Philippa Kaur

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The bee health modelThe conceptual model of the interactions of various stressors with honey bee health is described by the following system of ordinary differential equations (ODEs)$${{tau }_{{HB}}dot{x}}_{{HB}}= {-{delta }_{{HB}}x}_{{HB}}+{g}_{{TC}}left({x}_{{TC}}right)+{g}_{{VA}}left({x}_{{VA}}right)+{g}_{{VI}}left({x}_{{VI}}right) \ +{bar{f}}_{S,C}left({u}_{S},{u}_{C},{x}_{{TC}},{x}_{{VA}}right)+{bar{f}}_{P}left({u}_{P},{x}_{{TC}}right)+{underline{f}}_{{HB}}left({u}_{T}right)$$
(1)
$${{tau }_{{TC}}dot{x}}_{{TC}}={-{delta }_{{TC}}x}_{{TC}}+{g}_{{HB}}left({x}_{{HB}}right)$$
(2)
$${{tau }_{{VA}}dot{x}}_{{VA}}={-{delta }_{{VA}}x}_{{VA}}+{h}_{{VA}}left({{x}_{{HB}},x}_{{TC}},varepsilon {x}_{{VI}}right)+{underline{f}}_{{VA}}left({u}_{T}right)$$
(3)
$${{tau }_{{VI}}dot{x}}_{{VI}}={-{delta }_{{VI}}x}_{{VI}}+{h}_{{VI}}left({{x}_{{HB}},x}_{{TC}},{varepsilon x}_{{VI}}right)$$
(4)
for the state variables ({x}_{{HB}}) representing honey bee health, ({x}_{{TC}}) the stress due to toxic compounds (e.g., neonicotinoid insecticides), ({x}_{{VA}}) the stress due to parasites (e.g., V. destructor) and ({x}_{{VI}}) the stress due to pathogens (e.g., DWV). The system includes the effects of external inputs as sugar ({u}_{S}), pollen ({u}_{P}), absolute deviation from desired temperature ({u}_{T}) and sub-optimal temperature ({u}_{C}). All the inputs and possible parameters are non-negative; the coefficients (tau) denote the time constants; the coefficients (delta) denote the self-regulation parameters; (varepsilon) in the last two equations allows to account for pathogens that can ((varepsilon , > , 0)) or cannot ((varepsilon=0)) impair the immune system (through link m in Fig. 1). We assume that the functions (g) are smooth, bounded, positive, convex and decreasing to 0; the functions (bar{f}) are smooth, bounded, non-negative, concave and increasing with respect to (w.r.t.) (u) arguments (vanishing only when the first (u) argument vanishes) while convex and decreasing to 0 w.r.t. (x) arguments; the functions ({underline{f}}) are smooth, bounded, non-positive and decreasing (vanishing only when (u=0)); the functions (h) are smooth, bounded, positive, convex and decreasing to 0 w.r.t. the first argument while concave and increasing w.r.t. all the other arguments. For a detailed description of the various functions, together with a summary of the biological effects they account for and a reference to the conceptual model in Fig. 1, see Supplementary Table 3.Structural analysis of the bee health modelWe describe here the structural considerations and computations that yield the structural influence matrix for the honey bee health system.The structural influence matrix (M) is defined as follows. (M) is a symbolic matrix with entries ({M}_{{ij}}) chosen among: +,−,0,?, according to the criteria described below. Consider an equilibrium point (bar{x}) and a constant perturbation (u) applied on the (j)-th system variable (small enough not to compromise the stability of the equilibrium). The equilibrium value will be modified as (bar{x}+delta bar{x}). Consider the sign of the perturbation of the (i)-th variable, (delta bar{{x}_{i}}). Then ({M}_{{ij}}) = + if (delta bar{{x}_{i}}) always has the same sign as (u); ({M}_{{ij}}=) − if (delta bar{{x}_{i}}) always has the opposite sign as (u); ({M}_{{ij}}) = 0 if always (delta bar{{x}_{i}}=0); regardless of the system parameters. Conversely, if the sign does depend on the system parameters, we set ({M}_{{ij}}) = ?.In this section we prove that the influence matrix of the honey bee health system is structurally determined, i.e., there are no “?”‘ entries in (M).We start with the following proposition.
