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Study site
Small passerines were caught using a 13 m mist net placed between trees and shrubs on the edge of the village of Tarbet, Argyll & Bute (56.21 N 4.71 W), adjacent to a large forestry plantation. Siskins can forage up to at least 5 km from their nest during the breeding season5,18, and the area within 5 km was therefore considered likely to include breeding siskins that would move through the catching site. Plantation forestry species, age, and area were determined from maps from the Forestry Commission compartment data base. There were 1152 ha of plantation forestry within 5 km of the catching site, comprising 79% Sitka spruce, 7% Norway spruce Picea abies, 13% larch and  More

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National yield stability
We used the FAOSTAT database (http://www.fao.org/faostat, visited in September 2019) to obtain data on annual crop production (in tons) and area harvested (in hectares) from 1961 to 2010 for 138 crops in 91 populous nations. Following Renard and Tilman16, we accounted for differences among nations in data quality and excluded five nations, namely North Korea, Guinea, Kenya, Mozambique and Zambia, for which at least 20% of the data on area harvested or production were extrapolated by the FAO (see details in16). We calculated for each nation and each year the total annual caloric yield (millions of kcal ha-1). To do so, we first calculated the kcal production of each crop by multiplying the production of each crop by its commodity-specific kilocalorie conversion factor from the USDA Nutrient Database32. In doing so, we were able to compare the production of different crops. Then, we summed these kcal harvests across all crops and divided this value by the sum of harvested area for all crops. We calculated national yield stability (S) as the ratio of mean total annual caloric yield (µT) over its time-detrended standard deviation (σT) for fifty consecutive years (1961–2010). We accounted for a temporal trend of increasing total annual crop yield by implementing a loess regression between annual crop yield and years. σT corresponds to the standard deviation of the residuals of this regression. Finally, we compared this stability index (largely used in the biodiversity-ecological functioning research, e.g.14,16,17,18) with the resilience index used by Zampieri et al.22. Both indices were strongly correlated (r = 0.992), strengthening our findings.
Individual crop yield stability and yield asynchrony
For each country, we quantified the average stability of yields of individual crops as the mean of the inverse of the coefficient of variation of yield of each crop:

$$ {{left( {mathop sum limits_{i = 1}^{N} frac{{mu_{i} }}{{sigma_{i} }}} right)} mathord{left/ {vphantom {{left( {mathop sum limits_{i = 1}^{N} frac{{mu_{i} }}{{sigma_{i} }}} right)} N}} right. kern-nulldelimiterspace} N} $$
(1)

where (mu_{i}) is the temporal mean of crop’s annual kcal yield and (sigma_{i}) its time-detrended standard deviation. Time-detrended crop yield was computed through a loess regression between individual, annual crop yield and years.
We computed the asynchrony between crop yield fluctuations following the index developed by Loreau and De Mazancourt11:

$$ Phi = 1 – frac{{sigma^{2}_{T} }}{{left( {mathop sum nolimits_{i = 1}^{N} sigma_{i} } right)^{2} }} $$
(2)

where Φ is the asynchrony of crop species based on annual caloric yield (millions of kcal ha−1) with (sigma_{T}^{2}) the temporal variance of the time-detrended national yield and (sigma_{i}) the time-detrended standard deviation of each crop’s annual kcal yield. The value of asynchrony varies between zero (perfect synchrony) and one (perfect asynchronous temporal fluctuations).
To test whether yield fluctuations of the most abundant crops have a greater impact on the stability of national food production, we weighted the annual yield of each crop by the proportion of total harvested area occupied by that crop. Average stability of yields of individual crops and yield asynchrony were computed on both the non-weighted and abundance-weighted yields.
Crop diversity
For each country and year, we used both the total number of crop commodities (i.e. crop richness) and the Shannon information index (H′) to quantify crop diversity. H′ weights each crop in a nation by the proportion of total cropland it occupies (pi):

$$ H^{prime } = – mathop sum limits_{i = 1}^{N} left( {p_{i} lnleft[ {p_{i} } right]} right) $$
(3)

with N being the total number of crops grown in a country each year.
The exponential form of the Shannon diversity index gives the effective crop diversity that is the number of crops representing an equal share of harvested area24. In other words, the exponential of the Shannon diversity index weighs all species by their frequency, without favouring either common or rare species24. We averaged the annual effective diversity of crop across the fifty years studied to test the effect of crop diversity on national yield stability.
Agricultural inputs
We extracted the annual national application of nitrogen and the annual cropland area equipped for irrigation from the FAOSTAT database. Because Ireland, New Zealand and Netherlands use much of their fertilizers on pastures rather than croplands, we excluded these nations from our analysis. Similarly, we excluded Egypt because it has 100% of cropland equipped for irrigation. We calculated the annual rates of nitrogen application and irrigation per hectare by dividing their use by the total annual cropland area.
Climate variability
We used global gridded climatic data from the Climate Research Unit of the University of East Anglia33 to compute the year-to-year variability of growing season precipitation and temperature for each country, both strongly affecting the stability of national food production16. From these data, we derived annual precipitation and temperature for each grid cell in a country by taking the sum of monthly precipitation and the mean of monthly temperature values weighted by the proportion of cropland in each grid cell34. We then computed the year-to-year coefficient of variation of cropland-based temperature and precipitation for each country.
Statistical analysis
We used structural equation models (SEMs) to evaluate how irrigation, intensity of use of nitrogen fertilizers and crop diversity affected national yield stability through changes in the average stability of yields of individual crops and asynchrony of yields. SEMs represent a powerful way to disentangle complex mechanisms controlling crop diversity-stability relationships, as previously done in natural ecosystems (e.g.14,15,35,36). We set up two different structural equation models, one based on non-weighted indices of stability of individual crops and asynchrony, the other based on the same indices weighted by the proportion of total harvested area accounted for by each crop. We firstly considered the effects of agricultural inputs and crop diversity on the stability of national food production via the path of average yield stability. The second path quantified the indirect effects of agricultural inputs and crop diversity on national stability via their impacts on crop yield asynchrony. We also accounted for the direct effects of agricultural inputs and crop diversity on national yield stability. Finally, we controlled for the effects of climate variability on total, national yield stability, individual crop yield stability and yield asynchrony. SEMs were run with the lavaan R library37. We used the standardized estimates to compare the relative importance of the different paths. The model fit was evaluated using the Fisher C’score and its associated p values. Because the structural equation model assumes linear relationships between predictors and the dependent variable, we also plotted the relationships between total national yield stability and both asynchrony and average stability of individual crop yield to control for linearity (Fig. 2). Similarly, we investigated the relationships between crop diversity and asynchrony (Fig. 3), as well as between irrigation rate and the average stability of individual crop yield (Fig. 4). More

