion at the edge of the forest in the DRC.Full size imageImplications of rapid monitoring of local carbon lossNear-real time alerts combined with biomass maps result in spatially explicit forest carbon loss, unlike global tabular statistics of national data22,23. We provide new insights into the spatiotemporal dynamics of carbon loss with consistent assessment of accuracy that could enable transparency and completeness for countries reporting on their REDD + progress to the UNFCCC24. We provide monthly carbon loss estimates that could play a key role in local, national, and international forest initiatives for global carbon policy goals25. Such a system can be implemented with minimal costs and is based on open-source datasets and Google Earth Engine cloud computing platform26, thus enabling cost-effective national monitoring of forest carbon loss7. Providing rapid reporting on the location, time, and amount of carbon lost across Africa’s primary humid forest will help undertake immediate action to protect and conserve carbon-rich threatened forests. Furthermore, countries will be able to predict and estimate their annual carbon loss before a reporting period ends, thus having the opportunity to adjust their practices to meet their country-specific commitments for climate change mitigation initiatives.Limitations and future improvementsWe used the RADD alerts (Radar for Detecting Deforestation)18 with a minimum mapping unit (MMU) of 0.2 ha as accuracy estimates were available for this MMU. Events smaller than 0.2 ha would add to the total carbon loss but are by nature associated with higher uncertainties18. The implications of the RADD alerts using a global humid tropical forest product as a forest baseline for 201816,27,28 are twofold. First, the global nature of this product might result in inconsistencies at the local level18. Second, because the forest cover loss information used to generate the forest baseline is based on optical Landsat data, persistent cloud cover in the second half of 2018 in some areas led to missed reporting of forest disturbances, thus being detected at the beginning of 2019 by the RADD alerts. This possible overestimation of carbon loss at the start of 2019 is not an issue for a near-real-time alerting system since later months are not affected. Furthermore, the alerts do not distinguish between human-induced disturbances and natural forest disturbances18. When a new forest disturbance alert is detected, it will be confirmed or rejected within 90 days by subsequent Sentinel-1 images18. That is why our carbon loss reporting separates between high and low confidence alerts for the last three months of 2020, which is common for most forest disturbance alerting products18,29. We separated all the alerts into core and boundary pixels. Core alerts represent complete tree cover removal and we assumed complete carbon loss within a pixel. For boundary alerts, we assumed a 50% carbon loss since these mainly represent forest disturbances with partial tree cover removal. Detecting and quantifying the level of degradation remains challenging and future developments will minimize this uncertainty by providing variable percentages of degraded forest30. The timeliness and spatial details of future forest disturbance alerting products will improve with the availability of open access long-wavelength radar data from near-future satellite missions (e.g., NISAR L-band SAR in 202331), by using a combination of optical and radar forest disturbance alert products, and integration with high-resolution satellite products.We relied on an aboveground biomass baseline map from 201832, prior to RADD alerts starting from 2019. Biomass estimation for the tropical moist forests is based on ALOS-2 PALSAR-2 L-band satellite and its usage needs to account for the local biases, especially underestimating AGB values higher than 250 Mg ha−1 (ref. 32). Although we reduced this underestimation by adjusting the AGB map based on ground field data, more research is needed on providing up-to-date high-resolution aboveground carbon estimates33 that could further increase the accuracy of local carbon loss estimation. Radar-based estimation of forest carbon stocks is challenging over mountainous terrain and is less accurate in complex canopies3 and future integration of radar and optical satellite data will provide more robust estimates33. Nevertheless, new spaceborne missions (e.g., GEDI34, BIOMASS35) will provide an unprecedented amount of forest structure samples that will improve the algorithms and thus the final accuracy of aboveground biomass estimates.We focused on exploring and analyzing local carbon losses and showing high temporal and spatial patterns of carbon losses. We showed the country statistics to emphasize the temporal dynamics of carbon losses and compare the temporal profiles across our study region. Our approach was not to provide stratified area estimations36 associated with forest disturbances but we used this concept in the sense that we had a stratified sample of higher quality reference data18 to estimate the omission and commission errors and consider those in our uncertainty estimation on the pixel level. The analysis showed that omission and commission errors are small and rather balanced, and thus do not result in a major area bias for the forest disturbances. The uncertainties of the aboveground biomass product32 were adjusted for known regional biases using regional forest biomass plot data sources. With this approach, the original aboveground biomass map bias was partly corrected using a model-based approach deemed to be an alternative to a sample-based approach whenever country data are unavailable37. Our uncertainty analysis and error reduction showed that we expect only minor bias in the forest disturbance and the biomass data and the remaining uncertainties are propagated in our pixel-based uncertainty layer. More

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    Experimental immune challenges reduce the quality of male antennae and female pheromone output

    Kraaijeveld, A. R. & Godfray, H. C. J. Trade-off between parasitoid resistance and larval competitive ability in Drosophila melanogaster. Nature 389(6648), 278–280 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    Lochmiller, R. L. & Deerenberg, C. Trade-offs in evolutionary immunology: Just what is the cost of immunity?. Oikos 88(1), 87–98 (2000).Article 

    Google Scholar 
    Zuk, M. & Stoehr, A. M. Immune defense and host life history. Am. Nat. 160(4), S9–S22 (2002).Article 

    Google Scholar 
    McKean, K. A. & Nunney, L. Increased sexual activity reduces male immune function in Drosophila melanogaster. Proc. Natl. Acad. Sci. 98(14), 7904–7909 (2001).ADS 
    CAS 
    Article 

    Google Scholar 
    Schwenke, R., Lazzaro, B. P. & Wolfner, M. F. Reproduction–immunity trade-offs in insects. Annu. Rev. Entomol. 61(1), 239–256. https://doi.org/10.1146/annurev-ento-010715-023924 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    McNamara, K. B., Wedell, N. & Simmons, L. W. Experimental evolution reveals trade-offs between mating and immunity. Biol. Lett. 9(4), 20130262. https://doi.org/10.1098/rsbl.2013.0262 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nystrand, M. & Dowling, D. K. Effects of immune challenge on expression of life-history and immune trait expression in sexually reproducing metazoans—a meta-analysis. BMC Biol. 18(1), 135. https://doi.org/10.1186/s12915-020-00856-7 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lawniczak, M. K. N. et al. Mating and immunity in invertebrates. Trends Ecol. Evol. 22(1), 48–55 (2007).Article 

    Google Scholar 
    Ahtiainen, J. J., Alatalo, R. V., Kortet, R. & Rantala, M. J. A trade-off between sexual signalling and immune function in a natural population of the drumming wolf spider Hygrolycosa rubrofasciata. J. Evol. Biol. 18(4), 985–991. https://doi.org/10.1111/j.1420-9101.2005.00907.x (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    Simmons, L. W., Zuk, M. & Rotenberry, J. T. Immune function reflected in calling song characteristics in a natural population of the cricket Teleogryllus commodus. Anim. Behav. 69, 1235–1241. https://doi.org/10.1016/j.anbehav.2004.09.011 (2005).Article 

