Ecology

Conceptualization of organismal to tumor biogeography through clone phylogeniesWe first map organismal biogeographic concepts and models to the process of migration and colonization of cancer cells during metastasis. Tumors are populations consisting of a diversity of cancer cells with different genetic profiles that may represent different lineages in the clone phylogeny. We use the example in Fig. 1, which contains a phylogeny of 17 clones found in one primary tumor (P) and four metastases (M1–M4). Events occurring along a branch in a phylogeny are anagenetic events, which include diversification, extinction, and expansion12,14. In organismal evolutionary biology, anagenetic events are not directly observed except through the fossil record. However, one can map the collection of genetic variants that likely arose on individual lineage in a phylogeny. In many cancers, sequencing of temporally sampled biopsies’ can directly reveal anagenetic events similar to the sequencing of ancient DNA in paleogenomics.The other types of evolutionary events in the phylogeny are cladogenetic, including genetic divergences and dispersals (Fig. 1). Genetic differences observed among species and populations are the key to detect cladogenetic events reconstructed in molecular phylogenies of living descendants. In cancer, temporal sampling of biopsies can reveal cladogenetic events that produced extinct descendants.In biogeography, genetic divergence results in the diversification of lineages within an area. Sometimes, the term duplication is used, but we avoid its further use because of the confusion it may cause in evolutionary genomics. Divergence events are also observed in a clone phylogeny, particularly when clone lineages diverge from each other within a tumor or across tumors. The exact opposite of genetic diversification can also be observed when lineages partially or fully disappear from the phylogeny. Extinction can occur due to random chance, selection, or environmental pressures. Even though extinction is rarely discussed in tumor clone phylogenetics, it happens frequently.Phylogenies also reveal movements of lineages between locations (geographic areas or body parts) when the locations of individual cells, species, or populations are known5,6,7,8,9,15. When lineages accumulate genetic differences along a branch in the phylogeny, and the evolved lineages migrate to a new area, we observe an expansion event. Expansions differ from dispersals in such that the growth of a population occurs in the same place. This movement of cells of a clone from one location to another, where they would potentially form a metastasis, results in the dispersal of  these cells of that clone to additional areas, which is modeled by a dispersal rate (d) in organismal biogeography. When a clone genetically diverges following its migration, then a distant dispersal event is said to have occurred. Similarly, when a clone diverges from the rest of the clones within a tumor and disperses to another tumor, we have observed an expansion event. Thus, clone phylogenies can give insights into the origin and trajectory of cancer cells between tumors.When a clone is no longer present at a location, it is extinct at that location. Extinctions are modeled by an extinction rate (e) in biogeographic models. As a result of extinction, the range of descendent clones on a phylogeny can be smaller than the ancestors. Biogeography models also have a parameter (J) to consider founder events that establish new populations from a few individuals. In phylogenies, founder events can be detected if only one or a few cells are found to have moved from one location to another to start diversifying in a new area. Both distant dispersal and founder events may result in forming a new colony of cells, i.e., a new metastasis in the case of cancer cell migrations. The primary distinction between dispersal and founder events is the relative number of migrating cells. Founder events are due to one or a few cells, whereas dispersal events involve a larger number of migrating cells. Founder events are expected to be more common in tumor evolution because metastases are thought to be formed by the spread of only one or a few cancer cells. These biogeographic events have been mathematically modeled and implemented in various