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AffiliationsDepartment of Forest Ecology and Management, Swedish University of Agricultural Sciences (SLU), Umeå, SwedenHyungwoo Lim, Torgny Näsholm, Tomas Lundmark & Harald GripNicholas School of the Environment, Duke University, Durham, NC, USARam OrenDepartment of Forest Sciences, University of Helsinki, Helsinki, FinlandRam OrenDepartment of Soil and Environment, SLU, Uppsala, SwedenMonika StrömgrenSouthern Swedish Forest Research Centre, SLU, Alnarp, SwedenSune LinderAuthorsHyungwoo LimRam OrenTorgny NäsholmMonika StrömgrenTomas LundmarkHarald GripSune LinderCorresponding authorsCorrespondence to
Hyungwoo Lim or Ram Oren. More

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Calculating landscape, attractor, and restrictorIn this work, we considered communities with commensal, mutualistic, and exploitative interactions. Below, we describe the differential equations for each type of interaction, and how we calculate the corresponding community function landscape, species-composition attractor, and Newborn restrictor.Commensal H–M community: The model community for most simulations is the same commensal H–M community used in our previous work15. The community function landscape plots P(T) as a function of ϕM(0) and ({overline{f}}_{P}(0)). Assume that a Newborn community has 100 biomass units, that all cells have the same genotype (all M cells have the same ({f}_{P}={overline{f}}_{P}(0))), that death and birth processes are deterministic, and that there is no mutation. P(T) can then be numerically integrated from the following set of scaled differential equations for any given pair of ϕM(0) and ({overline{f}}_{P}(0))15:$$frac{dR}{dt}=-{c}_{{RM}}{g}_{M}M-{c}_{{RH}}{g}_{H}H$$
(1)
$$frac{dB}{dt}={g}_{H}H-{c}_{{BM}}{g}_{M}M$$
(2)
$$frac{dP}{dt}={f}_{P}{g}_{M}M$$
(3)
$$frac{dH}{dt}={g}_{H}H-{delta }_{H}H$$
(4)
$$frac{dM}{dt}={g}_{M}left(1-{f}_{P}right)M-{delta }_{M}M$$
(5)
where$${g}_{H}(R)={g}_{{Hmax}}frac{R}{R+{K}_{{HR}}}$$
(6)
$${g}_{M}(R, B)={g}_{{Mmax}}frac{{R}_{M}{B}_{M}}{{R}_{M}+{B}_{M}}left(frac{1}{{R}_{M}+1}+frac{1}{{B}_{M}+1}right)$$
(7)
and RM = R/KMR and BM = B/KMB. Unless otherwise specified, landscapes in this paper are obtained by integrating Equations (1–5) from t = 0 to t = 17.Equation (1) states that Resource R is depleted by biomass growth of M and H, where cRM and cRH represent the amount of R consumed per unit of M and H biomass, respectively. Equation (2) states that Byproduct B is released as H grows, and is decreased by biomass growth of M due to consumption (cBM amount of B per unit of M biomass). Equation (3) states that Product P is produced as fP fraction of potential M growth. Equation (4) states that H biomass increases at a rate dependent on Resource R in a Monod fashion (Equation (6)) and decreases at the death rate δH. Note that Agricultural waste is not a state variable here as it is present in excess. Equation (5) states that M biomass increases at a rate dependent on Resource R and Byproduct B according to the Mankad and Bungay model (Equation (7)51) discounted by (1 − fP) due to the fitness cost of making Product, and decreases at the death rate δM. In the Monod growth model (Equation (6)), gHmax is the maximal growth rate of H and KHR is the R at which gHmax/2 is achieved. In the Mankad and Bungay model (Equation (7)), KMR is the R at which gMmax/2 is achieved when B is in excess; KMB is the B at which gMmax/2 is achieved when R is in excess.Mutualistic H–M community: If Byproduct is harmful for H, then the community is mutualistic: H and M promote the growth of each other. Such a mutualistic community can still be described by Equations (1–5) and (7), but Equation (6) is replaced with$${g}_{H}(R)={g}_{{Hmax}}frac{R}{R+{K}_{{HR}}}exp left(-frac{B}{{B}_{0}}right)$$
(8)
where larger B0 indicates lower sensitivity, or higher resistance of H to its Byproduct B.Exploitative H–M community: If M releases an antagonistic byproduct A that inhibits the growth of H, then the interaction is exploitative: H promotes the growth of M, but M inhibits the growth of H. Besides Eqs (1–5) and (7), we then need to add an equation that describes the dynamics of A$$frac{dwidetilde{A}}{dt}={r}_{A}{g}_{M}left(1-{f}_{P}right)M$$where rA is the amount of A released when M’s biomass grows by 1 unit. We can then normalize (widetilde{A}) with rA$$A=widetilde{A}/{r}_{A}$$so that$$frac{dA}{dt}={g}_{M}left(1-{f}_{P}right)M.$$
(9)
We also need to modify the growth rates for H:$${g}_{H}={g}_{H}(R)={g}_{{Hmax}}frac{R}{R+{K}_{{HR}}}frac{{A}_{0}}{A+{A}_{0}}$$
(10)