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MDA-MB-231 generally out-compete MCF-7 cells under all pH, glucose and glutamine levels, and starting frequencies
Fluorescently labeled MCF-7 (GFP) and MDA-MB-231 (RFP) cells allowed us to non-destructively measure population dynamics within each micro-spheroid. We used this spheroid system to: (1) observe the outcome of competition (mixed culture), (2) compare population growth models (mono-cultures), (3) measure intrinsic growth rates (DI), (4) measure carrying capacities (DD), and (5) determine competition coefficients (FD). In experiments 1 and 2, spheroids started with a plating density of 20,000 and 10,000 cells, respectively, with initial conditions of 0%, 20%, 40%, 60%, 80% and 100% MDA-MB-231 cells relative to MCF-7 cells. In experiment 1, additional treatments included neutral (7.4) versus low (6.5) pH and varying levels of glucose (0, 1, 2, 4.5 g/L) (Fig. 1b). Experiment 2 had treatments of pH (7.4, 6.5), glucose (0, 4.5 g/L), and glutamine positive and starved media (Fig. 1c). The ranges of these values encompass both physiologic and extreme tumor conditions. For example, pH ~ 6.5 and very low levels of glucose have been measured near the necrotic core of the tumor. Conversely, high glucose and physiologic pH are present at the tumor edge and near vasculature, and also reflect standard culture conditions for our cell lines. In experiment 1, microspheres were grown for 670 h with fluorescence measured every 24–72 h. In experiment 2, microspheres were grown for 550 h with fluorescence measured every 6–12 h. Growth medium was replaced every 4 days without disturbing the integrity of the microsphere.
Counter to our hypothesis, MDA-MB-231 outcompete MCF-7 cells in physiologic (2 g/L glucose, 7.4 pH) conditions (Fig. 2). However, in more acidic conditions (2 g/L glucose, 6.4 pH) the MCF-7 cells appear to persist, suggesting coexistence (Fig. 3, Supplementary Fig. 1). Furthermore MCF-7 cells persist in either pH condition with high 4 g/L glucose (Fig. 4). This raises two questions: (1) Is the success of MDA-MB-231 in most conditions a consequence of higher initial growth rates, increased carrying capacity, or consistently favorable competition coefficients? and, (2) what characteristics of MCF-7 cells allow them to competitively persist at high glucose conditions?
Figure 2

Photographs of spheroids illustrative of the progression and outcome of competition in normal pH. This series shows the progression of spheroids grown physiological conditions (2 g/L glucose, 7.4 pH). Note that, in this case, the MDA-MB-231 cells appear to take over the spheroids by day 20.

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Figure 3

Photographs of spheroids illustrative of the progression and outcome of competition in acidic pH. This series shows the progression of spheroids grown in acidic conditions (6.5 pH) with 2 g/L glucose. Note that, in this case, the MCF-7 cells appear to persist to the end of the experiment.

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Figure 4

Three time points spanning from the middle to the end for all culture conditions.

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Analysis of growth rate
The growth rate of MCF-7 and MDA-MB-231 cells was quantified by measuring the instantaneous exponential growth rate (in units of per day) for each monoculture during the first 72 h. In general, MCF-7 cells had equal or higher growth rates under all growth conditions when compared to MDA-MB-231 cells. MCF-7 cell growth rate with lower (10,000 initial cells) plating density was reduced when grown in low pH 6.5 (Fig. 5b,c) environments compared to physiologic pH 7.4 (Fig. 5e,f). The higher (20,000 initial cells) plating density resulted in reduced growth rates in normal pH compared to low 6.5 pH (Fig. 5a,d). Not surprisingly, in the harshest environment of low pH 6.5, no glucose, and no glutamine, MCF-7 cells had the lowest, even negative, growth rate (Fig. 5c). Interestingly, the absence of glutamine (Fig. 5c,f) reduced the difference in growth rates between the two cell types, regardless of pH or glucose concentration, consistent with glutamine’s role as a substrate for respiration, which is more pronounced in MCF-7 cells.
Figure 5

Growth rates (per day) of both MCF-7 and MDA-MB-231 cells as measured by the instantaneous exponential growth rate for each monoculture in the first 72 h under all experimental conditions. ANOVA analysis is provided in Supplementary Tables 1–3.