approaches to infer species migration events12, which are directly applicable in the inference of cancer cell migrations between tumors.Model fitsWe began by analyzing the statistical fits of six biogeographic models (Table 1) to 80 computer-simulated tumor evolutionary datasets. Simulations enable us to assess the performance of computational approaches and reveal potential caveats associated with their use because the ground truth is known. These datasets were simulated using four main clone migration schemes defined by the different number of migrating clones (1–3), the small and large number of tumor areas (5–7 tumors, m5 datasets; 8–11 tumors, m8 datasets), and the different types of source areas of migration (primary or metastasis). The following seeding scenarios reflect this complexity of the clone migration schemes: monoclonal single-source seeding (mS), polyclonal single-source seeding (pS), polyclonal multisource seeding (pM), and polyclonal reseeding (pR) (see “Methods” section).Table 1 Phylogenetic and biogeographic events considered in seven biogeographic models used for analysis.Full size tableWe considered biogeographic models that weigh genetic divergence, dispersal/expansion, and extinction events differently (Table 1). We also explored the provision of including founder events in our models on the accuracy of detecting clone migrations. The parameterization of the aforementioned events results in models with two free parameters, i.e., dispersal rate (d) and extinction rate (e), and models with three free parameters by adding the founder-event speciation (J); see “Methods” section for more details.Overall, we tested six biogeographic models for their fit to the tumor data, three models with two free parameters and three others with three free parameters. BAYAREALIKE, DEC, and DIVALIKE models have two parameters each. They are nested within their respective models that add the founder effect, resulting in a model with three free parameters (hereinafter +J models). We used the BioGeoBEARS software for all model fit analyses. In data analysis, we first inferred phylogeny of cancer cell populations (clone phylogeny) using an existing method16, followed by the use of BioGeoBEARS to infer the clone migration history in which the clone phylogeny is used along with the location of tumor sites in which each clone is observed (Fig. 2). BioGeoBEARS estimates the probabilities of annotating internal nodes with tumor locations. These annotations are then used to derive cancer cell migration paths when two adjacent nodes are annotated with different tumor locations. In these analyses, we assumed the correct clone phylogeny because our focus was not assessing the impact of errors in a phylogeny on the accuracy of clone migration inferences. We also compared the accuracy of migration histories reconstructed using biogeographic models in BioGeoBEARS with those obtained from the approaches that do not model biogeographic processes (BBM9, MACHINA5, and PathFinder7).Figure 2Data analysis pipeline using BioGeoBEARS in R14 to infer clonal migration histories.Full size imageWe first conducted Likelihood Ratio Tests (LRTs) to examine the improvement offered by considering founder events in modeling tumor migrations. In this case, the fit of the BAYAREALIKE, DEC, and DIVALIKE models was compared to their +J counterparts, respectively. The null hypothesis was rejected for more than 50% of the datasets (BAYAREALIKE: 71.25%, DEC: 60%, and DIVALIKE: 53.75%; P  More

The Fur-regulated T6SS1 plays an important role in iron acquisition in C. necator
To explore the function of T6SS1 (Reut_A1713 to Reut_A1733) in C. necator (Fig. S1A), we analyzed the T6SS1 promoter and identified a Fur binding site (AGAAATA) upstream of gene reut_A1733. This Fur binding site was highly similar to the Fur-box reported in E. coli [38], with a probability score of 2.25 (out of a maximum score = 2.45) (Fig. S1B), which was calculated by applying the position weight matrix to a sequence [39]. Incubation of the T6SS1 promoter probe with purified Fur protein led to decreased mobility of the probe in the electrophoretic mobility shift assay, suggesting a direct interaction between Fur and the T6SS1 promoter (Fig. 1A). To further determine the function of Fur on the expression of T6SS1, a single-copy