where larger A0 indicates lower sensitivity, or higher resistance of H to M’s Antagonistic by product A.To calculate the community function landscape, species attractor, and Newborn restrictor, all phenotype parameters, except ({overline{f}}_{P}(0)) take the value from the Bounds column in Table 1. To construct the landscape such as in Fig. 2c, we calculated P(T) for every grid point on a 2D quadrilateral mesh of 10−2 ≤ ϕM(0) ≤ 0.99 and (1{0}^{-2} le {overline{f}}_{P}(0) le 0.99) with a mesh size of ΔϕM(0) = 10−2 and ({{Delta }}{overline{f}}_{P}(0)=1{0}^{-2}). To construct the landscapes in Fig. 5b(ii) and b(iii), P(T) was similarly calculated on a 2D grid with a finer mesh of ΔϕM(0) = 5 × 10−3 and ({{Delta }}{overline{f}}_{P}(0)=1{0}^{-4}).To calculate the species composition attractor, we integrated Equations (1–5) to obtain ϕM(T) − ϕM(0) for each grid point on the 2D mesh of ϕM(0) and ({overline{f}}_{P}(0)). The contour of ϕM(T) − ϕM(0) = 0 is then the species attractor (blue dashed curve in Fig. 2b).The attractor-induced Newborn restrictor at a given ({overline{f}}_{P}(0)) is calculated from its definition: if ϕM(0) of a parent Newborn is on the restrictor, then so is the average ϕM(0) among its offspring Newborns. Under no spiking, since the average ϕM(0) among offspring Newborn is the same as ϕM(T) of their parent Adult, the Newborn restrictor coincides with the species attractor (Fig. 3b and Fig. 5b ii). Under x% H spiking, x% of the biomass in Newborns is replaced with H cells. Thus if the parent Adult’s fraction of M biomass is ϕM(T), the average ϕM(0) among its offspring Newborns is (1 − x%)ϕM(T) under x% H spiking. The Newborn restrictor therefore is the contour of (1 − x%)ϕM(T) − ϕM(0) = 0 (teal curve in Fig. 5a ii and b iii, Fig. 2d ii). Compared with the orange restrictor under no spiking, the teal restrictor is shifted down.Parameter choicesDetails justifying our parameter choices are given in the Methods section of our previous work15. Briefly, our parameter choices are based on experimental measurements of microorganisms (e.g., S. cerevisiae and E. coli). To ensure the coexistence of H and M, M must grow faster than H for part of the maturation cycle since M has to wait for H’s Byproduct at the beginning of a cycle. Because we have assumed M and H to have similar affinities for Resource (Table 1), the maximal growth rate of M (gMmax) must exceed the maximal growth rate of H (gHmax), and M’s affinity for Byproduct (1/KMB) must be sufficiently large. Moreover, metabolite release and consumption need to be balanced to avoid extreme species ratios. We assume that H and M consume the same amount of Resource per new cell (cRH = cRM) since the biomass of various microbes shares similar elemental (e.g., carbon or nitrogen) compositions. We set consumption value so that the input Resource can support a maximum of 104 total biomass. The evolutionary bounds are set, such that evolved H and M could coexist for fp  0, the number of H cells supplemented to the Newborn community is the nearest integer to (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}{L}_{H}^{-1}). Because integer number of cells is assigned to each Newborn, the total biomass might not be exactly BMtarget but within a small deviation of ~2 biomass units.To mimic reproducing through pipetting, each M and H cell in an Adult community is assigned a random integer between 1 and dilution factor nD (Equation (12)). All cells assigned with the same random integer are then dealt to the same Newborn, generating nD Newborn communities. If φS  > 0, the number of H cells supplemented into each Newborn is a random number drawn from a Poisson distribution of a mean of (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}{L}_{H}^{-1}).To mimic reproducing through cell sorting, each Newborn receives a biomass of (B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{varphi }_{S}right)) from its parent Adult. Suppose that the fraction of M biomass in the parent Adult is ϕM(T), then M cells from the parent Adult are randomly assigned to the Newborn, until the total biomass of M comes closest to (B{M}_{{{{{{{{rm{target}}}}}}}}}{phi }_{M}(T)left(1-{varphi }_{S}right)) without exceeding it. H cells with a total biomass of (B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{phi }_{M}(T)right)left(1-{varphi }_{S}right)) are assigned similarly. If φS  > 0, the number of H cells supplemented to the Newborn community is the nearest integer to (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}{L}_{H}^{-1}) where LH is the biomass of individual H cell in the parent Adult. Because each of M and H cells had a length between 1 and 2, the actual biomass of M and H assigned to a Newborn could vary from the target by up to 2 biomass units. Consequently, deviations of BM(0) from BMtarget and of ϕM(0) from parent Adult’s ϕM(T) are only a few percent.Simulating species spiking when both H and M cells evolveIn the more complex scenario, both H and M evolve. We thus need to spike with evolved H and M clones. Additionally, Newborns