    Google Scholar 
    Spencer, K. A., Buchanan, K. L., Leitner, S., Goldsmith, A. R. & Catchpole, C. K. Parasites affect song complexity and neural development in a songbird. Proc. R. Soc. B 272(1576), 2037–2043. https://doi.org/10.1098/rspb.2005.3188 (2005).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rantala, M. J., Koskimaki, J., Taskinen, J., Tynkkynen, K. & Suhonen, J. Immunocompetence, developmental stability and wingspot size in the damselfly Calopteryx splendens L. Proc R Soc B 267(1460), 2453–2457 (2000).CAS 
    Article 

    Google Scholar 
    Clotfelter, E. D., Ardia, D. R. & McGraw, K. J. Red fish, blue fish: Trade-offs between pigmentation and immunity in Betta splendens. Behav. Ecol. 18(6), 1139–1145. https://doi.org/10.1093/beheco/arm090 (2007).Article 

    Google Scholar 
    Rantala, M., Jokinen, I., Kortet, R., Vainikka, A. & Suhonen, J. Do pheromones reveal male immunocompetence?. Proc. R. Soc. B 269, 1681–1685 (2002).Article 

    Google Scholar 
    Worden, B., Parker, P. & Pappas, P. Parasites reduce attractiveness and reproductive success in male grain beetles. Anim. Behav. 59, 543–550 (2000).CAS 
    Article 

    Google Scholar 
    Barthel, A., Staudacher, H., Schmaltz, A., Heckel, D. G. & Groot, A. T. Sex-specific consequences of an induced immune response on reproduction in a moth. BMC Evol. Biol. 15(1), 282. https://doi.org/10.1186/s12862-015-0562-3 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sadd, B. et al. Modulation of sexual signalling by immune challenged male mealworm beetles (Tenebrio molitor L.): Evidence for terminal investment and dishonesty. J. Evol. Biol. 19(2), 321–325. https://doi.org/10.1111/j.1420-9101.2005.01062.x (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    Chemnitz, J., Bagrii, N., Ayasse, M. & Steiger, S. Variation in sex pheromone emission does not reflect immunocompetence but affects attractiveness of male burying beetles—a combination of laboratory and field experiments. Sci. Nat. 104(7), 53. https://doi.org/10.1007/s00114-017-1473-5 (2017).CAS 
    Article 

    Google Scholar 
    Johansson, B. G. & Jones, T. M. The role of chemical communication in mate choice. Biol. Rev. 82(2), 265–289. https://doi.org/10.1111/j.1469-185X.2007.00009.x (2007).Article 
    PubMed 

    Google Scholar 
    Rantala, M. J., Kortet, R., Kotiaho, J. S., Vainikka, A. & Suhonen, J. Condition dependence of pheromones and immune function in the grain beetle Tenebrio molitor. Funct. Ecol. 17(4), 534–540 (2003).Article 

    Google Scholar 
    Niven, J. E. & Laughlin, S. B. Energy limitation as a selective pressure on the evolution of sensory systems. J. Exp. Biol. 211(11), 1792. https://doi.org/10.1242/jeb.017574 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Stöckl, A. et al. Differential investment in visual and olfactory brain areas reflects behavioural choices in hawk moths. Sci. Rep. 6(1), 26041. https://doi.org/10.1038/srep26041 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Elgar, M. A. et al. Insect antennal morphology: The evolution of diverse solutions to odorant perception. Yale J. Biol. Med. 91(4), 457–469 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Symonds, M. R. E., Johnson, T. L. & Elgar, M. A. Pheromone production, male abundance, body size, and the evolution of elaborate antennae in moths. Ecol. Evol. 2(1), 227–246. https://doi.org/10.1002/ece3.81 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chapman, R. F. Chemoreception: The significance of receptor numbers. In Advances in Insect Physiology (eds Berridge, M. J. et al.) 247–356 (Academic Press, Cambridge, 1982).
    Google Scholar 
    Symonds, M. R. E. & Elgar, M. A. The evolution of pheromone diversity. Trends Ecol. Evol. 23(4), 220–228. https://doi.org/10.1016/j.tree.2007.11.009 (2008).Article 
    PubMed 

    Google Scholar 
    Wyatt, T. Pheromones and Animal Behaviour: Communication by Smell and Taste (Cambridge University Press, Cambridge, 2003).Book 

    Google Scholar 
    Elgar, M. A., Johnson, T. L. & Symonds, M. R. E. Sexual selection and organs of sense: Darwin’s neglected insight. Anim. Biol. 69(1), 63–82. https://doi.org/10.1163/15707563-00001046 (2019).Article 

    Google Scholar 
    Wang, Q. et al. 2018 Antennal scales improve signal detection efficiency in moths. Proc. R. Soc. B 285, 20172832. https://doi.org/10.1098/rspb.2017.2832 (1874).CAS 
    Article 

    Google Scholar 
    Johnson, T. L., Symonds, M. & Elgar, M. Sexual selection on receptor organ traits: Younger females attract males with longer antennae. Sci. Nat. 104, 1–6 (2017).CAS 
    Article 

    Google Scholar 
    Xu, J. & Wang, Q. Male moths undertake both pre- and in-copulation mate choice based on female age and weight. Behav. Ecol. Sociobiol. 63(6), 801–808. https://doi.org/10.1007/s00265-009-0713-x (2009).MathSciNet 
    Article 

    Google Scholar 
    Fricke, C., Adler, M. I., Brooks, R. C. & Bonduriansky, R. The complexity of male reproductive success: Effects of nutrition, morphology, and experience. Behav. Ecol. 26(2), 617–624. https://doi.org/10.1093/beheco/aru240 (2015).Article 

    Google Scholar 
    Bernays, E. A. & Chapman, R. F. Phenotypic plasticity in numbers of antennal chemoreceptors in a grasshopper: Effects of food. J. Comp. Physiol. 183(1), 69–76. https://doi.org/10.1007/s003590050235 (1998).CAS 
    Article 

    Google Scholar 
    Johnson, T. L., Symonds, M. R. E. & Elgar, M. A. 2017 Anticipatory flexibility: Larval population density in moths determines male investment in antennae, wings and testes. Proc. R. Soc. B 284(1866), 2017–2087. https://doi.org/10.1098/rspb.2017.2087 (1866).Article 

    Google Scholar 
    Pomiankowski, A. & Møller, A. P. A resolution of the lek paradox. Proc. R. Soc. Lond. B 260(1357), 21–29. https://doi.org/10.1098/rspb.1995.0054 (1995).ADS 
    Article 

    Google Scholar 
    Cardé, R. & Baker, T. Sexual communication with pheromones. In Chemical Ecology of Insects (eds Bell, W. & Cardé, R.) (Chapman and Hall, London, 1984).
    Google Scholar 
    Kokko, H. & Wong, B. B. M. What determines sex roles in mate searcing?. Evolution 61(5), 1162–1175. https://doi.org/10.1111/j.1558-5646.2007.00090.x (2007).Article 
    PubMed 