PT6SS1::lacZ fusion reporter was introduced into the chromosomes of C. necator wild-type (WT), Δfur deletion mutant, and the Δfur(fur) complementary strain. Compared to WT, the PT6SS1::lacZ promoter activity was significantly increased in the Δfur mutant (about 2.2-fold), and this increase could be restored by introducing the complementary plasmid pBBR1MCS-5-fur (Fig. 1B). Similar results were obtained by analyzing the expression of T6SS1 core component genes (hcp1, clpV1, vgrG1, and tssM1) with qRT-PCR (Fig. S1C). These results demonstrate that the expression of T6SS1 in C. necator is directly repressed by Fur, the master regulator of genes involved in iron homeostasis in many prokaryotes [40, 41].Fig. 1: Regulation of T6SS1 expression by Fur.A The interactions between His6-Fur and the T6SS1 promoter examined by EMSA. Increasing amounts of Fur (0, 0.03, 0.06, 0.13, 0.25, and 1.0 μM) and 10 nM DNA fragments were used in the assay. A 500 bp unrelated DNA fragment (Control A) and 1 µM BSA (Control B) were included in the assay as negative controls. B Fur represses the expression of T6SS1. β-galactosidase activities of T6SS1 promoter from chromosomal lacZ fusions in relevant C. necator strains were measured. C Iron uptake requires T6SS1. Stationary-phase C. necator strains were washed twice with M9 medium. Iron associated with indicated bacterial cells were measured with ICP-MS. The vector corresponds to the plasmid pBBR1MCS-5 (B) and pBBR1MCS-2 (C), respectively. Data are represented as mean values ± SD of three biological replicates, each with three technical replicates. **p  More

AffiliationsAnimal Production Systems group, Wageningen University & Research, Wageningen, The NetherlandsAbigail Muscat, Evelien M. de Olde, Raimon Ripoll-Bosch & Imke J. M. de BoerFarming Systems Ecology group, Wageningen University & Research, Wageningen, The NetherlandsHannah H. E. Van ZantenPublic Administration and Policy group, Wageningen University & Research, Wageningen, The NetherlandsTamara A. P. Metze & Catrien J. A. M. TermeerPlant Production Systems group, Wageningen University & Research, Wageningen, The NetherlandsMartin K. van IttersumAuthorsAbigail MuscatEvelien M. de OldeRaimon Ripoll-BoschHannah H. E. Van ZantenTamara A. P. MetzeCatrien J. A. M. TermeerMartin K. van IttersumImke J. M. de BoerCorresponding authorCorrespondence to
Imke J. M. de Boer. More

Late Quaternary climate reconstructionsPrecipitationOur reconstructions of Late Quaternary precipitation are based on outputs from a statistical emulator of the HadCM3 general circulation model62. The emulator was developed using 72 3.75° × 2.5° resolution snapshot climate simulations of HadCM3, covering the last 120k years and in 2k year time steps from 120k to 22k years ago and 1k-year time steps from 21k years ago to the present, where each time slice represents climatic conditions averaged across a 30-year post-spin-up period28,63. The emulator is based on grid-cell-specific linear regressions between the local time series of HadCM3 climate data and four time-dependent forcings, given by the mean global atmospheric CO2 concentration and three orbital parameters: eccentricity, obliquity, and precession. The values of these four predictors are known well beyond the last 120k years; thus, applying them to the calibrated grid-cell-specific linear regressions allows for the statistical extrapolation of global climate up to 800k years into the past62. The emulated climate data have been shown to correspond closely to the original HadCM3 simulations for the last 120k years, and to match long-term empirical climate reconstructions well62.Here, we used precipitation data from the emulator, denoted ({bar{{{{bf{P}}}}}}_{{{{{rm{HadCM3}}}}}_{{{{rm{em}}}}}}(t)), of the last 300k years at 1k-year time steps, (tin {{{{bf{T}}}}}_{300k}). The data were spatially downscaled from their native 3.75° × 2.5° grid resolution, and subsequently bias-corrected, in two steps, similar to the approach described in ref. 64, whose description we follow here. Both steps use variations of the delta method65, under which a high-resolution, bias-corrected reconstruction of precipitation at sometime t is obtained by applying the difference between lower-resolution present-day simulated and high-resolution present-day observed climate—the correction