Proposition 1
Assume that a matrix
(J)
is Hurwitz stable (i.e., all its eigenvalues have negative real part) and has the sign pattern
$${sign}left(Jright)=left[begin{array}{cccc}- & – & – & -\ – & – & 0 & 0\ – &+& – &+\ – &+& 0 & -end{array}right]$$
Then, the sign pattern of
({adj}left(-Jright))
, the adjoint of
(-J)
, is
$${sign}left({adj}left(-Jright)right)=left[begin{array}{cccc}+& – & – & -\ – &+&+&+\ – &+&+&+\ – &+&+&+end{array}right]$$
Proof To prove the statement, we just change the sign of the first variable, hence we change sign to the first row and column of matrix (J). The resulting matrix (M) is such that$${sign}left(Mright)=left[begin{array}{cccc}- &+&+&+\+& – & 0 & 0\+&+& – &+\+&+& 0 & -end{array}right]$$We observe that (M) is a Metzler matrix, namely, all its off-diagonal entries are non-negative. Moreover, the matrix is Hurwitz stable. Then, we can proceed as in the proof of Proposition 4 in a previous report16. Given a Metzler matrix that is Hurwitz stable, its inverse has non-positive entries; hence, the inverse of (-M) has non-negative entries: ({left(-Mright)}^{-1}ge 0) elementwise. Moreover, we observe that(,M) is an irreducible matrix, i.e., there is no variable permutation that brings the matrix in a block (either upper or lower) triangular form. This implies that the inverse of (-M) has strictly positive entries: ({left(-Mright)}^{-1} , > , 0) elementwise. Also, stability implies that the determinant of (-M) is positive: ({det }left(-Mright) , > , 0). Then, ({adj}left(-Mright)={left(-Mright)}^{-1}{det }left(-Mright) > 0), hence the adjoint of (-M) is also positive elementwise. To consider again the original sign of the variables, we change sign to the first row and column of ({adj}left(-Mright)), and we get the signature above for ({adj}left(-Jright)).The next step is the characterization of the structural influence matrix, which corresponds to the sign pattern of the adjoint of the negative Jacobian matrix in Proposition 1.To this aim, we first consider the linearized system and write it in a matrix-vector form$$dot{x}left(tright)={Jx}left(tright)+{e}_{j}u$$where (dot{x}left(tright)) is the time derivative of the four-dimensional vector (xleft(tright)) and ({e}_{k}), (k={{{{mathrm{1,2,3,4}}}}}), is an input vector, constant in time, with a single non-zero component, the (k)-th, equal to 1, while the scalar (u , > , 0) is the magnitude of the input. We wish to assess the (i)-th component of (xleft(tright)), ({x}_{i}left(tright)={e}_{i}^{T}xleft(tright)). If (J) is Hurwitz, as assumed, the steady-state value of variable ({x}_{i}left(tright)) due to the input perturbation ({e}_{k}) applied to the equation of variable ({x}_{k}left(tright)) is achieved for$$0=Jbar{x}+{e}_{k}u,$$namely$${x}_{i}=-{e}_{i}^{T}{J}^{-1}{e}_{k}u,$$which implies that the sign of the steady-state value ({bar{x}}_{i}) of variable ({x}_{i}) due to a persistent positive input acting on the (k)-th equation has the same sign as ({(-{J}^{-1})}_{{ik}}), the (left(i,kright)) entry of matrix ({left(-Jright)}^{-1}). Since we assume Hurwitz stability, we have that ({det }left(-Jright)) is positive, hence the sign pattern of the inverse ({left(-Jright)}^{-1}) corresponds to the sign pattern of the adjoint, ({adj}left(-Jright)). In fact, ({adj}left(-Jright)={left(-Jright)}^{-1}{det }left(-Jright)).We next consider the nonlinear system under investigation, which we write in the form$$dot{x}left(tright)=fleft(xleft(tright)right)$$and without restriction we assume that the zero vector is an equilibrium point: (0=fleft(0right)). This condition can be always achieved, without loss of generality, by a translation of coordinates. We also consider a stable equilibrium: we assume that the linearized system at the equilibrium is asymptotically stable, namely its Jacobian (J), which has the sign pattern considered in Proposition 1 above, is Hurwitz. We also assume that a constant input perturbation of magnitude (u) is applied to the system, affecting the (k)-th equation, i.e.,$$dot{x}left(tright)=fleft(xleft(tright)right)+{e}_{k}u,$$and that the perturbation is small enough to keep the state in the domain of attraction of the considered equilibrium. Due to this perturbation, a new steady state (bar{x}left(uright)) is reached that satisfies the condition$$0=fleft(bar{x}left(uright)right)+{e}_{k}u$$To determine the sign of the new equilibrium components (bar{x}left(uright)), we consider this new equilibrium vector as a function of (u) in a small interval (left[0,{x}_{{MAX}}right]). Adopting the implicit function theorem yields$$frac{d}{{dx}}bar{x}left(uright)=-J{left(uright)}^{-1}{e}_{k}u,$$where we have denoted by (Jleft(uright)) the Jacobian matrix computed at the perturbed equilibrium (bar{x}left(uright)). Hence, for (u) small enough, the sign of the derivatives of the entries of the new, perturbed equilibrium are, structurally, the same as those in the (k)-th column of matrix (-{J}^{-1}). Since, by construction, (xleft(0right)=0), this