    Google Scholar 
    Alberts, A. Constraints on the design of chemical communication systems in terrestrial vertebrates. Am. Nat. 139, S62–S89 (1992).Article 

    Google Scholar 
    van Dongen, S., Matthysen, E., Sprengers, E. & Dhondt, A. A. Mate selection by male winter moths Operophtera brumata (Lepidoptera, Geometridae): Adaptive male choice or female control?. Behaviour 135, 29–42 (1998).Article 

    Google Scholar 
    Henneken, J., Goodger, J. Q. D., Jones, T. M. & Elgar, M. A. Diet-mediated pheromones and signature mixtures can enforce signal reliability. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2016.00145 (2017).Article 

    Google Scholar 
    Harari, A. R., Zahavi, T. & Thiéry, D. Fitness cost of pheromone production in signaling female moths. Evolution 65(6), 1572–1582. https://doi.org/10.1111/j.1558-5646.2011.01252.x (2011).Article 
    PubMed 

    Google Scholar 
    Pham, H. T., McNamara, K. B. & Elgar, M. A. Socially cued anticipatory adjustment of female signalling effort in a moth. Biol. Lett. 16(12), 20200614. https://doi.org/10.1098/rsbl.2020.0614 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morgan, F. D. & Cobbinah, J. R. Oviposition and establishment of Uraba lugens (Walker), the gum leaf skeletoniser. Aust. For. 40(1), 44–55. https://doi.org/10.1080/00049158.1977.10675665 (1977).Article 

    Google Scholar 
    Pham, H. T., McNamara, K. B. & Elgar, M. A. Age-dependent chemical signalling and its consequences for mate attraction in the gumleaf skeletonizer moth, Uraba lugens. Anim. Behav. 173, 207–213. https://doi.org/10.1016/j.anbehav.2020.12.010 (2021).Article 

    Google Scholar 
    McNamara, K. B., van Lieshout, E., Jones, T. M. & Simmons, L. W. Age-dependent trade-offs between immunity and male, but not female, reproduction. J. Anim. Ecol. 82(1), 235–244. https://doi.org/10.1111/j.1365-2656.2012.02018.x (2012).Article 
    PubMed 

    Google Scholar 
    Simmons, L. W. Resource allocation trade-off between sperm quality and immunity in the field cricket, Teleogryllus oceanicus. Behav. Ecol. 23(1), 168–173. https://doi.org/10.1093/beheco/arr170 (2012).Article 

    Google Scholar 
    Triseleva, T. A. & Safonkin, A. F. Variation in antennal sensory system in different phenotypes of large fruit-tree tortrix Archips podana Scop (Lepidoptera: Tortricidae). Biol Bull 33(6), 568–572. https://doi.org/10.1134/s1062359006060069 (2006).Article 

    Google Scholar 
    Rasband, W. S. ImageJ (National Institutes of Health, Maryland USA, 2009).
    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Austria, 2013).
    Google Scholar 
    Sanes, J. R. & Hildebrand, J. G. Origin and morphogenesis of sensory neurons in an insect antenna. Dev. Biol. 51(2), 300–319. https://doi.org/10.1016/0012-1606(76)90145-7 (1976).CAS 
    Article 
    PubMed 

    Google Scholar 
    Gill, K. P., Wilgenburg, E. V., Macmillan, D. L. & Elgar, M. A. Density of antennal sensilla influences efficacy of communication in a social insect. Am. Nat. 182(6), 834–840. https://doi.org/10.1086/673712 (2013).Article 
    PubMed 

    Google Scholar 
    Jayaweera, A. & Barry, K. L. Male antenna morphology and its effect on scramble competition in false garden mantids. Sci. Nat. 104(9), 75. https://doi.org/10.1007/s00114-017-1494-0 (2017).CAS 
    Article 

    Google Scholar 
    Greenfield, M. D. Moth sex pheromones: An evolutionary perspective. Fla Entomol. 64(1), 4–17. https://doi.org/10.2307/3494597 (1981).Article 

    Google Scholar 
    McNamara, K. B., van Lieshout, E. & Simmons, L. W. The effect of maternal and paternal immune challenge on offspring immunity and reproduction in a cricket. J. Evol. Biol. 27(6), 1020–1028. https://doi.org/10.1111/jeb.12376 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Foster, S. P. & Anderson, K. G. 2020 Sex pheromone biosynthesis, storage and release in a female moth: Making a little go a long way. Proc. R. Soc. B 287, 20202775. https://doi.org/10.1098/rspb.2020.2775 (1941).CAS 
    Article 

    Google Scholar 
    Gibb, A. R. et al. Major sex pheromone components of the Australian gum leaf skeletonizer Uraba lugens: (10E,12Z)-hexadecadien-1-yl acetate and (10E,12Z)-hexadecadien-1-ol. J. Chem. Ecol. 34(9), 1125–1133. https://doi.org/10.1007/s10886-008-9523-2 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Kerr, A. M., Gershman, S. N. & Sakaluk, S. K. Experimentally induced spermatophore production and immune responses reveal a trade-off in crickets. Behav. Ecol. 21(3), 647–654. https://doi.org/10.1093/beheco/arg035 (2010).Article 

    Google Scholar 
    Ahmed, A. M., Baggott, S. L., Maingon, R. & Hurd, H. The costs of mounting an immune response are reflected in the reproductive fitness of the mosquito Anopheles gambiae. Oikos 97(3), 371–377 (2002).Article 

    Google Scholar 
    Hurd, H. Host fecundity reduction: A strategy for damage limitation?. Trends Parasitol. 17(8), 363–368. https://doi.org/10.1016/S1471-4922(01)01927-4 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    Adamo, S. A. Evidence for adaptive changes in egg laying in crickets exposed to bacteria and parasites. Anim. Behav. 57(1), 117–124. https://doi.org/10.1006/anbe.1998.0999 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    Arnqvist, G. & Nilsson, T. The evolution of polyandry: Multiple mating and female fitness in insects. Anim. Behav. 60, 145–164 (2000).CAS 
    Article 

    Google Scholar 
    Parker, G. A., Lessells, C. M. & Simmons, L. W. Sperm competition games: A general model for precopulatory male-male competition. Evolution 67(1), 95–109. https://doi.org/10.1111/j.1558-5646.2012.01741.x (2013).Article 
    PubMed 

    Google Scholar 
    Simmons, L. W., Lüpold, S. & Fitzpatrick, J. L. Evolutionary trade-off between secondary sexual traits and ejaculates. Trends Ecol. Evol. 32(12), 964–976. https://doi.org/10.1016/j.tree.2017.09.011 (2017).Article 
    PubMed 

    Google Scholar 
    Parker, G. A. & Pizzari, T. Sperm competition and ejaculate economics. Biol. Rev. 85(4), 897–934. https://doi.org/10.1111/j.1469-185X.2010.00140.x (2010).Article 
    PubMed 