term—to the simulated climate at time t. The delta method has been used to downscale and bias-correct palaeoclimate simulations before (e.g. for the WorldClim database66), and, despite its conceptual simplicity, has been shown to outperform alternative methods commonly used for downscaling and bias-correction67.A key limitation of the delta method is that it assumes the present-day correction term to be representative of past correction terms. This assumption is substantially relaxed in the dynamic delta method used in the first step of our approach to downscale ({bar{{{{bf{P}}}}}}_{{{{rm{HadCM}}}}{3}_{{{{rm{em}}}}}}(t)) to a ~1° resolution. This method involves the use of a set of high-resolution climate simulations that were run for a smaller but climatically diverse subset of T300k. Simulations at this resolution are computationally very expensive, and therefore running substantially larger sets of simulations is not feasible; however, these selected data can be very effectively used to generate a suitable time-dependent correction term for each (tin {{{{bf{T}}}}}_{300k}). In this way, we can increase the resolution of the original climate simulations by a factor of ∼9, while simultaneously allowing for the temporal variability of the correction term. In the following, we describe the approach in detail.We used high-resolution precipitation simulations from the HadAM3H model63, generated for the last 21,000 years in 9 snapshots (2k year time intervals from 12k to 6k years ago, and 3k year time intervals otherwise) at a 1.25° × .83° grid resolution, denoted ({bar{{{{bf{P}}}}}}_{{{{rm{HadAM}}}}3{{{rm{H}}}}}(t)), where (tin {{{{bf{T}}}}}_{21k},)represents the nine time slices for which simulations are available. These data were used to downscale ({bar{{{{bf{P}}}}}}_{{{{rm{HadCM}}}}{3}_{{{{rm{em}}}}}}(t)) to a 1.25° × 0.83° resolution by means of the multiplicative dynamic delta method, yielding$${bar{{{{bf{P}}}}}}_{ sim 1^circ }(t)mathop{=}limits^{{{{rm{def}}}}}{bar{{{{bf{P}}}}}}_{{{{rm{HadCM}}}}{3}_{{{{rm{em}}}}}}^{ boxplus }(t)cdot frac{{bar{{{{bf{P}}}}}}_{{{{rm{HadAM}}}}3{{{rm{H}}}}}(hat{t})}{{bar{{{{bf{P}}}}}}_{{{{rm{HadCM}}}}{3}_{{{{rm{em}}}}}}^{ boxplus }(hat{t})}.$$
(1)
The ⊞-notation indicates that the coarser-resolution data were interpolated to the grid of the higher-resolution data, for which we used an Akima cubic Hermite interpolant68, which, unlike the bilinear interpolation, is continuously differentiable but, unlike the bicubic interpolation, avoids overshoots. The time (hat{t}in {{{{bf{T}}}}}_{21k}) is chosen as the time at which climate was, in a sense specified below, close to that at time (tin {{{{bf{T}}}}}_{300k}). In contrast to the classical delta method (for which (hat{t}=0) for all (t)), this approach does not assume that the resolution correction term, ((frac{{bar{{{{bf{P}}}}}}_{{{{rm{HadAM}}}}3{{{rm{H}}}}}(hat{t})}{{bar{{{{bf{P}}}}}}_{{{{rm{HadCM}}}}{3}_{{{{rm{em}}}}}}^{ boxplus }(hat{t})})), is constant over time. Instead, the finescale heterogeneities that are applied to the coarser-resolution ({bar{{{{bf{P}}}}}}_{{{{rm{HadCM}}}}{3}_{{{{rm{em}}}}}}(t)) are chosen from the wide range of patterns simulated for the last 21k years. The strength of the approach lies in the fact that the last 21k years account for a substantial portion of the glacial-interglacial range of climatic conditions present during the whole Late Quaternary. Following ref. 64, we used global CO2, a key indicator of the global climatic state, as the metric according to which (hat{t}) is chosen; i.e. among the times for which HadAM3H simulations are available, (hat{t}) is the time at which global CO2 was closest to the respective value at the time of interest, t.In the second step of our approach, we used the classical multiplicative delta method to bias-correct and further downscale ({{{{bf{P}}}}}_{ sim 1^circ }(t)) to a hexagonal grid69 with an internode spacing of ~55 km (~0.5°),$${bar{{{{bf{P}}}}}}_{ sim 0.5^circ }(t)mathop{=}limits^{{{{rm{def}}}}}{bar{{{{bf{P}}}}}}_{ sim 1^circ }^{ boxplus }(t)cdot frac{{bar{{{{bf{P}}}}}}_{{{{rm{obs}}}}}(0)}{{bar{{{{bf{P}}}}}}_{ sim 1^circ }^{ boxplus }(0)},$$
(2)