are spiked with H or M clones from their own lineage as demonstrated in Supplementary Fig. 11a. Below, we describe the simulation code for the experimental procedure (Supplementary Fig. 11a) we simulated.In all simulations where 6 or 7 phenotypes are modified by mutations, chosen Adults are reproduced through pipetting in a similar fashion as described above. After Newborns are reproduced from a chosen Adult in Cycle C − 1, a preset number of H or M cells are randomly picked from the remaining of this Adult to form H or M-spiking mix for Cycle C. At the end of Cycle C, we choose 10 Adults with the highest functions. Assuming that each chosen Adult is reproduced through pipetting with φS-H-spiking strategy, a Newborn receives on average a biomass of (B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{varphi }_{S}right)) from its parent Adult community and on average a biomass of BMtargetφS from H spiking mix generated at the end of Cycle C − 1. Since each chosen Adult usually gives rise to 10 Newborns, the number of cells distributed from the chosen Adult to each Newborn is drawn from a multinomial distribution. Specifically, denote the integer random numbers of cells that would be assigned to 10 Newborns to be {x1, x2,…, x10}. If the chosen Adult has a total biomass of BM(T) composed of IM M cells and IH H cells (both IM and IH are integers), the probability that {x1, x2,…, x10} cells are assigned to 10 Newborns, respectively, and x11 cells remain, is$$Pr left({{x}_{1},{x}_{2},…,{x}_{10},{x}_{11}}right)=frac{({I}_{H}+{I}_{M})!}{{x}_{1}!cdots {x}_{10}!{x}_{11}!},{p}_{0}{{,}^{{x}_{1}+cdots +{x}_{10}}},{p}_{11}^{{x}_{11}}.$$Here, ({p}_{0}=B{M}_{{{{{{{{rm{target}}}}}}}}}left(1-{varphi }_{S}right)/BM(T)) is the probability that a cell is assigned to one of 10 Newborns, p11 = 1 − 10p0 is the probability that a cell is not assigned to Newborns. Thus, ({x}_{11}={I}_{H}+{I}_{M}-mathop{sum }nolimits_{i = 1}^{10}{x}_{i}) is the number of cells remaining after reproduction, from which H and M cells are randomly picked to generate the spiking mix for Cycle C + 1.Suppose that the current spiking strategy is φS-H, then these 10 Newborns are spiked with H-spiking mix generated in Cycle C − 1. An average of BMtargetφS of H biomass is spiked into each Newborn so that the total biomass of Newborns is on average BMtarget. Suppose that five H cells from the parent Adult’s lineage are randomly picked at the end of Cycle C − 1, and that they have biomass {LH1, LH2, LH3, LH4, LH5}, respectively. The total number of H cells assigned to each Newborn, xH, is then randomly drawn from a Poisson distribution with a mean of (B{M}_{{{{{{{{rm{target}}}}}}}}}{varphi }_{S}/{overline{L}}_{H}), where ({overline{L}}_{H}=frac{1}{5}mathop{sum }nolimits_{j = 1}^{5}{L}_{Hj}) is the average biomass of the five H cells. Each spiked H cell has an equal chance of being one of the five cells.Updating spiking percentage based on heritability checksWhen the community function landscape is unknown, we can estimate heritability of community function under different spiking percentages through parent–offspring regression. In most simulations (e.g., Fig. 7), heritability evaluation is carried out about every 100 cycles (“periodic heritability check”). In the simulations demonstrated in Supplementary Fig. 17, the average improvement rate in community function is estimated from the chosen Adults over the last 50 cycles. Heritability evaluation is carried out when this average improvement rate becomes negative (“adaptive heritability check”). For both periodic and adaptive checks, heritability evaluation can be postponed until within-community selection improves cell growth sufficiently to provide sufficient biomass for heritability check.During one round of heritability evaluation, heritability of community function is estimated through parent–offspring community function regression under all candidate spiking strategies (Supplementary Fig. 11b). The current spiking strategy is updated if an alternative spiking strategy confers significantly higher community function heritability.To evaluate heritability under one spiking strategy, up to 100 Newborn communities are generated under this spiking strategy. After these mature into Adults, their functions are the parent functions. Each Adult parent then gives rise to six Newborn offspring under the same spiking strategy. When the six Newborn offspring mature into Adults, the median of their functions is the average offspring function. When offspring functions are plotted against their parent functions, the slope of the least-squares linear regression (green dashed line in Supplementary Fig. 11b) quantifies the heritability of community function. Heritability of a community function is thus similar to heritability of an individual trait, except that we use