    Google Scholar 
    Katsuki, M. & Lewis, Z. A trade-off between pre- and post-copulatory sexual selection in a bean beetle. Behav. Ecol. Sociobiol. 69(10), 1597–1602. https://doi.org/10.1007/s00265-015-1971-4 (2015).Article 

    Google Scholar 
    Gage, M. J. G. Continuous variation in reproductive strategy as an adaptive response to population-density in the moth Plodia interpunctella. Proc. R. Soc. B 261(1360), 25–30 (1995).ADS 
    Article 

    Google Scholar 
    Shiel, B. P., Sherman, C. D. H., Elgar, M. A., Johnson, T. L. & Symonds, M. R. E. Investment in sensory structures, testis size, and wing coloration in males of a diurnal moth species: Trade-offs or correlated growth?. Ecol. Evol. 5(8), 1601–1608. https://doi.org/10.1002/ece3.1459 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rolff, J. Bateman’s principle and immunity. Proc. R. Soc. B 269(1493), 867–872. https://doi.org/10.1098/rspb.2002.1959 (2002).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Calabrese, E. J. & Baldwin, L. A. Hormesis: A generalizable and unifying hypothesis. Crit. Rev. Toxicol. 31(4–5), 353–424. https://doi.org/10.1080/20014091111730 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    Calabrese, E. J. & Mattson, M. P. How does hormesis impact biology, toxicology, and medicine?. NPJ Aging Mech. Dis. 3(1), 13. https://doi.org/10.1038/s41514-017-0013-z (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Density of invasive western honey bee (Apis mellifera) colonies in fragmented woodlands indicates potential for large impacts on native species

    Geslin, B. et al. Massively introduced managed species and their consequences for plant–pollinator interactions. Adv. Ecol. Res. 57, 147–199 (2017).
    Google Scholar 
    Huryn, V. M. B. Ecological impacts of introduced honey bees. Q. R. Biol. 72, 275–297 (1997).
    Google Scholar 
    Stout, J. C. & Morales, C. L. Ecological impacts of invasive alien species on bees. Apidologie 40, 388–409 (2009).
    Google Scholar 
    Hung, K.-L.J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. R. Soc. Ser. B 285, 20172140 (2018).
    Google Scholar 
    Paini, D. R. Impact of the introduced honey bee (Apis mellifera) (Hymenoptera: Apidae) on native bees: A review. Austral Ecol. 29, 399–407 (2004).
    Google Scholar 
    Moritz, R. F. A., Hartel, S. & Neumann, P. Global invasions of the western honey bee (Apis mellifera) and the consequences for biodiversity. Ecoscience 12, 289–301 (2005).
    Google Scholar 
    Paini, D. R. & Roberts, J. D. Commercial honey bees (Apis mellifera) reduce the fecundity of an Australian native bee (Hylaeus alcyoneus). Biol. Cons. 123, 103–112 (2005).
    Google Scholar 
    Munoz, I. & De la Rua, P. Wide genetic diversity in old world honey bees threatened by introgression. Apidologie 52, 200–217 (2021).
    Google Scholar 
    Williams, I. H. The dependences of crop production within the European Union on pollination by honey bees. Agric. Zool. Rev. 6, 229–257 (1994).
    Google Scholar 
    Thompson, C. E., Biesmeijer, J. C., Allnutt, T. R., Pietravalle, S. & Budge, G. E. Parasite pressures on feral honey bees (Apis mellifera sp.). PLoS One 9, e105164 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Belsky, J. & Joshi, N. K. Impact of biotic and abiotic stressors on managed and feral bees. Insects 10, 233 (2019).PubMed Central 

    Google Scholar 
    Medina-Flores, C. A., Guzman-Novoa, E., Hamiduzzaman, M. M., Arechiga-Flores, C. F. & Lopez-Carlos, M. A. Africanized honey bees (Apis mellifera) have low infestation levels of the mite Varroa destructor in different ecological regions in Mexico. Genet. Mol. Res. 13, 7282–7293 (2014).CAS 
    PubMed 

    Google Scholar 
    Portman, Z. M., Tepedino, V. J., Tripodi, A. D., Szalanski, A. L. & Durham, S. L. Local extinction of a rare plant pollinator in Southern Utah (USA) associated with invasion by Africanized honey bees. Biol. Invasions 20, 593–606 (2018).
    Google Scholar 
    Santos, G. M. D. et al. Invasive Africanized honeybees change the structure of native pollination networks in Brazil. Biol. Invasions 14, 2369–2378 (2012).
    Google Scholar 
    Chapman, R. E. & Bourke, A. F. G. The influence of sociality on the conservation biology of social insects. Ecol. Lett. 4, 650–662 (2001).
    Google Scholar 
    Aizen, M. A. et al. When mutualism goes bad: Density-dependent impacts of introduced bees on plant reproduction. New Phytol. 204, 322–324 (2014).
    Google Scholar 
    Breeze, T. D. et al. Agricultural policies exacerbate honeybee pollination service supply-demand mismatches across Europe. PLoS One 9, e82996 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Baum, K. A. et al. Spatial distribution of Africanized honey bees in an urban landscape. Landsc. Urban Plan. 100, 153–163 (2011).
    Google Scholar 
    Ratnieks, F. L. W., Piery, M. A. & Cuadriello, I. The natural nest and nest density of the africanized honey-bee (Hymenoptera, Apidae) near Tapachula, Chiapas, Mexico. Can. Entomol. 123, 353–359 (1991).
    Google Scholar 
    Baum, K. A., Rubink, W. L., Pinto, M. A. & Coulson, R. N. Spatial and temporal distribution and nest site characteristics of feral honey bee (Hymenoptera: Apidae) colonies in a coastal prairie landscape. Environ. Entomol. 33, 727–739 (2004).
    Google Scholar 
    Rangel, J. et al. Africanization of a feral honey bee (Apis mellifera) population in South Texas: Does a decade make a difference?. Ecol. Evol. 6, 2158–2169 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Oldroyd, B. P., Thexton, E. G., Lawler, S. H. & Crozier, R. H. Population demography of Australian feral bees (Apis mellifera). Oecologia 111, 381–387 (1997).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Arundel, J. et al. Remarkable uniformity in the densities of feral honey bee Apis mellifera Linnaeus, 1758 (Hymenoptera: Apidae) colonies in South Eastern Australia. Austral Entomol. 53, 328–336 (2014).
    Google Scholar 
    Remm, J. & Lõhmus, A. Tree cavities in forests—The broad distribution pattern of a keystone structure for biodiversity. For. Ecol. Manag. 262, 579–585 (2006).
    Google Scholar 
    Lindenmayer, D., Crane, M., Blanchard, W., Okada, S. & Montague-Drake, R. Do nest boxes in restored woodlands promote the conservation of hollow-dependent fauna?. Restor. Ecol. 24, 244–251 (2016).
    Google Scholar 
    New South Wales Department of Planning, Industry and Environment 2003. https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2000-2003/competition-from-feral-honeybees-key-threatening-process-listing (accessed 22 Feb 2021).Goldingay, R. L., Rohweder, D. & Taylor, B. D. Nest box contentions: Are nest boxes used by the species they target?. Ecol. Manag. Restor. 21, 115–122 (2020).
    Google Scholar 
    Lindenmayer, D. B. et al. Are nest boxes a viable alternative source of cavities for hollow-dependent animals? Long-term monitoring of nest box occupancy, pest use and attrition. Biol. Cons. 142, 33–42 (2009).
    Google Scholar 
    Lindenmayer, D. B. et al. The anatomy of a failed offset. Biol. Conserv. 210, 286–292 (2017).
    Google Scholar 
    Macak, P. V. Nest boxes for wildlife in Victoria: An overview of nest box distribution and use. Vic. Nat. 137, 4–14 (2020).
    Google Scholar 
    Le Roux, D. S. et al. Effects of entrance size, tree size and landscape context on nest box occupancy: Considerations for management and biodiversity offsets. For. Ecol. Manag. 366, 135–142 (2016).
    Google Scholar 
    Berris, K. K. & Barth, M. PVC nest boxes are less at risk of occupancy by feral honey bees than timber nest boxes and natural hollows. Ecol. Manag. Restor. 21, 155–157 (2020).
    Google Scholar 
    Jaffe, R. et al. Estimating the density of honeybee colonies across their natural range to fill the gap in pollinator decline censuses. Conserv. Biol. 24, 583–593 (2010).PubMed 