is also the sign of the elements of vector (bar{x}left(uright)), for (u) in the interval (left[0,{x}_{{MAX}}right]).We have therefore proved that the original nonlinear system describing honey bee health admits the following structural influence matrix:$$left[begin{array}{cccc}+& – & – & -\ – &+&+&+\ – &+&+&+\ – &+&+&+end{array}right]$$System equilibriaThe results concerning the system equilibria were obtained through a standard analytical treatment of the nonlinear equations describing the equilibrium conditions of the system of differential Eqs. (1), (2), (3), (4). A detailed description of methods is reported in Supplementary Methods.Laboratory experiments using honey beesTo confirm the bistability of the system representing honey bee health as affected by multiple stressors, we used data from several survival experiments, carried out in a laboratory environment according to the same standardized method, over a 6-year period (Source data file).All experiments involved Apis mellifera worker bees, sampled at the larval stage or before eclosion, from the hives of the experimental apiary of the University of Udine (46°04′54.2″N, 13°12′34.2″E). Previous studies indicated that the local bee population consists of hybrids between A. mellifera ligustica and A.m. carnica62,63. Ethical approval was not required for this study.We considered experiments on the effect of the following stressors: infection with 1000 DWV genome copies administered through the diet before pupation, feeding with a 50 ppm nicotine in a sugar solution at the adult stage, exposition to a sub-optimal temperature of 32 °C at the adult stage. All experiments were replicated 3 to 13 times, using, in total, the number of bees reported in Table 1.For the artificial infection with DWV, we collected with soft forceps individual L4 larvae from the brood cells of several combs. Groups of 20–30 of such larvae were placed in Petri dishes with an artificial diet made of 50% royal jelly, 37% distilled water, 6% glucose, 6% fructose, and 1% yeast. 25 DWV copies per mg of diet were added or not to the diet according to the experimental group (note that a bee larva at this stage consumes about 40 mg of larval food per day, thus the viral infection per bee was 1000 viral copies). After 24 h larvae were transferred onto a piece of filter paper to remove the residues of the diet and then into a clean Petri dish, where they were maintained until eclosion. At the day of emergence, bees were transferred to plastic cages in a thermostatic cabinet, where they were kept until death. The DWV extract was prepared according to previously described protocols64 and quantified according to standard methods.For the treatment with nicotine, 10 µL of pure nicotine were added to 200 g of the sugar solution used for the feeding of the caged bees, to reach the concentration of 50 ppm.Finally, to expose bees to a 32 °C temperature, the plastic cages with the adult bees were kept in a thermostatic cabinet whose temperature was set accordingly.To monitor the survival of the adult bees treated as above, they were maintained from eclosion until death in plastic cages in a dark incubator at 34.5 °C (or 32 °C, according to the experiment), 75% R.H.; two syringes were used to supply a sugar solution made of 2.4 mol/L of glucose and fructose (61% and 31%, respectively) and water, respectively; dead bees were counted daily.All the results of these experiments are reported in Source data file.All experiments were carried out during the summer months, from June to September for 6 consecutive years. Previous data indicated that, in this region, virus prevalence increases along the active season starting from very low levels in spring and reaching 100% of virus-infected honey bees by the end of the summer; virus abundance in infected honey bees follows a similar trend28. For this reason, it can be assumed that bees sampled early in the season are either uninfected or they bear only a very low viral infection level, whereas bees sampled later in the season are likely to be virus-infected, bearing moderate to high viral infections. To confirm this assumption and identify a method for filtering our data according to viral infection, we assessed viral infection in a sample of bees from the untreated control group of each experiment, by means of qRT-PCR. According to standard practice, we assumed that Ct values below 30 are indicative of an effective viral infection, whereas Ct above that threshold are more likely in virus negative bees. As expected, we found that virus prevalence increases from June to September (Supplementary Figure 1a), in such a way that up to mid July only the minority of bees can be considered as viral infected (Supplementary Figure 1b). Therefore, we classified as “early” all the samples collected up to mid July and assumed that viral infection in those samples was low; on the other hand, samples collected from mid July till September were