where ({{{{bf{P}}}}}_{{{{rm{obs}}}}}(0)) denotes present-era (1960–1990) observed precipitation70.We reconstructed land configurations for the last 300k years using present-day elevation71 and a time series of Red Sea sea level72. For locations that are currently below sea level, the delta method does not work. For these locations, precipitation was extrapolated using a inverse distance weighting approach. With the exception of a brief window from 124–126k years ago, sea level in the past was lower than it is today; thus, present-day coastal patterns are spatially extended as coastlines move, but not removed. For all (tin {{{{bf{T}}}}}_{300k}), maps of annual precipitation ({bar{{{{bf{P}}}}}}_{ sim 0.5^circ }(t)) with the appropriate land configuration are available as Supplementary Movie 1.Based on these data representing 30-year climatological normals at 1k-year time steps between 300k years ago and the present, we generated, for each millennium, 100 maps representing 10-year average climatologies as follows. We used 3.75° × 2.5° climate simulations from the HadCM3B-M2.1 model, providing a 1000-years-long annual time series of annual precipitation for each millennium between 21k years ago and the present73. Millennia were simulated in parallel; thus, the 1000-years-long time series representing each millennium is in itself continuous, but the beginnings and ends of the time series of successive millennia generally do not coincide. For (tin {{{{bf{T}}}}}_{21k}), we denote the available 1000 successive maps of annual precipitation by ({{{{bf{P}}}}}_{{{{rm{HM}}}}}^{(1)}(t),ldots ,{{{{bf{P}}}}}_{{{{rm{HM}}}}}^{(1000)}(t)). We used these data to compute the relative deviation of the climatic average of each decade within a given millennium, and the climatic average of the 30-year period containing the specific decade as$${{{{boldsymbol{epsilon }}}}}_{HM}^{(d)}(t)mathop{=}limits^{{{{rm{def}}}}}frac{{sum }_{i=1+(d-1)cdot 10}^{dcdot 10}{{{{bf{P}}}}}_{{{{rm{HM}}}}}^{(i)}(t)}{{sum }_{n=1+(d-2)cdot 10}^{(d+1)cdot 10}{{{{bf{P}}}}}_{{{{rm{HM}}}}}^{(n)}(t)},d=1,ldots ,100$$
(3)
Finally, we applied these ratios of 10-year to 30-year climatic averages to the previously derived 1k-year time step climatologies to obtain, for each (tin ,{{{{bf{T}}}}}_{300k}), 100 sets of 10-year average annual precipitation,$${{{{bf{P}}}}}_{ sim 0.5^circ }^{(d)}(t)mathop{=}limits^{{{{rm{def}}}}}{bar{{{{bf{P}}}}}}_{ sim 0.5^circ }(t)cdot {{{{boldsymbol{epsilon }}}}}_{HM}^{(d), boxplus }(hat{t}),,d=1,ldots ,100$$
(4)
where, analogous to our approach in Eq. (1),(, boxplus ) denotes the interpolation to the ~55 km hexagonal grid, and where (hat{t}) is chosen as the time at which global CO2 was closest to the respective value at time t.AridityThe Köppen aridity index used here is defined as the ratio of annual precipitation (in mm) to the sum of mean annual temperature (in °C) and a constant of 33 °C (cf. Eq. (8)). This measure of aridity was found to be the most reliable one of a set of alternative indices in palaeoclimate contexts30.Decadal-scale mean annual temperature data between 300k years ago and the present were created using analogous methods to those previously applied to reconstruct precipitation. 3.75° × 2.5° resolution emulator-derived simulations of mean annual temperature of the past 300k years at 1k time steps62, denoted ({bar{{{{bf{T}}}}}}_{{{{rm{HadCM}}}}{3}_{{{{rm{em}}}}}}(t)), were first downscaled by means of the additive dynamic delta method, using 1.25° × 0.83° HadAM3H simulations of mean annual temperature of the past 21k years, denoted ({bar{{{{bf{T}}}}}}_{{{{rm{HadAM}}}}3{{{rm{H}}}}}(t)), yielding, analogous to Eq. (1),$${bar{{{{bf{T}}}}}}_{ sim 1^circ }(t)mathop{=}limits^{{{{rm{def}}}}}{bar{{{{bf{T}}}}}}_{{{{rm{HadCM}}}}{3}_{{{{rm{em}}}}}}^{ boxplus }(t)+left({bar{{{{bf{T}}}}}}_{{{{rm{HadAM}}}}3{{{rm{H}}}}}(hat{t})-{bar{{{{bf{T}}}}}}_{{{{rm{HadCM}}}}{3}_{{{{rm{em}}}}}}^{ boxplus }(hat{t})right).$$
(5)
Analogous to Eq. (2), Next, present-day observed mean annual temperature, ({bar{{{{bf{T}}}}}}_{{{{rm{obs}}}}}(0)), was used to further downscale and bias-correct the data by means of the additive delta method to obtain$${bar{{{{bf{T}}}}}}_{ sim 0.5^circ }(t)mathop{=}limits^{{{{rm{def}}}}}{bar{{{{bf{T}}}}}}_{ sim 1^circ }^{ boxplus }(t)+left({bar{{{{bf{T}}}}}}_{{{{rm{obs}}}}}(0)-{bar{{{{bf{T}}}}}}_{ sim 1^circ }^{ boxplus }(hat{t})right).$$
(6)