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While the growth rates of MDA-MB-231 cells during these early time points were generally unaffected by changes in environmental conditions, the initial plating density had a significant effect. The higher (20,000 initial cells) plating density (Fig. 5a,d) resulted in slower, even negative, growth rates when compared to the lower (10,000 initial cells) plating density. This suggests that these plating densities may be approaching MDA-MB-231’s carrying capacities.
Analysis of carrying capacity
Due to small differences in the RFU intensity between the GFP and RFP used for the MCF-7 and MDA-MB-231 cells respectively, a direct comparison of carrying capacity using these RFU measurements cannot be made. In this way we split the results in to two figures (Figs. 6 and 7). We see RFU’s higher than 60 in the MCF-7 data while the maximum RFU for MDA-MB-231 data is never greater than 20 (please note the varying y-axis limits in the corresponding figures). Even with this difference in RFU intensity, it is not unreasonable to assume, due to the large difference, the MCF-7 cells do indeed have a higher carrying capacity across all experimental conditions.
Figure 6

Carrying capacities of MCF-7 cells under all experimental conditions, as measured by the mean fluorescence of the final ten time points. ANOVA analysis is provided in Supplementary Tables 4, 5.

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Figure 7

Carrying capacities of MDA-MB-231 cells under all experimental conditions, as measured by the mean fluorescence of the final ten time points. ANOVA analysis is provided in Supplementary Tables 4, 5.

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For MCF-7 cells, an increase in glucose resulted in an increase in carrying capacity across experimental conditions, as would be expected (Fig. 6a–f). Surprisingly, the MCF-7 cells experience a decrease in carrying capacity under normal pH conditions when compared to acidic pH 6.5 conditions suggesting an advantage in acidic conditions.
For the MDA-MB-231 cells, after a high seeding density of 20,000 cells, an increase in glucose resulted in an increase in carrying capacity, just like the MCF-7 cells (Fig. 7a,d). For the low seeding density of 10,000 cells, the opposite occurred (Fig. 7b,c,e, and f), though not as dramatically. Furthermore, the MDA-MB-231 cells experience an increase in carrying capacity in normal pH when compared to acidic pH 6.5 conditions suggesting an advantage in normal pH conditions.
Analysis of initial seeding frequencies
For the analysis of frequency dependent interactions of the two cells lines we analyzed the instantaneous growth rates (in units of per day) during the first 72 h in the mixed spheroids (Figs. 8 and 9). The figures are split between low glucose conditions of 0 g/L (Fig. 8) and high glucose conditions of 4.5 g/L (Fig. 9). Interestingly, under both glucose environments, MDA-MB-231 cells have their maximum growth rates when there are high initial frequencies of MCF-7 cells in the spheroid. This suggests some benefit to the MDA-MB-231 cells from the presence of MCF-7 cells, or less inhibition of growth rates from MCF-7 cells. Furthermore, we see that the MCF-7 cells generally show a maximum growth rate when the seeding frequencies are 40%/60% or 60%/40% (Figs. 8 and 9b,c,e, and f), again suggesting possible benefits to having both cell types in the spheroid.
Figure 8

Based on the first 72 hours, estimates of growth rates (per day) for mixed population tumor spheroids under glucose starved conditions of 0 g/L. ANOVA analysis is provided in Supplementary Tables 1–3.

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Figure 9

Based on the first 72 hours, estimates of growth rates (per day) for mixed population tumor spheroids under glucose rich conditions of 4.5 g/L. ANOVA analysis is provided in Supplementary Tables 1–3.