median instead of mean of offspring functions, because median is less sensitive to outliers. The 95% confidence interval of heritability is then estimated by nonparametric bootstrap58,59. More specifically, first, 100 pairs of parent–offspring community functions are resampled with replacement. Second, heritability is calculated with the resampled data. Third, 1000 heritabilities are calculated from 1000 independent resamplings, from which the 95% confidence interval is estimated from the 5th and 95th percentile.An alternative spiking strategy is considered significantly more advantageous than the current spiking strategy if heritability of the alternative spiking strategy is higher than the right endpoint of the 95% confidence interval of the heritability of the current spiking strategy. If more than one alternative spiking strategies are more advantageous, the one with the highest heritability is implemented to replace the current strategy. Similarly, an alternative spiking strategy is considered more disadvantageous if heritability of the alternative spiking strategy is lower than the left endpoint of the 95% confidence interval of the heritability of the current spiking strategy. When implementing random spiking strategy, the current spiking strategy is updated with a strategy randomly picked from candidate spiking strategies.Simulating community selection with large population sizeWhen the population size of each community is scaled up by 10 or 100 times (Supplementary Figs. 2 and 18b), the simulation codes described above become inefficient. Instead of tracking the biomass and phenotype of each cell in a large population, we divide the cells into categories and track the number of cells from different categories, where a category is defined by a unique combination of cell biomass and phenotype ranges. In our simulations, the biomass of each cell ranges between 1 and 2, fP of each M cell ranges between 0 and 1. Since H cells do not mutate, H cells are divided into 100 categories. H cells that belong to category i have a biomass between [1 + (i − 1) × ΔL, 1 + i × ΔL] where ΔL = 10−2. Since only fP of M cells are modified by mutations, M cells are divided into 100 × 105 categories. M cells that belong to category (i, j) have a biomass between [1 + (i − 1) × ΔL, 1 + i × ΔL] and fP between [(j − 1) × ΔfP, j × ΔfP] where ΔfP = 10−5. Every time fP of a M cell is modified by mutations, this cell jumps from the current category to a new category determined by its new fP value.Similar to simulations with small population sizes, each selection cycle starts with ntot = 100 Newborn communities. Maturation time T is divided into time steps of length Δτ = 0.05. Over each time step, the growth in cell biomass and the changes in metabolites are simulated in a similar fashion as described above. At the end of each time step, the number of cells to die or to mutate in each category is drawn from a bionomial distribution. If fP of a M cell is modified by mutation, the mutation effect is drawn from the same distribution as described above: (frac{1}{2}) of mutations reduce fP to 0 and the other (frac{1}{2}) is randomly drawn from the distribution in Equation (11).At the end of a maturation cycle, top 10 Adults with the highest functions are chosen. Each then reproduces 10 Newborns via pipetting for the next cycle. The fold of dilution is similarly adjusted, so that the average of Newborn total biomass is BMtarget over all selection cycles. From each category of a chosen Adult, the number of cells assigned to a Newborn community is randomly drawn from a multinomial distribution.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Surface processes model and forcing mechanismsLandscape evolution over the last one million years interval is performed with the open-source modelling code Badlands34. It simulates the evolution of topography induced by sediment erosion, transport, and deposition (Fig. 1a). Amongst the different capabilities available in Badlands, we applied the fluvial incision and hillslope processes, which are described by geomorphic equations and explicitly solved using a finite volume discretisation. In this study, soil properties are assumed to be spatially and temporally uniform over the region, and we do not differentiate between regolith and bedrock. It is worth noting that the role of flexural responses induced by erosion and deposition is also not accounted for. Under these assumptions, the continuity of mass is governed by vertical land motion (U, uplift or subsidence in m/yr), long-term diffusive processes and detachment-limited fluvial runoff-based stream power law:$$frac{partial z}{partial t}=U+kappa {nabla }^{2}z+epsilon {(PA)}^{m}nabla {z}^{n}$$
(1)