For all (tin {{{{bf{T}}}}}_{300k}), maps of mean annual temperature ({bar{{{{bf{T}}}}}}_{ sim 0.5^circ }(t)) with the appropriate land configuration are available as Supplementary Movie 1.Finally, we incorporated HadCM3B-M2 simulations of mean annual temperature of the past 21k years, ({{{{bf{T}}}}}_{{{{rm{HM}}}}}^{(1)}(t),ldots ,{{{{bf{T}}}}}_{{{{rm{HM}}}}}^{(1000)}(t)) for (tin {{{{bf{T}}}}}_{21k}), to obtain 10-year average mean annual temperature,$$begin{array}{c}{{{{bf{T}}}}}_{ sim 0.5^circ }^{(d)}(t)mathop{=}limits^{{{{rm{def}}}}}{bar{{{{bf{T}}}}}}_{ sim 0.5^circ }(t)+{left(mathop{sum }limits_{i=1+(d-1)cdot 10}^{dcdot 10}{bar{{{{bf{T}}}}}}_{{{{rm{HM}}}}}^{(i)}(t)-mathop{sum }limits_{n=1+(d-2)cdot 10}^{(d+1)cdot 10}{bar{{{{bf{T}}}}}}_{{{{rm{HM}}}}}^{(n)}(t)right)}^{ boxplus },\ d=1,ldots ,100end{array}$$
(7)
Based on these data, the Köppen aridity index at the same spatial and temporal resolution is calculated as$${{{{bf{A}}}}}_{ sim 0.5^circ }^{(d)}(t)mathop{=}limits^{{{{rm{def}}}}}frac{{{{{bf{P}}}}}_{ sim 0.5^circ }^{(d)}(t)}{{{{{bf{T}}}}}_{ sim 0.5^circ }^{(d)}(t)+33}.$$
(8)
Comparison with empirical proxiesLong-term proxy records
Long-term proxy records allow us to assess whether simulations capture key qualitative dynamics observed in the empirical data. The lack of direct long-term time series reconstructions of annual precipitation and mean annual temperature makes it necessary to use proxies related to these two climate variables. Proxies providing temporal coverage beyond the last glacial maximum are not only extremely sparse in North Africa and Southwest Asia, but even the few records that exist are affected by environmental factors other than the specific climate variables considered here. For example, reconstructions of past wetness and aridity use proxies that reflect not only rainfall conditions but also the interaction of precipitation with other local and non-local hydro-climatic variables, e.g. river discharge or hydrological catchment across a larger area. Here, we have not attempted to correct for such processes, but assumed that the simulated climate at the site where the empirical record was taken provide a suitable approximation of the potentially broader climatic conditions relevant for the proxy data. Realistic climate simulations would therefore be expected to match major qualitative trends of the empirical records, rather than exhibit a perfect correlation with the data. We compared our precipitation simulations against three long-term humidity-related empirical proxies (Fig. 4a). Proxy 174 provides a time series of Dead Sea lake levels, for which wet and dry periods are associated with high-stand and low-stand conditions, respectively. Proxy 219 from the southern tip of the Arabian Peninsula was obtained from a marine sediment core that allows for reconstructing past changes in aridity over land from the stable hydrogen isotopic composition of leaf waxes (δDwax). Proxy 318 is an XRF-derived humidity index from a core near the Northwest African coast. Temperature simulations were compared against two long-term records of δ18O, which varies over time as a result of temperature fluctuations (in addition to other factors), from the Peqiin and Soreq caves in Northern Israel75 (Fig. 4e). Overall, the simulated data capture key phases observed in the empirical records well for both precipitation (Fig. 4b–d) and temperature proxies (Fig. 4f–h).Fig. 4: Comparison of our data to long-term proxy records.a Geographical locations of empirical proxies on a map of present-day annual precipitation. b–d Comparisons of simulated annual precipitation against the three wetness proxies. e Geographical locations of empirical proxies on a map of present-day mean annual temperature. f, g Comparisons of simulated mean annual temperature against the two δ18O records. Black lines represent the simulated climatological normals at 1k-year intervals (Eqs. (2) and (6)), grey shades represent the 10th and 90th percentile of the decadal simulations (n = 100; Eqs. (4) and (7)).Full size image

Pollen-based reconstructions