classified as “late” and we assumed that viral infection in those samples was high.qRT-PCR analysis of viral infection was carried out as follows. At the beginning of every experiment (i.e., at day 0), two to five bees for each replication were sampled in liquid nitrogen and transferred in a −80 °C refrigerator. After defrosting of samples in RNA later, the gut of each honey bee was eliminated to avoid the clogging of the mini spin column used after. The whole body of sampled bees was homogenized using a TissueLyser (Qiagen®, Germany). Total RNA was extracted from each bee according to the procedure provided with the RNeasy Plus mini kit (Qiagen®, Germany). The amount of RNA in each sample was quantified with a NanoDrop® spectrophotomer (ThermoFisher™, USA). cDNA was synthetized starting from 500 ng of RNA following the manufacturer specifications (PROMEGA, Italy). Additional negative control samples containing no RT enzyme were included. DWV presence was verified by qRT-PCR considering as positive all samples with a Ct value lower than 30. The following primers were adopted: DWV (F: GGTAAGCGATGGTTGTTTG, R: CCGTGAATATAGTGTGAGG65). 10 ng of cDNA from each sample were analyzed using SYBR®green dye (Ambion®) according to the manufacturer specifications, on a BioRad CFX96 Touch™ Real time PCR Detector. Primer efficiency was calculated according to the formula (E={10}^{left(-1/{{{{{{rm{slope}}}}}}}-1right)*100}). The following thermal cycling profiles were adopted: one cycle at 95 °C for 10 min, 40 cycles at 95 °C for 15 s and 60 °C for 1 min, and one cycle at 68 °C for 7 min.Individual survival and colony stabilityTo investigate how the death rate of forager bees affects colony growth, a compartment model of honey bee colony population dynamics was proposed50. This model showed that death rates over a critical threshold led to colony failure. Here we modified this model to include premature death of bees at younger age, as predicted by our model of individual bee health in the presence of an immuno-suppressive virus. We show that the critical threshold found in the previously published model50 becomes a decreasing function of the death rate of the younger individuals, so that premature death (and, in turn, immune-suppression) favors colony collapse.In more details, we first summarize the results of the previously published model50 where two populations (F) (forager) and (H) (hive) of bees are considered and where conditions are provided on the mortality (m) of (F) under which the whole population collapses: namely, mathematically stated, the system admits the zero equilibrium only. Here we extend the model partitioning (H) in two categories, (Y) (younger hive bees) and (O) (older hive bees), asintroducing an early mortality factor (n) for the young population, showing how such a factor worsens the collapsing condition.The previously published model50 concerns the interaction between hive bees (H) and forager bees (F) and is described by the ODEs$$dot{H}=Lfrac{H+F}{w+H+F}-Hleft(alpha -sigma frac{F}{H+F}right)$$$$dot{F}=Hleft(alpha -sigma frac{F}{H+F}right)-{mF}.$$Above, (L) is the queen’s eggs laying rate, (w) is the rate at which (L) is reached as the total population (H+F) gets large, (alpha) is the maximum rate at which hive bees become forager bees in the absence of the latter, (sigma) measures the reduction of recruitment of hive bees in the presence of forager bees and, finally, (m) is the death rate of forager bees (while the death rate of hive bees is assumed to be negligible).We first summarize the main results in terms of a threshold value for (m) in view of colony collapse, as our further analysis will follow a similar approach. All the parameters are assumed to be positive.The search for the equilibria of the above ODEs leads to the unique nontrivial equilibrium (beyond the trivial one)$$bar{H}=frac{L}{{mJ}}-frac{w}{1+J}$$$$bar{F}=Jbar{H}$$for$$J=Jleft(mright):=frac{alpha -sigma -m+sqrt{{left(alpha -sigma -mright)}^{2}+4malpha }}{2m}.$$Note that (J) is alway positive (and, moreover, it is independent of (L) and (w)). It follows that (bar{F}) and (bar{H}) have the same sign, so that the existence of the nontrivial equilibrium is equivalent to (bar{F}+bar{H} , > , 0). It is not difficult to recover that$$bar{F}+bar{H}=frac{w}{m}left(lfrac{1+J}{J}-mright)$$where (l:=L/w) is introduced for brevity. Then if (alpha le l) we get$$bar{F}+bar{H}=frac{w}{m}left(lfrac{1+J}{J}-mright)ge frac{w}{m}left(alpha frac{1+J}{J}-mright)=frac{w}{m}left(sigma+{mJ}right) , > , 0,$$with the last equality following from$$alpha -sigma frac{J}{1+J}-{mJ}=0,$$which in turn comes from annihilating the right-hand side of the second ODE and from using (J=bar{F}/bar{H}) while searching for equilibria. We conclude that, independently of (m), the colony never collapses if the recruitment rate (alpha) of forager bees is sufficiently low.Hence, we assume (alpha , > , l). Observe that$$bar{F}+bar{H}iff l , > , Jleft(m-lright)$$guarantees