where z is the surface elevation (m), t is the time (yr), κ is the diffusion coefficient for soil creep34 with different values for terrestrial and marine environments, ϵ is a dimensional coefficient of erodibility of the channel bed, m and n are dimensionless empirically constants, that are set to 0.5 and 1, respectively, and PA is a proxy for water discharge that numerically integrates the total area (A) and precipitation (P) from upstream regions34.Both κ and ϵ depend on lithology, precipitation, and channel hydraulics and are scale dependent34. All our landscape evolution simulations are running over a triangular irregular network of ~18. e6 km2 with a resolution of ~5 km, and outputs are saved every 1000 yr.The detachment-limited fluvial runoff-based stream power law is computed with a ({{{{{{{mathcal{O}}}}}}}}(n))-efficient ordering approach54 based on a single-flow-direction approximation where water is routed down the path of the steepest descent. The flow routing algorithm and associated sediment transport from source to sink depend on surface morphology, and sediment deposition occurs under three circumstances: (1) existence of depressions or endorheic basins, (2) if local slope is less than the aggregational slope in land areas and (3) when sediments enter the marine realm34. Submerged sediments are then transported by diffusion processes defined with a constant marine diffusion coefficient34.All landscape simulations are constrained with different forcing mechanisms, and five scenarios were tested (Supplementary Table 2).First, we impose precipitation estimates from the PaleoClim database38,39,40. These estimates are products from paleoclimate simulations (coupled atmosphere-ocean general circulation model) downscaled at approximately the same resolution as our landscape model (~5 km at the equator). Annual averages of precipitation rates are then used to provide rainfall trends in our simulations based on the ten specific snapshots available (from the mid-Pliocene warm period to late Holocene and present day). Between two consecutive snapshots, we assume that precipitation remains constant for the considered time interval. For exposed regions that are considered flooded in the PaleoClim database, we define offshore precipitations using a nearest neighbour algorithm where closest precipitation estimates are averaged from PaleoClim inland regions. To evaluate the role of precipitation variability on landscape dynamics, we also run a uniform rainfall scenario (2 m/yr obtained by averaging the annual precipitation rates from the PaleoClim database).Secondly, the models are forced with sea level fluctuations known to play a major role in the flooding history of the Sunda Shelf11,13,53. Two sea level curves are tested (Supplementary Fig. 1d). To account for the inherent uncertainty in reconstructed sea level variations, we chose a first curve37 obtained from a sea level stack constructed from five to seven individual reconstructions that agrees with isostatically adjusted coral-based sea level estimates at both 125 and 400 ka. The second one is taken from the global sea level curve reconstruction36 based on benthic oxygen isotope data and has been recently used to reconstruct the subsidence history of Sundaland17,18.The last forcing considered in our study is the tectonic regime. First, we chose to explore a non-tectonic model based on the default assumption of stability for the shelf17. Secondly, we assumed a uniform subsidence rate of −0.25 mm/yr recently derived from a combination of geomorphological observations, coral reef growth numerical simulations and shallow seismic stratigraphy interpretations17. Then, to represent the regional variations in the tectonic regime, we have compiled and digitised a number of calibration points (Supplementary Fig. 1b and Table 1) that were used to produce a subsidence and uplift map by geo-referencing calibration points and available tectonic polygons, and by Gaussian-smoothing and normalising the uplift and subsidence rates between the calibrated range to avoid sharp transitions in regions without observations. The resulting map does not account for fine spatial scale tectonic features such as fault systems43,55 or orogenic and sedimentary related isostatic responses. It rather represents a regional vertical tectonic trend with an overall uplift of Wallacea and NW Borneo regions and long-wavelength subsidence of Sunda Shelf and Singapore Strait17.Landscape evolution model calibrationThe landscape models start during the Calabrian in the Pleistocene Epoch, one million years before the present. At each time interval, the landscape evolves following Eq. (1) and the surface adjusts under the action of rivers and soil creep (Fig. 1a). In addition to surface changes, we extract morphometric characteristics such as drainage basins extents, river profiles lengths (Fig. 3 and Supplementary Fig. 