Pollen records used to empirically reconstruct past climate do not reach as far back in time as the above-described proxy records and are not available at the same temporal resolution; however, in contrast to those proxies, they can be used to quantitatively estimate local annual precipitation and mean annual temperature directly. Here, we used the dataset of pollen-based reconstructions of precipitation and temperature for the mid-Holocene (6k years ago) and the last glacial maximum (21k years ago)76 (Fig. 5a). Our precipitation and temperature data are overall in good agreement with the empirical reconstructions (Fig. 5b–e). During the mid-Holocene, our simulations suggest slightly less precipitation at low levels than most of the empirical records (Fig. 5d), while our data match the empirical reconstruction available from a very arid location during the last glacial maximum very well (Fig. 5e).Fig. 5: Comparison of our data to pollen-based climate reconstructions from the mid-Holocene and the last interglacial period.a Geographical locations and timings of pollen records. b–e Comparisons of our data against empirical reconstructions. Vertical centre measures and error bars represent the empirical reconstructed values and their uncertainties, respectively; horizontal centre measures and error bars represent simulated climatological normals at 1k-year intervals (Eqs. (2) and (6)) and the 10th and 90th percentile of the simulated decadal data (n = 100; Eqs. (4) and (7)), respectively.Full size image

Interglacial palaeolakes on the Arabian Peninsula
Finally, we plotted time series of our precipitation simulations in three locations in which palaeolakes have been dated to the last interglacial period, following the approach in ref. 24, in which the authors tested whether their climate simulations predicted higher rainfall during the last interglacial period than at present at palaeolake sites on the Arabian Peninsula. Figure 6 shows the locations of three palaeolakes in the northeast (western Nefud near Taymal; proxy 1), the centre (at Khujaymah; proxy 2), and the southwest (at Saiwan; proxy 3) of the peninsula24 (described in detail in refs. 23,77), and our precipitation data in these locations. In two out of the three locations, our data predict that more rainfall occurred at the estimated timings of the palaeolakes than at any point in time since; in the third location, slightly more rainfall than during the dated time interval is simulated only for a period around 8k years ago.Fig. 6: Comparison of our data against the dates of three palaeolakes on the Arabian peninusla.a Geographical locations of the lakes. b–d Time series of our precipitation data. Black lines represent the simulated climatological normals at 1k-year intervals (Eqs. (2) and (6)), grey shades represent the 10th and 90th percentile of the decadal simulations (n = 100; Eq. (4)). Horizontal error bars represent the estimated dates of the lakes24.Full size image
Determining the minimum precipitation and aridity tolerance required for out-of-Africa exitsWe denote by ({{{bf{X}}}}={({lambda }_{1},{phi }_{1}),({lambda }_{2},{phi }_{2}),ldots },)the set of longitude and latitude coordinates of the hexagonal grid with an internode spacing of ~55 km (~0.5°)69 that are contained in the longitude window [15°E, 70°E] and the latitude window [5°N, 43°N] (shown in Fig. 3). We denote by ({{{bf{E}}}}) the set of the present-day elevation values of the coordinates in ({{{bf{X}}}}) (in meters)78, i.e. ({{{bf{E}}}}({x}_{i})) is a positive number in a point ({x}_{i}=({lambda }_{i},{phi }_{i})) if ({x}_{i}) is currently above sea level, and negative if ({x}_{i}) is currently below sea level. We denote by (s(t)) the sea level (in meters) at the time (tin {{{{bf{T}}}}}_{300k}) (where ({{{{bf{T}}}}}_{300k}) represents the last 300k years in 1k time steps), for which we used a long-term reconstruction of Red Sea sea level72. In particular, we have (s(0)=0) at present day. For each millennium (tin {{{{bf{T}}}}}_{300k}), we denote by (bar{{{{bf{X}}}}}(t)) the subset of points in (X) that are above sea level:$$bar{{{{bf{X}}}}}(t)mathop{=}limits^{{{{rm{def}}}}}{xin {{{bf{X}}}}:{{{bf{E}}}}(x), > , s(t)}$$
(9)
Based on the precipitation map ({{{{bf{P}}}}}_{ sim 0.5^circ }^{(d)}(t)) for a decade (d=1,ldots ,100) in millennium (t) (Eq. (4)), and a given precipitation threshold value (p) (in mm year−1), we denote by ({mathop{{{{bf{X}}}}}limits^{=}}_{p}^{(d)}(t)) the subset of (bar{{{{bf{X}}}}}(t)) that would be suitable grid cells for humans assuming that they cannot survive in areas where precipitation levels are below (p):$${mathop{{{{bf{X}}}}}limits^{=}}_{p}^{(d)}(t)mathop{=}limits^{{{{rm{def}}}}}left{xin bar{{{{bf{X}}}}}(t):{{{{bf{P}}}}}_{ sim 0.5^circ }^{(d)}(t)ge pright}$$