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In low glucose and at the high seeding density of 20,000 cells, the MCF-7 cells experience a significant decrease in growth rate, even negative growth rates, as the seeding frequency of MDA-MB-231 cells increase. This points to the MDA-MB-231 cells potentially approaching the carrying capacity provided within these nutrient starved conditions, essentially fully depleting all the resources for other cell types. When the glucose is increased, these MCF-7 growth rates increase, suggesting there are now sufficient nutrients for both cell types (Figs. 8 and 9a,d). Upon seeding it appears that both cell types generally have higher initial growth rates when the starting frequencies of MDA-MB-231 are low.
The logistic model best describes individual cell growth
Frequency-dependent effects (here measured as competition coefficients) can only be quantified by modeling the growth of multiple interacting types or species. In order to obtain an accurate estimate of these, the mode of growth must first be determined. In this way, we use the monoculture spheroids of either MCF-7 or MDA-MB-231 alone as a data set to first understand the growth dynamics without the added complexity of the mixed cultured spheroids. Although several growth models have been proposed for spheroids, tumors, and organoids, no specific consensus has been reached34,35. Because of this, we agnostically compared four population growth models: exponential, logistic, Gompertz, and Monod-like (Table 1).
Table 1 The four models considered for estimating the mode of growth of the cancer cells based on data from monoculture spheroids seeded at a low density of 10,000 cells.
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The exponential growth model is commonly used in bacterial studies and assumes unlimited resources. Logistic population growth is the simplest constrained growth model and assumes that per capita population growth rate declines linearly with population size or density36. Although it gives a similar shape, Gompertz growth is exponentially constrained by increasing population. Several authors and works have suggested that the Gompertz equation provides a better fit to the growth of tumors in vivo37. The Monod-like equation imagines resource matching where per capita growth rates are proportional to the amount of resources supplied per individual. It provides a good fit to the population growth curves of bacteria and other single cell organisms grown in chemostats38. Here, we consider all four models when estimating the DI parameter of intrinsic growth rate (r) and the latter three models for estimating the DD parameter of carrying capacity (K).
Using constrained parameter optimization, all four models were fit to the mono-culture spheroid growth of MCF-7 or MDA-MB-231 cells under the eight environmental conditions (Supplementary Fig. 1). Due to the lower number of time points in the experiments with a high seeding density of 20,000 cells, these experiments were not used. Quality of fit for each model was evaluated using adjusted R2, Root Mean Square Error (RMSE), and Akaike Information Criterion (AIC) (Table 2, Supplementary Table 6).
Table 2 Average adjusted R2, RMSE, and AIC when fitting the four growth models to monoculture spheroids.
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Two-way ANOVAs (one for each cell type with pH and nutrient levels as independent variables) showed that adjusted R2’s were similar for the three density-dependent models and significantly lower for the exponential model (Supplementary Table 6). Conversely, the RMSE indicated the best fit for exponential growth (lowest value), lowest quality fit for the Monod-like equation, and similar, intermediate values for the logistic and Gompertz growth models. AIC analysis suggests logistic fits to be the best fit in the majority of scenarios.
While any of these four models could be used to model this system, this analysis suggests either an exponential or logistic model provides the best fit to the data. The principal difference between these two models is that logistic growth includes density-dependence and the growth curve approaches a carrying capacity, whereas exponential growth ultimately leads to extinction (if negative growth rates) or infinitely large population sizes (if positive growth rates). In this way, we evaluated whether the spheroids showed evidence of approaching or reaching a carrying capacity. To do this, we examined the slope of the final ten time points for both the red and green fluorescent values (RFU’s) for all experimental conditions (Supplementary Table 7). As most mono-culture spheroids showed declining slopes, or slopes near to zero, we favor the logistic model for this study.
Parameter estimation for Lotka–Volterra competition model
With the logistic equation showing the best fit for the mono-culture experimental data, we expanded to the Lotka–Volterra competition equations to model the mixed population tumor spheroids. The Lotka–Volterra equations represent a two species extension of logistic growth with the addition of competition coefficients.

$$begin{aligned} frac{{dN_{1} }}{dt} & = N_{1} r_{1} left( {1 – frac{{N_{1} + alpha N_{2} }}{{K_{1} }}} right) \ frac{{dN_{2} }}{dt} & = N_{2} r_{2} left( {1 – frac{{N_{2} + beta N_{1} }}{{K_{2} }}} right) \ end{aligned}$$

where Ni are population sizes, ri are intrinsic growth rates and Ki are carrying capacities. We let species 1 and 2 be MCF-7 and MDA-MB-231, respectively. The competition coefficients (α and β) scale the effects of inter-cell-type competition where α is the effect of MDA-MB-231 on the growth rate of MCF-7 in units of MCF-7, and vice-versa for β. If α (or β) is close to zero, then there is no inter-cell-type suppression; if α (or β) is close to 1 then intra-cell-type competition and inter-cell-type competition are comparable; if α (or β) is > 1 then inter-cell-type competition is severe, and if α (or β) is  (K_{MDA})/β and (K_{MCF})/α  > (K_{MDA}). Similarly, MDA-MB-231 cells are expected to outcompete MCF-7 cells if (K_{MDA})/β  > (K_{MCF}) and (K_{MDA})  > (K_{MCF})/α. Either MCF-7 or MDA-MB-231 will out-compete the other depending upon initial conditions (alternate stable states) when (K_{MCF})  > (K_{MDA})/β and (K_{MDA})  > (K_{MCF})/α. The stable coexistence of MCF-7 and MDA-MB-231 cells is expected when (K_{MDA})/β  > (K_{MCF}) and (K_{MCF})/α  > (K_{MDA}).
To estimate the values for (K_{MCF}), (K_{MDA} ,{ }) α, and β, we fit the co-culture data with low seeding density to the two-species Lotka–Volterra equation creating different fits for each combination of experimental conditions. It is important to note here that the Lotka–Volterra competition equations are not spatially explicit. From Figs. 2, 3 and 4, it can be seen that the cells are segregated within the tumor spheroid. In our application, the Lotka–Volterra competition equations simply provide a first-order linear approximation of between cell-type competition, not a mechanistic model of the consumer-resource dynamics or the spatial dynamics that likely occur in the spheroids.
Using nonlinear constrained optimization, we varied the values for instantaneous growth rates, carrying capacities, and competition coefficients α and β to minimize the RMSE between the Lotka–Volterra model and the experimental data (Fig. 10). In addition to estimating the competition coefficients, this uses the mixed culture data to re-estimate the growth rates and carrying capacities previously estimated from the monoculture spheroids as growth rate and carrying capacity depend on initial seeding density (Figs. 8 and 9).
Figure 10

(a) Estimates for growth rates, carrying capacities, and competition coefficients using nonlinear constrained optimization fitting to the Lotka–Volterra competition growth curves using the low (10,000 cell) seeding density experiments. The average of the four replicates for each experimental condition are shown along with the optimized fit to the Lotka–Volterra model. (b) Optimized parameters for growth rates (per day), carrying capacities, and competition coefficients are shown for each experimental condition. The predicted outcome of competition is given in the last column.