existence whenever (m) is sufficiently small, viz. (mle l). Assume then (m , > , l), so that the above condition reads$$J , < , frac{l}{m-l}$$leading to the threshold condition$$m , < , bar{m}:=frac{l}{2}frac{alpha+sigma+sqrt{{left(alpha -sigma right)}^{2}+4sigma l}}{alpha -l}$$by using the definition of (J), see Eq. (2) the previously published model50.A standard stability analysis shows that, assuming (alpha,m , > , l), the nontrivial equilibrium is (globally) asymptotically stable whenever it exists (positive), i.e., whenever (m , < , bar{m}). Otherwise, the only (globally) attracting equilibrium is the trivial one, corresponding to colony collapse (see Fig. 5 for the previously published model50 or Fig. 4 for (n=0)). In the mathematical jargon, the disappearance of the positive equilibrium, for (m) exceeding (bar{m}), is referred to as a transcritical bifurcation43.Now, in view of the outcome of the analysis of our model of individual bee health, we introduce a mortality term for the younger bees. As forager bees are recruited from adult hive bees, we divide the class of hive bees (H) in younger (Y) and older (O), assuming that the former die at a rate (n), while the death rate of the latter remains negligible according to the previously published model50. Obviously, (H=Y+O). The original ODEs are consequently modified as$$dot{Y}=Lfrac{H+F}{w+H+F}-Y$$$$dot{O}=left(1-nright)Y-Hleft(alpha -sigma frac{F}{H+F}right)$$$$dot{F}=Hleft(alpha -sigma frac{F}{H+F}right)-{mF}.$$Note that the sum of the first two equations above gives$$dot{H}=Lfrac{H+F}{w+H+F}-Hleft(alpha -sigma frac{F}{H+F}right)-{nY}.$$The new negative mortality term for younger hive bees, (-{nY}), models the fact that only the younger hive bees die prematurely while the rest of the dynamics is unchanged with respect to the original model.The search for equilibria soon gives$$bar{Y}=Lfrac{bar{H}+bar{F}}{w+bar{H}+bar{F}}$$from the first ODE above, so that the remaining two equilibrium conditions lead to$$bar{H}=frac{{L}_{n}}{{mJ}}-frac{w}{1+J}$$$$bar{F}=Jbar{H}$$for the same (J) originally defined and ({L}_{n}:=Lleft(1-nright)) (note that (nin left({{{{mathrm{0,1}}}}}right)), and the case (n=0) brings us back to the original model). From this point on the analysis is the same as that previously summarized for the original model, but for replacing (L) with ({L}_{n}) and (l) with (l:=lleft(1-nright)). Consequently, by assuming (alpha,m , > , {l}_{n}) (which is less restrictive when (n , > , 0)), the threshold condition (m < bar{m}) becomes$$m , < , bar{m}left(nright):=frac{{l}_{n}}{2}frac{alpha+sigma+sqrt{{left(alpha -sigma right)}^{2}+4sigma {l}_{n}}}{alpha -{l}_{n}},$$which clearly returns the original threshold condition when (n=0). Since$$frac{dbar{m}}{{dn}}left(nright) , < , 0$$as it can be immediately verified, it follows that the critical value for (m), (bar{m}left(nright)), beyond which the colony system admits only the zero equilibrium, i.e., the transcritical bifurcation value, decreases with (n) (Fig. 4). We thus conclude that colony collapse is favored by the premature death of younger hive bees, possibly caused by a virus impairing the immune system as shown by the analysis of our model of individual bee health.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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MaterialsWe collected from the literature and available databases a dataset of radiocarbon dates from Europe (West of 60°E and North of 37°N) either obtained from remains of the four analyzed species or from archaeological layers where they have been observed. However, we only considered observations dated between 7500 and 47,000 cal BP: their scarcity before this period may bias the GAMs, and after it, domesticated cattle, pigs and (later) horses arrived in Europe, making it difficult to differentiate them from their wild forms.We excluded any record fitting one or more of the following conditions: unreliable; not in accord with the expected chronology of their archaeological layer; without a reported standard error; available only as terminus ante/post quem.All dates were calibrated with OxCal5 version 4.4 using the IntCal20 curve51, and we further excluded any record for which calibration resulted in an error, resulting in the number of points presented in Table 1 as “Original dataset” (available at the link https://doi.org/10.6084/m9.figshare.20510364).Table 1 Number of observations for each species.Full size tableSDMs based on GAMs need presence/background data, not frequencies; moreover, multiple observations (i.e., presence in different archaeological layers) from the same site and time slice are likely to introduce stronger sample biases linked to chrono-geographically differential sampling efforts. For this reason, we collapsed our observations by keeping only one point per grid cell per time slice for each species, leaving the number of observations reported in Table 1 as “Collapsed