2), distance between main rivers outlet (Supplementary Fig. 3) and tracks the cumulative erosion and deposition over time (Fig. 1b and Supplementary Fig. 1d).For model calibration, we perform a series of steps consisting in adjusting the initial elevation and the erosion–deposition parameters (i.e., κ and ϵ in Eq. (1)) to match with different observations.The initial paleo-surface is obtained by applying the uplift and subsidence rates backwards to calculate the total change in topography for the 1 Myr interval. Then, we test the simulated paleo-river drainages against results from a combination of phylogenic studies9,13 and paleo-river channels and valleys found from seismic and well surveys41,42,44. Iteratively, we modify our paleo-elevation to ensure those main river basins (e.g., Johore, Siam, Mekong, East Sunda) encapsulate the paleo-drainage maps reconstructed using lowland freshwater taxa described in13 (Supplementary Fig. 1a and Table 4) and that the major rivers follow paleo-rivers systems derived from both 2D and 3D seismic interpretations (Fig. 1b).For surface processes parametrisation, we tested different ranges of diffusion and erodibility coefficients and compared the final sediment accumulation across the Sunda Shelf (Fig. 1b) using estimated deposit thicknesses41,42,43,44. The Sunda Shelf is predominantly experiencing deposition over the past 500 kyr and increases in deposition are positively correlated with periods of sea level rise (i.e., Pearson’s coefficients for correlation with sea level above 80%, Supplementary Fig. 1d). After exploring a range of values, we set κ values to 1. e−2 and 8. e−2 m2/yr for terrestrial and marine environments and ϵ between 2.5 and 7.5 e−8yr−1 for the different scenarios to fit with chosen surveys dataset (Supplementary Table 2 and 3).Upon uniform subsidence case (−0.25mm/yr), flooding is limited, and the shelf only undergoes two full marine transgressions ( >60% of the shelf flooded) around 125 ka and during the last 10–20 kyr (Supplementary Fig. 1c). Upon spatially variable tectonics (non-uniform subsidence), partial flooding events are more pervasive, with higher magnitudes and greater temporal durations. Due to the shallow and flat physiography of Sundaland, we also note that even small increases in sea level amplitudes ( 0) and values higher than one and two standard deviations (zsc  > 1 and 2, respectively, Supplementary Fig. 1b). The approach provides a quantitative assessment of flow maps sensitivity to the chosen resistance maps.To gain additional insights into the distribution of connectivity regions across the shelf, we also employed a local spatial autocorrelation indicator, namely the Getis-Ord Gi⋆ index57. This hotspot analysis method assesses spatial clustering of the obtained current density maps, and the resultant z-scores provide spatially and statistically significant high or low clustered areas. The approach consists in looking at each local current value relative to its neighbouring one. From this spatial analysis, we extract both statistically significant hot and cold spots for each combination of resistance surfaces (Supplementary Fig. 5c). To extract statistically significant and persistent biogeographic connectivity areas across the exposed Sunda Shelf, we then combine all hotspots together and define preferential migration pathways as regions having a positive Gi⋆ z-scores for all resistance surfaces combination.We used the function zscore in the SciPy stats package to obtain the z-scores and the ESDA library for the Gi⋆ indicator computation.Modelling assumptions and limitationsThere are a number of important caveats for interpreting our modelling results.First, we made several assumptions related to our transient landscape evolution simulations. A single-flow direction algorithm54 was used to simulate temporal changes in river pathways. Recent work58 has shown that this algorithm might lead to numerical diffusion, fast degradation of knickpoints and underestimation of river captures particularly in flat regions. One way to address this would be to use a multiple flow direction method59 which allow for a better representation of flow distribution across the landscape. In this study, we also assumed a uniform and invariant soil erodibility coefficient for the entire domain and a detachment-limited erosion law. Even though the erodibility coefficient was calibrated independently for each simulation (Supplementary Table 3), soil cover and properties vary notably between Borneo, Sumatra, Java and the Malay Peninsula and soil conditions for the exposed sea floor would have changed significantly over successive flooding events12. Badlands software34 allows for multiple erodibility coefficients representing different soil compositions to be defined, and this functionality could be used