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As in the monoculture parameter estimation, MCF-7 had higher intrinsic growth rates and carrying capacities than MDA-MB-231 under all culture conditions. For all culture conditions, α  > 1 (as high as 12.6) and β ≈ 0 (the magnitude of six of the eight β’s was less than 0.1). Under two conditions (normal Ph and glutamine), the β’s are less than zero allowing for the possibility that MCF-7 cells are actually facilitating the per capita growth rates of the MDA-MB-231. Thus, across all treatments MDA-MB-231 cells had large competitive effects on MCF-7 growth, while the MCF-7 cells had virtually no inhibitory effects on the growth of MDA-MB-231 cells. It appears that this frequency dependent (FD) effect provides a competitive advantage for the MDA-MB-231 cells that allows them to remain in the tumor spheroids despite MCF-7’s higher intrinsic growth rates (DI) and carrying capacities (DD).
The greatest frequency dependent effect of MDA-MB-231 on MCF-7 (highest α) occurred under conditions of 4.5 g/L glucose, 6.5pH, and no glutamine. The greatest effect of MCF-7 cells on MDA-MB-231 (highest β) occurred in the absence of glucose and glutamine at neutral pH. This fits our original predictions; however, the effect of β is not strong enough to rescue MCF-7 cells from competitive exclusion. In accordance with the hypothesis that glucose permits MDA-MB-231 cells to be highly competitive, α’s (the effect of MDA-MB-231 on MCF-7) were generally lower under glucose-starved conditions. Further, both cell types appear to experience decreased growth efficiency and competition when glucose/glutamine starved, and under acidic conditions. This is consistent with more general ecological studies showing that competition between species is generally less under harsh physical conditions39,40.
Using the optimized parameters values calculated by the constrained optimization analysis of the Lotka–Volterra equations can predict the outcome of competition. This analysis suggests that under physiologic pH, regardless of glucose or glutamine concentration, MDA-MB-231 cells will outcompete the MCF-7 cells. Furthermore, under acidic pH, regardless of glucose or glutamine concentration, MDA-MB-231 cells and MCF-7 cells will actually coexist. This is observed experimentally in Figs. 2 and 3 where acidic pH results in coexistence of the two cell types and normal pH results in no MCF-7 cells remaining at the end of the experiment. While a robust result, this does not accord with our original expectations.
In vivo exploration of cell competition
MDA-MB-231 and MCF-7 cells were grown in the mammary fat pads of 8–10 week old nu/nu female mice. Three tumor models were evaluated: only MCF-7, only MDA-MB-231, and 1:1 mix of both cell types with three replicates for each (total of nine mice). Tumor volumes were measured weekly. At 5 weeks, the tumors were harvested and evaluated for ER expression which marks MCF-7 (ER positive) cells providing estimates of the ratio of MDA-MB-231 to MCF-7 cells.
H&E staining indicates that MCF-7 tumors had the greatest viability and lowest necrosis while MDA-MB-231 tumors displayed decreased viability and increased necrosis (Fig. 11a). The small differences in necrotic and viable tissue between the MDA-MB-231 and mixed tumors are indicative of the expansive effect that MDA-MB-231 phenotype has on tumor progression (Fig. 11c).
Figure 11

Results of in vivo mono- and co-culture tumors. (a) Histology slides of MCF-7, MDA-MB-231, and 1:1 co-cultured tumors. Slides were stained with H&E and ER immune stain. (b) Changes in the standardized tumor volume with time (3 mice per treatment). (c) Tumor viability for all three tumor types. Notice a decrease in viability and increase in necrosis in the MDA-MB-231 and mixed tumors. (d) Measures of CAIX, Glut1, and ER biomarker staining for each tumor type. (e) The presence of small clumps of ER staining in mixed tumors reflect the clumping of MCF-7 cells in the progression of the mixed tumor spheroids (these are enlarged photos of the indicated portions of the co-cultured tumors shown in a), suggesting the coexistence of these two phenotypes in certain tumor microenvironments. All error bars show standard error of the mean.

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Changes in tumor size over 5 weeks indicated that MCF-7 cell tumors grew more slowly than MDA-MB-231 tumors and mixed tumors (Fig. 11b; ANCOVA of natural logarithm of standardized size with day as a covariate, F1,49 = 248.0 p  More