    Google Scholar 
    Utaipanon, P., Schaerf, T. M. & Oldroyd, B. P. Assessing the density of honey bee colonies at ecosystem scales. Ecol. Entomol. 44, 291–304 (2019).
    Google Scholar 
    Utaipanon, P., Holmes, M. J., Chapman, N. C. & Oldroyd, B. P. Estimating the density of honey bee (Apis mellifera) colonies using trapped drones: Area sampled and drone mating flight distance. Apidologie 50, 578–592 (2019).CAS 

    Google Scholar 
    Williamson, E. M. Reliability of honey bee hive density estimates using drone sampling: does relative hive size or distance affect a colony’s drone contribution? Honours Thesis, The University of Adelaide (2020).Benson, J. S. The effect of 200 years of European settlement on the vegetation and flora of New South Wales. Cunninghamia 2, 343–370 (1991).
    Google Scholar 
    New South Wales Office of Environment and Heritage 2015. Upgraded NSW woody vegetation extent for 2011. http://data.auscover.org.au/xwiki/bin/view/Product+pages/nsw+5m+woody+extent+and+fpc (accessed 13 May 2020).R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). www.R-project.org (accessed 12 January 2021).Burnham, K. P. & Anderson, D. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

    Google Scholar 
    Albert, A. & Anderson, J. A. On the existence of maximum likelihood estimates in logistic regression models. Biometrika 71, 1–10 (1984).MathSciNet 
    MATH 

    Google Scholar 
    Firth, D. Bias reduction of maximum likelihood estimates. Biometrika 80, 27–38 (1993).MathSciNet 
    MATH 

    Google Scholar 
    Kosmidis, I., Pagui, E. C. K. & Sartori, N. Mean and median bias reduction in generalized linear models. Stat. Comput. 30, 43–59 (2020).MathSciNet 
    MATH 

    Google Scholar 
    Anderson, D. R. Model Based Inference in the Life Sciences: A Primer on Evidence (Springer Science & Business Media, 2007).
    Google Scholar 
    Barton, K. MuMIn: Multi-model inference. R package version 1.43.17 (2016).Hijmans, R. J. Raster: Geographic Data Analysis and Modeling. R package version 3.4-5 (2020).Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.3.0 (2018).Kosmidis, I. brglm2: Bias Reduction in Generalized Linear Models. R package version 0.6.2 (2020).Kosmidis, I., Schumacher, D. detectseparation: Detect and Check for Separation and Infinite Maximum Likelihood Estimates. R package version 0.1 (2020).Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439–446 (2018).
    Google Scholar 
    Pateiro-Lopez, B., Rodriguez-Casal, A. Alphahull: Generalization of the Convex Hull of a Sample of Points in the Plane. R package version 2.2 (2019).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).MATH 

    Google Scholar 
    Wickham, H. The split-apply-combine strategy for data analysis. J. Stat. Softw. 40, 1–29 (2011).
    Google Scholar 
    Wickham, H. Forcats: Tools for working with categorical variables (factors). R package version 0.5.0 (2018).Wickham, H., François, R., Henry, L., Müller, K. dplyr: A Grammar of Data Manipulation. R package version 1.0.0 (2021).Birtchnell, M. J. & Gibson, M. Long-term flowering patterns of melliferous Eucalyptus (Myrtaceae) species. Aust. J. Bot. 54, 745–754 (2006).
    Google Scholar 
    Steinhauer, N. et al. Drivers of colony losses. Curr. Opin. Insect Sci. 26, 142–148 (2018).PubMed 

    Google Scholar 
    Cunningham, S. A., Heard, T. & FitzGibbon, F. The future of pollinators for Australian Agriculture. Aust. J. Agric. Res. 53, 893–900 (2002).
    Google Scholar 
    Hinson, E. M., Duncan, M., Lim, J., Arundel, J. & Oldroyd, B. P. The density of feral honey bee (Apis mellifera) colonies in South East Australia is greater in undisturbed than in disturbed habitats. Apidologie 46, 403–413 (2015).
    Google Scholar 
    McIntyre, S. Ecological and anthropomorphic factors permitting low-risk assisted colonization in temperate grassy woodlands. Biol. Conserv. 144, 1781–1789 (2011).
    Google Scholar 
    Steffan-Dewenter, I. & Kuhn, A. Honeybee foraging in differentially structured landscapes. Proc. R. Soc. B Biol. Sci. 270, 569–575 (2003).
    Google Scholar 
    Wintle, B. A. et al. Global synthesis of conservation studies reveals the importance of small habitat patches for biodiversity. Proc. Natl. Acad. Sci. U.S.A. 116, 909–914 (2019).CAS 
    PubMed 

    Google Scholar 
    Arthur, A. D., Li, J., Henry, S. & Cunningham, S. A. Influence of woody vegetation on pollinator densities in oilseed Brassica fields in an Australian temperate landscape. Basic Appl. Ecol. 11, 406–414 (2010).
    Google Scholar 
    Lindenmayer, D. B. et al. New policies for old trees: Averting a global crisis in a keystone ecological structure. Conserv. Lett. 7, 61–69 (2014).
    Google Scholar 
    Crane, M. J., Lindenmayer, D. B. & Cunningham, R. B. The value of countryside elements in the conservation of a threatened arboreal marsupial Petaurus norfolcensis in agricultural landscapes of south-eastern Australia—the disproportional value of scattered trees. PLoS One 9, e107178 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gibbons, P., Lindenmayer, D. B., Barry, S. C. & Tanton, M. T. Hollow selection by vertebrate fauna in forests of southeastern Australia and implications for forest management. Biol. Conserv. 103, 1–12 (2002).
    Google Scholar 
    Seeley, T. D. & Morse, R. A. The nest of the honey bee (Apis mellifera L.). Insectes Soc. 23, 495–512 (1976).
    Google Scholar 
    Hung, K. L. J., Ascher, J. S., Davids, J. A. & Holway, D. A. Ecological filtering in scrub fragments restructures the taxonomic and functional composition of native bee assemblages. Ecology 100, e02654 (2019).PubMed 