(10)
We then determined whether there was a connected path in ({mathop{{{{bf{X}}}}}limits^{=}}_{p}^{(d)}(t)) between an initial point, for which we used ({x}_{{{{rm{start}}}}}=(32.6^circ {{{rm{E}}}},10.2^circ {{{rm{N}}}})), and any point in a set of coordinates outside of Africa, defined as ({{{{bf{X}}}}}_{{{{rm{end}}}}}mathop{=}limits^{{{{rm{def}}}}}{(lambda ,phi )in {{{bf{X}}}}:lambda, > , 65^circ {{{rm{E}}}},{{{rm{or}}}},phi , > , 37^circ N}). This was defined to be the case if there was a finite sequence$${x}_{{{{rm{start}}}}}to {x}_{1}to {x}_{2}to ldots to {x}_{n}in {{{{bf{X}}}}}_{{{{rm{end}}}}}$$
(11)
of points ({x}_{i}in {mathop{{{{bf{X}}}}}limits^{=}}_{p}^{(d)}(t)) such that the distance between any two successive points ({x}_{i}) and ({x}_{i+1}) was less or equal to the maximum internode spacing of the grid (X). Based on this approach, the critical precipitation threshold below which no connected path exists for the precipitation map ({{{{bf{P}}}}}_{ sim 0.5^circ }^{(d)}(t)) was determined using the following bisection method. Beginning with ({hat{p}}_{0}=1000) mm y−1 and ({check{p}}_{0}=0) mm y−1, for which a connected path between ({x}_{{{{rm{start}}}}}) and ({{{{bf{X}}}}}_{{{{rm{end}}}}}) exists, respectively, for all and for no (t) and (d), the values ({hat{p}}_{k}) and ({check{p}}_{k}) were iteratively defined as$$ , left.begin{array}{c}{check{p}}_{k+1}mathop{=}limits^{{{{rm{def}}}}}frac{{{hat{p}}_{k}+{check{p}}_{k}}}{2}\ {hat{p}}_{k+1}mathop{=}limits^{{{{rm{def}}}}}{hat{p}}_{k}hfillend{array}right},{{{rm{if}}}},{{{rm{a}}}},{{{rm{connected}}}},{{{rm{path}}}},{{{rm{exists}}}},{{{rm{for}}}},p=frac{{{hat{p}}_{k}+{check{p}}_{k}}}{2}\ , left.begin{array}{c}{check{p}}_{k+1}mathop{=}limits^{{{{rm{def}}}}}{check{p}}_{k}hfill\ {hat{p}}_{k+1}mathop{=}limits^{{{{rm{def}}}}}frac{{{hat{p}}_{k}+{check{p}}_{k}}}{2}end{array}right}, {{{rm{else}}}}$$
(12)
For all (k), the sought critical precipitation threshold, denoted ({p}_{{{{rm{crit}}}}}^{(d)}(t)), is bounded above by ({hat{p}}_{k}) and bounded below by ({check{p}}_{k}). For (kto infty ), both values converge to ({p}_{{{{rm{crit}}}}}^{(d)}(t)). Here, we defined$${p}_{{{{rm{crit}}}}}^{(d)}(t)mathop{=}limits^{{{{rm{def}}}}}frac{,{hat{p}}_{10}+{check{p}}_{10}}{2},$$
(13)
which lies within 1 mm y−1 of the true limit value.To specifically determine the precipitation tolerance required for a northern (Fig. 1a) or southern (Fig. 1b) exit, we rendered the passage of the respective other route impassable by removing appropriate cells from the grid. When investigating the southern route, we additionally assumed that no sea level and precipitation constraints applied within a ~40 km radius around the centre of the Bab al-Mandab strait.For aridity, the procedure is identical, with the exception that ({mathop{{{{bf{X}}}}}limits^{=}}_{p}^{(d)}(t)) is defined based on the relevant aridity map, ({{{{bf{A}}}}}_{ sim 0.5^circ }^{(d)}(t)), and the value 4.0 is used for the initial upper threshold (denoted ({hat{p}}_{0}) above).Width of the Strait of Bab al-MandabSimilar to ref. 52, we reconstructed the minimum distance required to cover on water in order to reach the Arabian peninsula (present-day west coast of Yemen) from Africa (present-day Djibouti and southeast Eritrea). We used a 0.0083° (~1 km at the equator) map of elevation and bathymetry78 and a time series of Red Sea sea level72 to reconstruct very-high-resolution land masks for the last 300k years. For each point in time, we determined the set of connected land masses, and the distances between the closest points of any two land masses. The result can be graph-theoretically represented by a complete graph whose nodes represent connected land masses and whose edge weights correspond to the minimum distances between land masses. The path involving the minimum continuous distance on water was then determined by solving the minmax path problem whose solution is the path between the two nodes representing Africa and the Arabian Peninsula that minimises the maximum weight of any of its edges (Fig. 1b grey shades).Analyses were conducted using Matlab R2019a79.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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