datasets”, used for all the analyses presented in this work.To perform all analyses, we used the R package pastclim v. 1.042 to couple each observation from the collapsed datasets to paleoclimatic reconstructions published in8 by setting dataset = “Beyer2020”. These are based on the Hadley CM3 model, include 14 different bioclimatic variables at a spatial resolution of 0.5°, and are available for the whole world every 1000 years until 22 kya and every 2000 years before that date (referred to in the manuscript as “time slices”). Specifically, each observation was associated with the relevant bioclimatic reconstruction based on its average age and spatial coordinates.As already mentioned, the four species analyzed show different preferences regarding temperature, habitat, and altitude. Therefore, for the Species Distribution Modelling, we choose five environmental variables that should be able to capture such differences: two measures of temperature (BIO5, maximum temperature of the warmest month, and BIO6, minimum temperature of the coldest month); two variables to help capture habitat differentiation (BIO12, total annual precipitation, and Net Primary Productivity, NPP), and one measure of topography (rugosity42).High collinearity can be problematic in SDMs; we confirmed that all our variables had a correlation below 0.7, a threshold commonly adopted for this kind of analysis52,53.Whilst the GAMs predicted all time points; we visualized our results by creating an average estimate for the following periods: pre-LGM (from the beginning of the time range analyzed, i.e., 47 kya to 27 kya), LGM (from 27 to 18 kya), Late Glacial (from 18 to 11.7 kya), Holocene (from 11.7 kya to the end of the time range analyzed, i.e., 7.5 kya).MethodsWe generated 25 sets of background points for each species to adequately represent the existing climatic space in our SDMs. Each set was generated by sampling, for each observation, 50 random locations matched by time. This resulted in n = 25 datasets (“repetitions”) of background points and presences (observations) for each species, which we used to repeat our analyses to account for the stochastic sampling of the background. For each dataset, we used GAMs to fit two possible models: a “constant niche” model, which included only the environmental variables as covariates, and a “changing niche” model, that also included interactions of each environmental variable with time (fitted as tensor products).In GAMs, the effect of a given continuous predictor on the response variable (in our case, the logit transformed probability of a presence) is represented by a smooth function; this smooth function can be linear or non-linear and can become highly complex in shape depending on the number of knots selected by the GAM fitting algorithm. The interaction between two covariates is modelled by tensor products54; this approach is equivalent to an interaction term in a linear model but with the added complexity of the smooth function. In our models, we confine tensor products to the interaction between an environmental variable and time; a simple way to think about such a tensor product is that it allows the smooth representation of the relationship between the variable and the probability of a presence to change progressively over time.GAMs were fitted using the mgcv package in R54 using thin plate regression splines (TPNR; bs = “tp”, default in mgcv) for environmental variables and their tensor products with time in the “niche changing” models. The GAM algorithm automatically selects the complexity of the smooth most appropriate to the data that are being fitted; as GAM can have issues with overfitting, we added an additional penalty against overly complex smooths (gamma = 1.4) and used Restricted Maximum Likelihood (REML = TRUE), as recommended by54. It is possible that even with these settings, the complexity of the smooth is not sufficient; we used mgcv::gam.check() to check this, and increased the basis dimension of the smooth, k, to make sure that k-1 was larger than the estimated degrees of freedom (edf). We found the best maximum thresholds for k to be 16 for bio06 and 10 for all other variables.We checked for non-linear correlation among variables using the mgcv::collinearity function and checked the values of estimated concurvity. All estimates were below the threshold of 0.8 in all models, runs and variables except for a few instances for time (Supplementary Figs. 5–8). We consider this not to be worrying: this is most likely a result of sample bias, and GAM is known to be robust to correlation/concurvity55,56.We verified the model assumptions by inspecting the residuals using the R package DHARMa57. Standard tests for deviations from the expected distribution and dispersion were non-significant for all repetitions for all species, as were the tests for outliers. Furthermore, we tested for spatial autocorrelation among residuals by computing Moran’s I; all tests were either