    Google Scholar 
    Cockle, K. L., Martin, K. & Drever, M. C. Supply of tree-holes limits nest density of cavity-nesting birds in primary and logged subtropical Atlantic forest. Biol. Conserv. 143, 2851–2857 (2010).
    Google Scholar 
    Heard, T. Stingless bees. In Australian Native Bees: A Practical Hand Book 106–139 (NSW Department of Primary Industries, 2016).Geoscience Australia 2006. GEODATA TOPO 250K. Commonwealth of Australia. http://pid.geoscience.gov.au/dataset/ga/63999 (accessed 11 December 2020). More

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    Iron and manganese co-limit the growth of two phytoplankton groups dominant at two locations of the Drake Passage

    Sabine, C. L. et al. The oceanic sink for anthropogenic CO2. Science 305, 367–371 (2004).CAS 
    PubMed 

    Google Scholar 
    Landschützer, P. et al. The reinvigoration of the Southern Ocean carbon sink. Science 349, 1221–1224 (2015).PubMed 

    Google Scholar 
    Dunne, J. P., Sarmiento, J. L. & Gnanadesikan, A. A synthesis of global particle export from the surface ocean and cycling through the ocean interior and on the seafloor. Global Biogeochem. Cycles 21, https://doi.org/10.1029/2006GB002907 (2007).Buesseler, K. O., Boyd, P. W., Black, E. E. & Siegel, D. A. Metrics that matter for assessing the ocean biological carbon pump. Proc. Natl Acad. Sci. USA 117, 9679–9687 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    de Baar, H. J. On iron limitation of the Southern Ocean: experimental observations in the Weddell and Scotia Seas. Mar. Ecol. Prog. Ser. 65, 105–122 (1990).
    Google Scholar 
    Twining, B. S. & Baines, S. B. The trace metal composition of marine phytoplankton. Annu. Rev. Mar. Sci. 5, 191–215 (2013).
    Google Scholar 
    Martin, J. H., Fitzwater, S. E. & Gordon, R. M. Iron deficiency limits phytoplankton growth in Antarctic waters. Glob. Biogeochem. Cycles 4, 5–12 (1990).CAS 

    Google Scholar 
    Boyd, P. W. et al. Mesoscale iron enrichment experiments 1993–2005: synthesis and future directions. science 315, 612–617 (2007).CAS 
    PubMed 

    Google Scholar 
    Sunda, W. Feedback interactions between trace metal nutrients and phytoplankton in the ocean. Front. Microbiol. 3, 204 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Martin, J. H. Glacial‐interglacial CO2 change: the iron hypothesis. Paleoceanography 5, 1–13 (1990).
    Google Scholar 
    Martin, J. H., Gordon, R. M. & Fitzwater, S. E. Iron in Antarctic waters. Nature 345, 156 (1990).CAS 

    Google Scholar 
    Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).CAS 

    Google Scholar 
    Behrenfeld, M. J. & Milligan, A. J. Photophysiological expressions of iron stress in phytoplankton. Annu. Rev. Mar. Sci. 5, 217–246 (2013).
    Google Scholar 
    Greene, R. M., Geider, R. J., Kolber, Z. & Falkowski, P. G. Iron-induced changes in light harvesting and photochemical energy conversion processes in eukaryotic marine algae. Plant Physiol. 100, 565–575 (1992).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Raven, J. A., Evans, M. C. & Korb, R. E. The role of trace metals in photosynthetic electron transport in O2-evolving organisms. Photosynthesis Res. 60, 111–150 (1999).CAS 

    Google Scholar 
    Raven, J. A. Predictions of Mn and Fe use efficiencies of phototrophic growth as a function of light availability for growth and of C assimilation pathway. N. Phytologist 116, 1–18 (1990).CAS 

    Google Scholar 
    Wolfe-Simon, F., Grzebyk, D., Schofield, O. & Falkowski, P. G. The role and evolution of superoxide dismutases in algae 1. J. Phycol. 41, 453–465 (2005).CAS 

    Google Scholar 
    Middag, R. D., De Baar, H. J. W., Laan, P., Cai, P. V. & Van Ooijen, J. C. Dissolved manganese in the Atlantic sector of the Southern Ocean. Deep Sea Res. Part II: Topical Stud. Oceanogr. 58, 2661–2677 (2011).CAS 

    Google Scholar 
    Buma, A. G., De Baar, H. J., Nolting, R. F. & Van Bennekom, A. J. Metal enrichment experiments in the Weddell‐Scotia Seas: effects of iron and manganese on various plankton communities. Limnol. Oceanogr. 36, 1865–1878 (1991).CAS 

    Google Scholar 
    Middag, R., de Baar, H. J., Klunder, M. B. & Laan, P. Fluxes of dissolved aluminum and manganese to the Weddell Sea and indications for manganese co‐limitation. Limnol. Oceanogr. 58, 287–300 (2013).CAS 

    Google Scholar 
    Browning, T. J. et al. Strong responses of Southern Ocean phytoplankton communities to volcanic ash. Geophys. Res. Lett. 41, 2851–2857 (2014).CAS 

    Google Scholar 
    Wu, M. et al. Manganese and iron deficiency in Southern Ocean Phaeocystis Antarctica populations revealed through taxon-specific protein indicators. Nat. Commun. 10, 1–10 (2019).
    Google Scholar 
    Browning, T. J., Achterberg, E. P., Engel, A. & Mawji, E. Manganese co-limitation of phytoplankton growth and major nutrient drawdown in the Southern Ocean. Nat. Commun. 12, 884 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Viljoen, J. J. et al. Links between the phytoplankton community composition and trace metal distribution in summer surface waters of the Atlantic southern ocean. Front. Mar. Sci. 6, 295 (2019).
    Google Scholar 
    Arrigo, K. R. Marine microorganisms and global nutrient cycles. Nature 437, 349–355 (2005).CAS 
    PubMed 

    Google Scholar 
    De Baar, H. J. W. von Liebig’s law of the minimum and plankton ecology (1899–1991). Prog. Oceanogr. 33, 347–386 (1994).
    Google Scholar 
    Saito, M. A., Goepfert, T. J. & Ritt, J. T. Some thoughts on the concept of colimitation: three definitions and the importance of bioavailability. Limnol. Oceanogr. 53, 276–290 (2008).CAS 

    Google Scholar 
    Pausch, F., Bischof, K. & Trimborn, S. Iron and manganese co-limit growth of the Southern Ocean diatom Chaetoceros debilis. PLos ONE 14, e0221959 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hopkinson, B. M. et al. Iron limitation across chlorophyll gradients in the southern Drake Passage: phytoplankton responses to iron addition and photosynthetic indicators of iron stress. Limnol. Oceanogr. 52, 2540–2554 (2007).CAS 