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The following museum acronyms are used (mostly following55):
BMNH, NHM, BM: Natural History Museum, London, formerly British Museum of Natural History (UK);
CAS: California Academy of Sciences, San Francisco (USA);
MHNC: Musée d’histoire naturelle, La Chaux-de-Fonds (CH);
MHNG: Muséum d’Histoire Naturelle, Genève (CH);
MNHN: Muséum national d’Histoire naturelle, Paris (F);
SMNS: Staatliches Museum für Naturkunde, Stuttgart (D);
UWBM The Washington State Museum of Natural History and Culture/Burke Museum, University of Washington (USA);
ZMB: Museum für Naturkunde Berlin, formerly Zoologisches Museum Berlin (D).
Study organism and taxonomic background
Acanthodactylus was first described as a subgenus of Lacerta Cuvier (sic)56, the type species is A. boskianus (Daudin, 1802). The few meristic characters described to be diagnostic for Acanthodactylus56 include: collar connected in the center but free on the sides, tempora squamosal, i.e., temporal region covered by small scales rather than larger shields, ventral scales rectangular and arranged in longitudinal rows, digits acutely fimbriate-denticulate forming “toe fringes”. Fringed toes have evolved in various shapes multiple times in lizards and in Lacertidae, and when triangular, projectional or conical as in Acanthodactylus they are commonly seen as adaptation to windblown sand substrate57.
The enigmatic Acanthodactylus guineensis is among the lesser-known species of the Lacertidae. Only limited information is available regarding the species’ morphology, habitat and distribution (see Meinig & Böhme44 for a review and references therein; and 34, 38, 45, 58). Based on one young specimen, A. guineensis was described as member of the genus Eremias Fitzinger, 1834 by Boulenger40. Boulenger does not mention the slightly projecting third row of scales around the toes and fingers (“fringed toes”) of the type specimen in his quite comprehensive description40, probably because he did not notice it in such a small (SVL 24 mm) specimen and because the projection is indeed rather minor compared to other members of the genus. This is probably also the reason why he did not place guineensis in the genus Acanthodactylus Wiegmann, 1834 but in Eremias Fitzinger, 1834 (in56).
About 30 years later, Boulenger revised the genus Eremias and divided it into five sections he assumed to be natural associations59: (1) Taenieremias Boulenger, 1918—monotypic, type species Eremias guineensis Boulenger, 1887 (currently Acanthodactylus guineensis); (2) Lampreremias Boulenger, 1918—type species Eremias nitida Günther, 1872 (currently Heliobolus nitidus); (3) Pseuderemias Boettger, 1883—type species Eremias mucronata (Blandford, 1870) (currently Pseuderemias mucronata); (4) Mesalina Gray, 1838—type species Eremias rubropunctata (Lichtenstein, 1823) (currently Mesalina rubropunctata); (5) Eremias s. str. Fitzinger, 1834—type Eremias velox (Pallas, 1771) (currently Eremias velox).
Monard47 described Eremias (Taenieremias) benuensis from Cameroon based on a few minor morphological differences compared to E. guineensis, but in 1969 E. benuensis was synonymized with E. guineensis60.
Salvador17 and one year later also Arnold18 in their respective major revisions of the genus Acanthodactylus both found that Eremias (Taenieremias) guineensis agrees with all the characteristic morphological features of Acanthodactylus with the exception of the arrangement of scales around the nostril. However, it was suggested that the E. guineensis condition with an extra suture across the area occupied by the first upper labial scale to produce a smaller first upper labial is easily derived from that found in Acanthodactylus, evidenced by BMNH 1966.430, a juvenile A. erythrurus lineomaculatus (sic) with a similar scale arrangement18. Within Acanthodactylus, both authors placed guineensis in the Western clade and in the Acanthodactylus erythrurus group which was assumed to consist of A. erythrurus, A. savignyi, A. boueti, A. guineensis, and A. blanci which was considered a subspecies of A. savignyi at the time14, 20.
Molecular data and phylogenetic analyses
We aimed at reconstructing a well-supported phylogeny of the genus Acanthodactylus using whole mitochondrial DNA sequences, assembled by means of a shotgun next generation sequencing strategy. Analyses of whole mitogenomes have been shown to resolve many nodes of the lacertid tree with high statistical support (e.g.31). We retrieved muscle tissue from a museum voucher of A. guineensis (ZFMK 59511 from Daroha, near Bobo Dioulasso, Burkina Faso43) that likely was never in contact with formalin for preservation and therefore offered good chances to obtain sufficient amounts of DNA of decent quality for sequencing. We extracted genomic DNA using the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) following the protocol provided by the manufacturer. We additionally sequenced a tissue sample of a freshly caught individual of Acanthodactylus schmidti (SB 642) from Abu Dhabi (UAE) to increase sampling of Acanthodactylus spp. in the mitogenomic tree. The lizard was euthanized by injection of an aqueous solution of benzocaine (20%) into the body cavity. Subsequently, a sample of muscle tissue was taken from the thigh, preserved in 98% Ethanol and stored in − 80 °C. Handling, euthanizing and collection of tissue samples of A. schmidti individuals was approved by the NYUAD Institutional Animal Care and Use Committee (IACUC 19-0002) and UAE No Objection Certificate (NOC 8416), and all applied methods were performed in accordance with the relevant guidelines and regulations.
Genomic DNA of SB 642 was extracted from ethanol-preserved muscle tissue using the Qiagen MagAttract HMW DNA Kit (Qiagen, Hilden, Germany) for high molecular weight DNA. We determined DNA yields on a Qubit fluorometer (Qubit, London, UK) with a dsDNA high sensitivity kit. Totals of 52 ng and 80 ng of DNA (diluted in 26 µl 10 mM Tris·Cl, 0.5 mM EDTA, pH 9.0 (AE buffer)) from A. guineensis and A. schmidti, respectively, were used for library preparation.