non-significant or, when significance was detected, the estimate of Moran’s I was very close to zero, revealing a trivial deviation from the assumptions which should not impact the results (Supplementary Tables 1–4).We performed model choice (Supplementary Tables 5–8) by comparing the constant- and changing-niche models for each combination of species and repetition using the Akaike Information Criterion (AIC). AIC strongly supported the changing-niche model in all species and repetitions, an inference supported by the higher Nagelkerke R2 and expected deviance for those models than for the constant-niche ones (Supplementary Tables 5–8).The model fit for each of the changing niche GAMs was evaluated with the Boyce Continuous Index25,26, designed to be used with presence-only data58,59. We set a threshold of Pearson’s correlation coefficient  >  0.8 to define acceptable models25 (Supplementary Table 9).The relative importance of each environmental variable was quantified for all the models above the BCI threshold of 0.8 in two different ways. Firstly, we computed the total deviance explained by each variable by simply fitting a GAM with only that variable. We then estimated the unique deviance explained by each variable by comparing the full model with one for which that variable was excluded (i.e., we computed the explained deviance lost by dropping that predictor). The difference between the two values represents the deviance explained by a variable which can also be accounted for by other variables (i.e., the deviance in common with other variables).To achieve more robust predictions60, we averaged in two different ensembles the repetitions for the changing niche GAMs with BCI  > 0.8: by mean and median. This step is intended to reduce the weight of models that are highly sensitive to the random sampling of the background60. Then, for each species, we selected the ensemble (either based on mean or median) with the higher BCI as the most supported and used it to perform all further analyses.The effect of different variables through time was visualized by plotting the interactions of the GAMs. For each model with a BCI  > 0.8, we used the R package gratia27 to generate a surface with time as the x-axis, the environmental variable as the y-axis, and the effect size as the z-axis (visualized as colour shades). We then plotted the mean surface for each species, which captures the signal consistent across all randomized background sets.To visualize the prediction for each species, we then transformed the predicted probabilities of occurrence from the ensemble into binary presence/absences by using the threshold needed to get a minimum predicted area encompassing 99% of our presences (function ecospat.mpa() from the ecospat R package61). The binary predictions were then visualized using the mean over the time steps within each major climatic period.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

General description of the experimental areaThe experiment was performed for two years at the National Key Irrigation Experimental Station located on the Songnen Plain in Heping town, Qing’an County, Suihua, Heilongjiang, China, with a geographical location of 45° 63′ N and 125° 44′ E at an elevation of 450 m above sea level (Fig. 1). This region consists of plain topography and has a semiarid cold temperate continental monsoon climate, i.e., a typical cold region with a black soil distribution area. The average annual temperature is 2.5 °C, the average annual precipitation is 550 mm, the precipitation is concentrated from June to September of each year, and the average annual surface evaporation is 750 mm. The growth period of crops is 156–171 days, and there is a frost-free period of approximately 128 days year−122. The soil at the study site is albic paddy soil with a mean bulk density of 1.01 g/cm3 and a porosity of 61.8% prevails. The basic physicochemical properties of the soil were as follows: the mass ratio of organic matter was 41.8 g/kg, pH value was 6.45, total nitrogen mass ratio was 15.06 g/kg, total phosphorus mass ratio was 15.23 g/kg, total potassium mass ratio was 20.11 g/kg, mass ratio of alkaline hydrolysis nitrogen was 198.29 mg/kg, available phosphorus mass ratio was 36.22 mg/kg and available potassium mass ratio was 112.06 mg/kg.Figure 1Location of the study area. The map and inset map in this image were drawn by the authors using ArcGIS software. The software version used was ArcGIS software v.10.2, and its URL is http://www.esri.com/.Full size imageHumic acid fertilizerHumic acid fertilizer was produced by Yunnan Kunming Grey Environmental Protection Engineering Co., Ltd., China (Fig. 2). The organic matter was ≥ 61.4%, and the total nutrients (nitrogen, phosphorus and potassium) were ≥ 18.23%, of which N ≥ 3.63%, P2O5 ≥ 2.03%, and K2O ≥ 12.57%. The moisture content was ≤ 2.51%, the pH value was 5.7, the worm egg mortality rate was ≥ 95%, and the amount of faecal colibacillosis