    Google Scholar 
    Trimborn, S., Hoppe, C. J., Taylor, B. B., Bracher, A. & Hassler, C. Physiological characteristics of open ocean and coastal phytoplankton communities of Western Antarctic Peninsula and Drake Passage waters. Deep Sea Res. Part I: Oceanographic Res. Pap. 98, 115–124 (2015).CAS 

    Google Scholar 
    Rijkenberg, M. J. et al. The distribution of dissolved iron in the West Atlantic Ocean. PLoS ONE 9, e101323 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Prézelin, B. B., Hofmann, E. E., Mengelt, C. & Klinck, J. M. The linkage between upper circumpolar deep water (UCDW) and phytoplankton assemblages on the west Antarctic Peninsula continental shelf. J. Mar. Res. 58, 165–202 (2000).
    Google Scholar 
    Varela, M., Fernandez, E. & Serret, P. Size-fractionated phytoplankton biomass and primary production in the Gerlache and south Bransfield Straits (Antarctic Peninsula) in Austral summer 1995–1996. Deep Sea Res. Part II: Topical Stud. Oceanogr. 49, 749–768 (2002).CAS 

    Google Scholar 
    Hoffmann, L. J., Peeken, I. & Lochte, K. Effects of iron on the elemental stoichiometry during EIFEX and in the diatoms Fragilariopsis kerguelensis and Chaetoceros dichaeta. Biogeosciences 4, 569–579 (2007).CAS 

    Google Scholar 
    Blanco-Ameijeiras, S. et al. Exopolymeric substances control microbial community structure and function by contributing to both C and Fe nutrition in Fe-limited Southern Ocean provinces. Microorganisms 8, 1980 (2020).CAS 
    PubMed Central 

    Google Scholar 
    Church, M. J., Hutchins, D. A. & Ducklow, H. W. Limitation of bacterial growth by dissolved organic matter and iron in the Southern Ocean. Appl. Environ. Microbiol. 66, 455–466 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Obernosterer, I., Fourquez, M. & Blain, S. Fe and C co-limitation of heterotrophic bacteria in the naturally fertilized region off the Kerguelen Islands. Biogeosciences 12, 1983–1992 (2015).
    Google Scholar 
    Fourquez, M., Obernosterer, I., Davies, D. M., Trull, T. W. & Blain, S. Microbial iron uptake in the naturally fertilized waters in the vicinity of the Kerguelen Islands: phytoplankton–bacteria interactions. Biogeosciences 12, 1893–1906 (2015).
    Google Scholar 
    Fourquez, M. et al. Microbial competition in the subpolar southern ocean: an Fe–C Co-limitation experiment. Front. Mar. Sci. 6, 776 (2020).
    Google Scholar 
    Boyd, P. W. et al. A mesoscale phytoplankton bloom in the polar Southern Ocean stimulated by iron fertilization. Nature 407, 695–702 (2000).CAS 
    PubMed 

    Google Scholar 
    De Baar, H. J. et al. Synthesis of iron fertilization experiments: from the iron age in the age of enlightenment. J. Geophys. Res. Oceans 110, https://doi.org/10.1029/2004JC002601 (2005).Smetacek, V. & Naqvi, S. W. A. The next generation of iron fertilization experiments in the Southern Ocean. Philos. Trans. R. Soc. A: Math., Phys. Eng. Sci. 366, 3947–3967 (2008).CAS 

    Google Scholar 
    Geider, R. J. & La Roche, J. The role of iron in phytoplankton photosynthesis, and the potential for iron-limitation of primary productivity in the sea. Photosynthesis Res. 39, 275–301 (1994).CAS 

    Google Scholar 
    van Leeuwe, M. A. & Stefels, J. Effects of iron and light stress on the biochemical composition of Antarctic Phaeocystis sp. (Prymnesiophyceae). II. Pigment composition. J. Phycol. 34, 496–503 (1998).
    Google Scholar 
    Hoffmann, L. J., Peeken, I., Lochte, K., Assmy, P. & Veldhuis, M. Different reactions of Southern Ocean phytoplankton size classes to iron fertilization. Limnol. Oceanogr. 51, 1217–1229 (2006).CAS 

    Google Scholar 
    Koch, F., Beszteri, S., Harms, L. & Trimborn, S. The impacts of iron limitation and ocean acidification on the cellular stoichiometry, photophysiology, and transcriptome of Phaeocystis antarctica. Limnol. Oceanogr. 64, 357–375 (2019).CAS 

    Google Scholar 
    Koch, F. & Trimborn, S. Limitation by Fe, Zn, Co, and B12 results in similar physiological responses in two antarctic phytoplankton species. Front. Mar. Sci. 6, 514 (2019).
    Google Scholar 
    Peers, G. & Price, N. M. A role for manganese in superoxide dismutases and growth of iron‐deficient diatoms. Limnol. Oceanogr. 49, 1774–1783 (2004).CAS 

    Google Scholar 
    Cefarelli, A. O. et al. Diversity of the diatom genus Fragilariopsis in the Argentine Sea and Antarctic waters: morphology, distribution and abundance. Polar Biol. 33, 1463–1484 (2010).
    Google Scholar 
    Marchetti, A. & Harrison, P. J. Coupled changes in the cell morphology and elemental (C, N, and Si) composition of the pennate diatom Pseudo-nitzschia due to iron deficiency. Limnol. Oceanogr. 52, 2270–2284 (2007).CAS 

    Google Scholar 
    Boyd, P. W. et al. Microbial control of diatom bloom dynamics in the open ocean. Geophys. Res. Lett. 39, https://doi.org/10.1029/2012GL053448 (2012).Behrenfeld, M. J. & Kolber, Z. S. Widespread iron limitation of phytoplankton in the South Pacific Ocean. Science 283, 840–843 (1999).CAS 
    PubMed 

    Google Scholar 
    Strzepek, R. F., Hunter, K. A., Frew, R. D., Harrison, P. J. & Boyd, P. W. Iron‐light interactions differ in Southern Ocean phytoplankton. Limnol. Oceanogr. 57, 1182–1200 (2012).CAS 

    Google Scholar 
    Klunder, M. B. et al. Dissolved Fe across the Weddell Sea and Drake Passage: impact of DFe on nutrient uptake. Biogeosciences 11, 651–669 (2014).
    Google Scholar 
    Trimborn, S. et al. Iron sources alter the response of Southern Ocean phytoplankton to ocean acidification. Mar. Ecol. Prog. Ser. 578, 35–50 (2017).CAS 

    Google Scholar 
    Smith, W. O. & Lancelot, C. Bottom-up versus top-down control in phytoplankton of the Southern Ocean. Antarct. Sci. 16, 531–539 (2004).
    Google Scholar 
    Schoffman, H., Lis, H., Shaked, Y. & Keren, N. Iron–nutrient interactions within phytoplankton. Front. Plant Sci. 7, 1223 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Meijers, A. J. S. The Southern Ocean in the coupled model intercomparison project phase 5. Philos. Trans. R. Soc. A: Math., Phys. Eng. Sci. 372, 20130296 (2014).CAS 