ZFMK 59511 libraries were prepared with NEB Ultra II FS DNA (New England Biolabs, Ipswich, MA, USA) kit as per protocol instructions with input below 100 ng. For sample SB642, linked reads were generated on a 10X Genomics Chromium following Genome reagent kits v2 instructions. Resulting libraries’ concentration, size distribution and quality were assessed on a Qubit fluorometer (Qubit, London, UK) with a dsDNA high sensitivity kit and on an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) using a High Sensitivity DNA kit. Subsequently, libraries were normalized and pooled, and pools quantified with a KAPA Library quantification kit for Illumina platforms (Roche Sequencing, Pleasanton, CA, USA) on a ABI StepOnePlus qPCR machine (Thermo Fisher Scientific Inc., Waltham, MA, USA), then loaded on a SP flowcell and paired-end sequenced (2 × 150 bp) on an Illumina NovaSeq 6000 next generation sequencer (Illumina, San Diego, CA, USA), and a S2 flowcell for linked read library. Raw reads were deposited in the Sequence Read Archive (SRA) under BioProject ID PRJNA700414 . All mitogenome assemblies and original alignments were deposited in Figshare under  https://doi.org/10.6084/m9.figshare.13754083.v1 .
Raw FASTQ sequenced reads were first assessed for quality using FastQC v0.11.561. The reads where then passed through Trimmomatic v0.3662 (parameters ILLUMINACLIP: trimmomatic_adapter.fa:2:30:10 TRAILING:3 LEADING:3 SLIDINGWINDOW:4:15 MINLEN:36) for quality trimming and adapter sequence removal. Following the quality trimming, the reads were assessed again using FastQC. The executed workflows were performed using BioSAILs63.
For SB 642 (A. schmidti), we received 436,370,000 single-end reads (average read length 139.5 bases (b)) with a raw coverage of 29.29X and a scaffold N50 of 21.12 kilobases (kb). For the MITObim analysis, read number for SB 642 was randomly reduced to 1,000,000 reads (360,463,035 b) using the awk command. For ZFMK 59511 (A. guineensis), we received 57,936,345 paired-end reads (average read length 151 b, raw coverage ~ 10x). Subsequently, the two sets of reads were converted to interleaved format. We used the quality filtered reads to assemble the mitogenomes of A. guineensis and A. schmidti using an iterative mapping strategy in MITObim v. 1.9.164. We used the Acanthodactylus aureus mitogenome (GB accession number xxxxx; assembly method is provided in the next paragraph) as seed for both samples; this rendered an initial mapping of a conserved region from a more distantly related individual unnecessary64. We therefore applied the –quick option in MITObim and iterations were run until no additional reads could be incorporated into the assembly (14 in A. guineensis, eight in A. schmidti).
We also assembled complete or nearly complete mitogenomes for additional twelve species of Gallotiinae and Eremiadini using anchored hybrid enrichment sequence data from Garcia-Porta et al.6 (see Supplementary methods in6 for extraction protocol and sequencing methods and Supplementary Table S1 online in6) with the Podarcis muralis complete mitogenome (GB accession number NC_011607) as reference sequence. The raw data of each individual was quality filtered using Trimmomatic v0.3662 (parameter MINLEN:45) and assembled using MITObim v. 1.9.1. All multiple alignment files generated in the final MITObim iteration were imported to Geneious R11 (https://www.geneious.com) to check for assembly quality and coverage.
The resulting assemblies were annotated with MITOS65 using defaults settings with the vertebrata database as a reference. The annotated assemblies were imported into PhyloSuite66 together with existing mitogenomic sequences of Lacertidae, and Blanus cinereus (Amphisbaenia; outgroup) available in GenBank (as of July 2020). Details of all mitogenomic sequences included in this study and the corresponding GenBank accession numbers are provided in Supplementary Table S1 online. Using PhyloSuite, we exported all protein coding sequences plus the two rRNAs. The resulting files were aligned with MAFFT67 using “auto” settings. Alignments of coding sequences were refined with MACSE68 to account for open reading frame structure during the alignments. The protein coding and rRNAs alignments were finally concatenated into a single alignment delimiting partitions by marker and, for the protein-coding genes, by codons within markers. Mitogenome phylogenies were inferred using a maximum likelihood approach with IQ-TREE v. 1.5.4 software69 and Bayesian inference (BI) analysis with MrBayes 3.270.
For the maximum likelihood approach, best-fitting partitioning schemes and substitution models were selected based on the Akaike Information Criterion (AIC) using the heuristic algorithms implemented in the MFP + MERGE option (Supplementary Table S3 online). After ML inference, branch support was assessed with 1000 ultrafast bootstrap replicates. As the alignments used for phylogenetic inference contained more than one sequence for some species, these terminals in the resulting tree were collapsed a-posteriori for aesthetic reasons using FigTree v1.4 (as depicted in Fig. 1). An uncollapsed version of this tree is presented in Supplementary Fig. S1 online. For the BI analysis, the best-fitting partition/substitution model scheme, as selected by the ModelFinder algorithm in iQTree (Supplementary Table S4 online), was implemented with MrBayes 3.270. Results of two independent runs of 10 million generations, each comprising four Markov Chains (three heated and one cold), were sampled every 1000 generation. Chain mixing and stationery was assessed by examining the standard deviation of split frequencies and by plotting the –lnL per generation using Tracer 1.5 software71. Samples corresponding to the initial phase of the Markov chain (25%) were discarded as burn-in and the remaining results were combined to obtain a majority rule consensus tree and the respective posterior probabilities of nodes.
An additional, more comprehensive, alignment was produced containing all currently known Acanthodactylus species to obtain a higher resolution perspective on the phylogenetic placement of the target species. Since no nuclear data was available for A. guineensis, we extracted from the newly obtained sequences the four best represented mitochondrial gene fragments across Lacertidae, as compiled by Garcia-Porta et al.6, despite the known shortcomings of partial mitochondrial datasets to resolve lacertid relationships9, 72, 73. The corresponding sequences of the ribosomal RNAs (12S, 16S), COB, and NADH-dehydrogenase subunit (ND4) were later added to the alignment of6 using –add and –keeplength options in MAFFT (Supplementary Table S5 online).