to evaluate the impact of differential erosion on physiographic changes. Similarly, several transport-limited laws are also available and could be compared against our detachment-limited simulations.A second set of simplifications lies in the climatic conditions (i.e., rainfall regimes) used to force our simulations. We relied on the PaleoClim database40 which contains nine high-resolution paleoclimate dataset38,39,40 corresponding to specific time periods (4.2–0.3 ka, 8.326–4.2 ka, 11.7–8.326 ka, 12.9–11.7 ka, 14.7–12.9 ka, 17.0–14.7 ka, ca. 130 ka, ca. 787 ka and 3.205 Ma). The climate simulations from which these time periods are extracted do not consider emerged Sunda Shelf for the oldest inter-glacial events which can result in incorrect climatic pattern60. From 0.3–17 ka, precipitation fields in PaleoClim are obtained from the TRaCE21ka transient simulations of the last 21 kyr run with the CCSM3 model40. Although Fordham et al.39 show that precipitation errors range from 10–200% in their modern experiment, the paleoclim dataset provides a statistical downscaling method that includes a bias correction (namely the Change-Factor method, in which the anomaly between the modern simulation and observations is removed from the paleoclimate experiment) allowing the use of the model for paleoclimate studies40. The very same technique is applied for 130 ka and 787 ka fields that were obtained with different GCMs (namely HadCM3 and CCSM2). Given the absence of a million-year long transient climate simulation, we oversimplified the climatic conditions by considering that precipitation distribution and intensity remain constant between two consecutive intervals, generating an artificial stepwise evolution of rainfall through time. To evaluate the sensitivity of physiographic responses on the Sunda Shelf to precipitation, we ran a model with uniform rainfall over 1 Myr (scenario 4). Despite changes in the timing and extent of basins reorganisation (Supplementary Fig. 2 and Fig. 3b), we found limited differences in terms of flooding history and erosion/deposition patterns when compared with scenario 5 (Supplementary Fig. 1c, d and Supplementary Table 2). Recent work60 suggests clear regional responses induced by the emerged Sunda Shelf with seasonal enhancement of moisture convergence and continental precipitation induced by thermal properties of the land surface. This could significantly impact our simulation results. However, and at the time of writing, more continuous high-resolution paleoclimatic simulations considering the shelf as an emerged continental platform were still unavailable. Using high-resolution multi-model outputs would allow to target the uncertainty on climatic maps4 and will surely represent a significant advance for future studies. One approach would have used the orographic rainfall capability61 available in Badlands. The method is better suited to run generic simulations but falls short when applied to real cases as it relies on imposing paleo-environmental boundary conditions (e.g., temporal changes in wind direction and speed, moisture stability frequency or depth of moist layer) difficult to obtain for Earth-like model applied over geological time scales.Finally, our species-agnostic approach assumes an equally weighted cost between the three considered geomorphic features and does not account for additional factors (temperature, vegetation cover, solar radiation to cite a few), which are all important when assessing landscape connectivity for wildlife. Most importantly, we model connectivity at very large scales (5 km resolution). Often, species are highly influenced by microclimates and small-scale topography47. From our regional-scale simulations and hotspot analysis (Fig. 6), higher resolution models focusing on highly connected regions (across the Gulf of Thailand and Siam basin) could be applied to produce more detailed representations of species migration in the region. In addition, current flow field calculations from Circuitscape35 rely on randomly selecting nodes around the region of interest. For connectivity analysis, we used 33 terrestrial points located around the perimeter of the buffered Sundaland area (white contour line in Fig. 1b). Using a selection of nodes in a buffered region allows to reduce the bias in current density estimates46. However, bias might depend on the buffer size compared to the study area as well as the number of nodes selected46,47. Because of memory limitations and the great number of computed grids used to cover the past 500 kyr, we made a trade-off between buffer size and the number of selected points for pairwise calculations. Additional experiments could possibly be tested to evaluate bias in the proposed connectivity maps potentially using a tilling approach to reduce cell number45. More