    Google Scholar 
    Hauck, J. et al. On the Southern Ocean CO2 uptake and the role of the biological carbon pump in the 21st century. Glob. Biogeochem. Cycles 29, 1451–1470 (2015).CAS 

    Google Scholar 
    Cabanes, D. J. et al. Using Fe chemistry to predict Fe uptake rates for natural plankton assemblages from the Southern Ocean. Mar. Chem. 225, 103853 (2020).CAS 

    Google Scholar 
    Cutter, G. A. et al. Sampling and sample-handling protocols for GEOTRACES Cruises, Version 3.0 (2017).Gerringa, L. J. A., De Baar, H. J. W. & Timmermans, K. R. A comparison of iron limitation of phytoplankton in natural oceanic waters and laboratory media conditioned with EDTA. Mar. Chem. 68, 335–346 (2000).CAS 

    Google Scholar 
    Hoppe, C. J. et al. Iron limitation modulates ocean acidification effects on Southern Ocean phytoplankton communities. PLoS ONE 8, e79890 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Hathorne, E. C. et al. Online preconcentration ICP-MS analysis of rare earth elements in seawater. Geochemistry, Geophysics, Geosystems 13, https://doi.org/10.1029/2011GC003907 (2012).Rapp, I., Schlosser, C., Rusiecka, D., Gledhill, M. & Achterberg, E. P. Automated preconcentration of Fe, Zn, Cu, Ni, Cd, Pb, Co, and Mn in seawater with analysis using high-resolution sector field inductively-coupled plasma mass spectrometry. Analytica Chim. Acta 976, 1–13 (2017).CAS 

    Google Scholar 
    Utermöhl, H. Zur vervollkommnung der quantitativen phytoplankton-methodik: Mit 1 Tabelle und 15 abbildungen im Text und auf 1 Tafel. Int. Ver. f.ür. theoretische und Angew. Limnologie: Mitteilungen 9, 1–38 (1958).
    Google Scholar 
    Edler, L. Recommendations on Methods for Marine Biological Studies in the Baltic Sea. Phytoplankton and Chlorophyll (Publication-Baltic Marine Biologists BMB (Sweden), 1979).Tomas, C. R. & Haste, G. R. Identifying Marine Phytoplankton (Academic Press, 1997).Olson, R. J., Zettler, E. R., Chisholm, S. W. & Dusenberry, J. A. in Particle Analysis in Oceanography 351–399 (Springer, 1991).Koch, F., Sanudo-Wilhelmy, S. A., Fisher, N. S. & Gobler, C. J. Effect of vitamins B1 and B12 on bloom dynamics of the harmful brown tide alga, Aureococcus anophagefferens (Pelagophyceae). Limnol. Oceanogr. 58, 1761–1774 (2013).CAS 

    Google Scholar 
    Welschmeyer, N. A. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnol. Oceanogr. 39, 1985–1992 (1994).CAS 

    Google Scholar 
    Oxborough, K. et al. Direct estimation of functional PSII reaction center concentration and PSII electron flux on a volume basis: a new approach to the analysis of Fast Repetition Rate fluorometry (FRRf) data. Limnol. Oceanogr.: Methods 10, 142–154 (2012).
    Google Scholar 
    Schlitzer, R. Ocean Data View. (2015). More

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    A scientist by any other name

    Many women in science, technology, engineering and mathematics (STEM) need to make decisions about marital name change, and have to consider how this might affect their publication record and future career. Mentorship that considers race, ethnicity, culture, religion and parenting, as well as a centralized system to dynamically and retroactively streamline name change, will promote agency and choice for women navigating STEM careers, writes Bala Chaudhary.Women, whether in same-sex or heterosexual relationships, still predominantly make decisions regarding marital name change1. In science, technology, engineering and mathematics (STEM) fields, as the proportion of female researchers rises, more women are considering the potential effects of marital name change on their careers. The stakes are high, as relationship status and name discrimination contribute to gender2 and racial3 inequities in faculty hiring. The shifting demographics of students and a greater proportion of STEM undergraduates engaging in research and publishing has also led to more scientists questioning decisions around name changes. Dual-scientist couples considering sharing a last name may wonder about gendered assessments of their contributions to work. Women occasionally ask for advice on this topic using social-media platforms such as Twitter. Community members chime in with myriad options: keep your name, change your name, hyphenate, add a middle name, couples choose a new name, keep separate personal and legal names, and so on. There is no single correct approach for this personal decision, so online discussions and testimonials4 are invaluable resources for women with few immediate role models. More

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    How itchy vicuñas remade a vast wilderness

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    When mange began to kill llama-like animals called vicuñas in the high Andes, their loss reverberated through the food web to affect grasslands and, eventually, condors1.

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    doi: https://doi.org/10.1038/d41586-022-00592-8

    ReferencesMonk, J. D. et al. Ecol. Lett. https://doi.org/10.1111/ele.13983 (2022).PubMed 
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    Learning from symbioses

    Esperanza Martínez-Romero is a professor of ecological genomics and was coordinator of the undergraduate programme on genomics at Universidad Nacional Autónoma de México. Her work on plant symbioses, and outreach with local farmers has encouraged uptake of sustainable practices and the use of biofertilizers.It was during my first year as an undergraduate student that I was exposed to genetic engineering, when Dr Francisco Bolívar lectured on his development of vectors for gene cloning. I found these results fascinating, and it was listening to talks from scientists at my institute that made me realize that research was my vocation. Towards the end of my bachelor’s degree, Dr Marc von Montagu from Belgium visited and told us about plant genetic transformations — a new field within genetic engineering. Although I was accepted into his laboratory to do my doctorate, I preferred Mexico. I turned my academic journey around and instead chose to apply to a new research centre in Cuernavaca outside of Mexico City — my next turning point. I suspected that a new research centre would provide more opportunities for the development of novel areas, and would have open positions for researchers. Indeed, I was hired at this new research centre and started my own ecology group. It was there that I started working with nitrogen-fixing bacteria and plants. The effects of nitrogen-fixing bacteria on plants were outstanding. Although the scope of molecular biology was incipient to the characterization of bacterial species and populations, we were nevertheless able to make molecular characterizations of the rhizobial species that formed nitrogen-fixing nodules on beans — the most important legume for human consumption in the world. In 1991, we described a novel species, Rhizobium tropici, which could deliver high levels of nitrogen to legumes. It was then that I realized nitrogen fixation is key to the development of sustainable agriculture and could benefit farmers in Mexico and around the world. Some of the species described by my group are now used as inoculants in agriculture, reducing the use of chemical fertilizers and allowing farmers to make cost savings. To facilitate this, I published a manual on biofertilization for farmers and gave conferences and workshops to them. My group has also undertaken reforestation programmes using nitrogen-fixing legume trees inoculated with the rhizobial species that we described. More