was ≤ 3%. The fertilizer contained numerous elements necessary for plants. The contents of harmful elements, including arsenic, mercury, lead, cadmium and chromium, were ≤ 2.8%, 0.01%, 7.6%, 0.1% and 4.7%, respectively; these were lower than the test standard.Figure 2Humic acid fertilizer in powder form.Full size imageExperimental design and observation methodsIrrigationIn this experiment, three irrigation practices, namely, control irrigation (C), wet irrigation (W) and flood irrigation (F), were designed (Table 1).Table 1 Different irrigation methods.Full size tableControl irrigation (C) of rice had no water layer in the rest of the growing stages, except for the shallow water layer at the regreen stage of rice, which was maintained at 0–30 mm, and the natural dryness in the yellow stage. The irrigation time and irrigation quota were determined by the root soil moisture content as the control index. The upper limit of irrigation was the saturated moisture content of the soil, the lower limit of soil moisture at each growth stage was the percentage of saturated moisture content, and the TPIME-PICO64/32 soil moisture analyser was used to determine the soil moisture content at 7:00 a.m. and 18:00 p.m., respectively. When the soil moisture content was close to or lower than the lower limit of irrigation, artificial irrigation occurred until the upper irrigation limit was reached. The soil moisture content was maintained between the upper irrigation limit and the lower irrigation limit of the corresponding fertility stage. Under the wet irrigation (W) and flood irrigation (F) conditions, it was necessary to read the depth of the water layer through bricks and a vertical ruler embedded in the field before and after 8:00 am every day to determine if irrigation was needed. If irrigation was needed, then the water metre was recorded before and after each irrigation. The difference between before and after was the amount of irrigation23.FertilizationIn our research, five fertilization methods were applied, as shown in Table 2. In this experiment, the rice cultivar “Suijing No. 18” was selected. Urea and humic acid fertilizer were applied according to the proportion of base fertilizer:tillering fertilizer:heading fertilizer (5:3:2). The amounts of phosphorus and potassium fertilizers were the same for all treatments, and P2O5 (45 kg ha−1) and K2O (80 kg ha−1) were used. Phosphorus was applied once as a basal application. Potassium fertilizer was applied twice: once as a basal fertilizer and at 8.5 leaf age (panicle primordium differentiation stage) at a 1:1 ratio22.Table 2 The fertilizer methods.Full size tableThis study was performed with a randomized complete block design with three replications. Three irrigation practices and five fertilizer methods were applied, for a total of 15 treatments as follows: CT1, CT2, CT3, CT4, CT5; WT1, WT2, WT3, WT4, WT5; FT1, FT2, FT3, FT4, and FT5 (C, W, and F represent control irrigation, wet irrigation, and flood irrigation; T represents fertilizer treatment).Measurements of the samplesA soil temperature sensor (HZTJ1-1) was buried in each experimental plot to monitor the temperature of each soil layer (5 cm, 10 cm, 15 cm, 20 cm and 25 cm depth). The transmission of photosynthetically active radiation was measured from 11:00 to 13:00 by using a SunScan Canopy Analysis System (Delta T Devices, Ltd., Cambridge, UK), and data during the crop-growing season were recorded every day24.Plant measurements were taken during the periods of tillering to ripening on days with no wind and good light. The fluorescence parameters were measured by a portable fluorescence measurement system (Li-6400XT, America). The detection light intensity was 1500 μmol m−2 s−1, and the saturated pulsed light intensity was 7200 μmolm−2 s−1. The functional leaves were dark adapted for 30 min, and then the maximum photosynthetic efficiency of PSII (Fv/Fm) was measured. Photochemical quenching (QP) and nonphotochemical quenching (NPQ) were measured with natural light. Simultaneously, the leaf chlorophyll relative content (SPAD) was monitored using SPAD 502 (Konica Minolta, Inc., Tokyo, Japan). For plant agronomic characteristics, the distance from the stem base to the stem tip was measured with a straight ruler to quantify plant height24.Statistical analysisExperimental data obtained for different parameters were analysed statistically using the analysis of variance technique as applicable to randomized complete block design. Duncan’s multiple range test was employed to assess differences between the treatment means at a 5% probability level. All statistical analyses were performed using SPSS 22.0 for Windows24.
Ethics approvalExperimental research and field studies on plants, including the collection of plant material, comply with relevant institutional, national, and international guidelines and legislation. We had appropriate permissions/licences to perform the experiment in the study area. More