Species distribution modeling
As basis for characterizing the species’ bioclimatic envelopes, we compiled an updated distribution map for A. guineensis and A. boueti including new discoveries in museum collections from the current study (see Supplementary Material online), all known records from the literature16, 34, 37, 38, 40, 42, 44, 45, 47, 58, 60, 74,75,76,77 and from the Global Biodiversity Information Facility (GBIF78). We provide all coordinates as latitude (decimal degrees), longitude (decimal degrees). Due to the general scarcity of records for A. guineensis and A. boueti, we added two localities for A. guineensis (12.5°, − 2.5°; 7.5°, 13.5°) and one locality for A. boueti (− 2.5°, 7.5°) published in Trape et al.45 that were not found on GBIF or in other publications, by extracting the center coordinates of the respective 1 × 1 degree grid. These additional localities have coordinates with very low accuracy and the corresponding environmental variables extracted from a 1 × 1 km resolution grid (30 arc-seconds; see below) therefore have high degrees of uncertainty. When examined, the majority of values fell within the total range for the respective environmental variable (the two exceptions are mentioned in the Results section), we consequently decided to keep them for our analysis. We reduced the final locality dataset to one record per km2 (the resolution of the environmental input data, see below) to avoid pseudoreplication using the R package spThin79. We then tested whether the remaining presence points are randomly dispersed or clustered using the Nearest Neighbor Index NNI in the R packages sp80 and spatialEco81. NNI was 1.4 for A. guineensis and 1.2 for A. boueti, indicating that the filtered point dataset consists of randomly distributed points which are not spatially autocorrelated.
As environmental parameters we downloaded spatial layers of the Terrestrial Ecoregions of the World (TEOW)82, global bioclimate and elevation layers39, data on potential evapotranspiration83 and global aridity index84 with a spatial resolution of 30 arc-seconds (~ 1 km2 near the equator). Although the climate datasets are interpolated from data from weather stations much farther apart from one another and are consequently not measurements of actual environmental conditions, they were rigorously cross-validated with observed data (including satellite data) during the development of the dataset and generally showed high correspondence with observations (especially temperature variables)39. Since for many applications such as our species distribution models data at high spatial resolution (i.e., 1 × 1 km) are preferable over lower resolution to capture variation for example across steep climate gradients in mountains39 we chose the 30 arc-seconds dataset over the 5 arc-minutes dataset (~ 9 × 9 km near the equator) for the ecoregions and climate datasets. In addition, we downloaded spatial layers with forest and grassland/scrub/woodland cover from the harmonized soil database (only available in 5 arc-minutes spatial resolution85). We downscaled the forest and grassland/scrub/woodland dataset to 30 arc-seconds resolution despite the resulting slight inaccuracy, which we kept in mind during interpretation but did not consider relevant for the vegetation datasets.
In order to compare the prevailing climate within the distribution range of A. guineensis and A. boueti to all other species of the genus Acanthodactylus we downloaded all available records of the other currently known Acanthodactylus species from GBIF. We cleaned the downloaded datasheet by deleting all entries with missing data for either latitude, longitude, species epithet, or all of those, and removed duplicates or spelling mistakes. We further removed all GBIF entries with imprecise coordinates, i.e. records with coordinates that when plotted landed just outside of coastal areas in the ocean instead of on land (usually coordinates with only two decimals). In addition, we added several curated locality data from Garcia-Porta et al.6 as available from Figshare (https://doi.org/10.6084/m9.figshare.8866271.v1/). Following Tamar et al.14 we treated A. lineomaculatus as a junior synonym of A. erythrurus and changed the respective records in our database accordingly. For species that did not have records published on GBIF we added further locality data from the literature (Supplementary Table S6 online). The final database contained 4286 records from all 44 currently recognized species of Acanthodactylus. We acknowledge that our database is by no means complete, however, we wanted to follow the most conservative approach and use only records that are supported by a voucher specimen and can be traced back using published databases. We trust that our record list covers a nearly complete representation of the ecological conditions inhabited by all currently accepted Acanthodactylus species.
Using the bioclim dataset39 we plotted annual mean temperature (bio1) and annual precipitation (bio12) for all locality records in our Acanthodactylus species database and compared the respective data for A. guineensis and A. boueti with its congeners. We developed species distribution models using Maxent 3.4.146 for A. guineensis and A. boueti. Maxent applies the maximum entropy principle86 for model fitting under the basic premise that the estimated species distribution deviates from a uniform distribution as minimally as required to explain the observations46. We used the cleaned datasets of A. guineensis and A. boueti with only one record per km2 as input presence locality data. We extracted environmental data for all presence sites from the 19 bioclim variables (bio1-19), elevation (alt), aridity index (AI), percentage cover of forest (forest) and grassland, scrub and woodland (grass) per grid cell, as well as four parameters comprising potential evapotranspiration (PET): PET of the wettest, driest, warmest and coldest quarter of the year (PETwet, PETdry, PETwarm, PETcold). To avoid issues resulting from correlated parameters we reduced the number of environmental predictors by performing multicollinearity analyses using the Variance Inflation Factor (VIF) with a threshold  More