dataesr/scientific-paragraphs-categorization huggingface dataset:
Area has been proposed to have a positive effect on cladogenesis and carrying capacity  3, 5, 7  , and a negative effect on extinction rates  7, 24. Archipelago isolation is hypothesised to reduce colonisation rates  6  and elevate anagenesis rates  25. Models including or excluding diversity-dependence in rates of colonisation and cladogenesis  9  (i. e. estimating a carrying capacity parameter) were compared. We also considered a set of post hoc models with alternative shapes for the relationships (post hoc power and post hoc sigmoid models, see Methods, Supplementary Information Table  1 ). We fitted a set of 28 candidate models to the global dataset using maximum likelihood (Supplementary Information Table  2 ). The shape of the relationship of CES rates with area and distance for the two best models is shown in Fig.
2  and Extended Data Fig. 1 ). In addition, we found no evidence for diversity-dependence, as K was estimated to be much larger than the number of species on the island and models without a carrying capacity parameter (no upper bound to diversity), such as M14 and M19, performed better than models including one (Supplementary Information Table  2 ). We also tested whether the inclusion of a combination of true archipelagos and single islands in our dataset could have affected our results, for example if opportunities for allopatric speciation are higher when an area is subdivided into multiple islands  26. We repeated analyses excluding single island units and found that the same model (M19) is preferred with similar parameter estimates. Hence, we discuss only the results for the main dataset (including both single islands and true archipelagos).
Our results are robust to uncertainty in colonisation and branching times (see 'Sensitivity to alternative divergence times and tree topologies section'). A parametric bootstrap analysis of the two preferred models (M14 and M19) demonstrated that the method is able to recover hyperparameters with precision and little bias (Extended Data Figs. 2  and 3 ). In order to test the significance of the relationships between area, isolation and CES rates, we conducted a randomization test on the global dataset with reshuffled areas and distances. This test estimated the exponent hyperparameters as zero in most reshuffled cases (i. e. no effect of area or isolation detected; Extended Data Fig. 4 ), confirming that the data are informative. To assess model fit we simulated archipelago communities under the best model (M19) and found that for most archipelagos the observed diversity metrics (numbers of species, cladogenetic species and colonisations) were similar to the expected numbers, with some exceptions: for example, diversity was underestimated for Comoros and São Tomé (Fig.
30  ), it remains possible that diversity dependence acts at the archipelago scale on multiple lineages  31. In addition to the effects of physical features on cladogenesis, we found that rates of anagenesis increase with island isolation. While impressive insular radiations tend to receive the most attention from evolutionary biologists (e. g. Darwin's finches or Hawaiian honeycreepers), our phylogenies revealed that the majority of endemic birds in our dataset in fact display an anagenetic pattern (at the time of human arrival 231 of 350 endemic species had no extant sister taxa on the archipelago and there were only 26 extant in situ radiations). The positive effect of archipelago isolation on rates of anagenesis that we estimate suggests this fundamental but overlooked process is impeded by high levels of movement between island and mainland populations.
Acknowledgments: (As these are quite long we can move some to the Methods section if requested) We thank the skilled guides and field assistants who helped with sample collection in the field. For comments on the manuscript: Alex Pigot, Tom Matthews   Colonisation times plot: grey horizontal lines -archipelago ages (Extended Data Table  1 ). Violin plots (blue) show the kernel density of the distribution of times of colonisation of bird species in each archipelago, obtained from the time-calibrated phylogenetic trees. Thick black line inside violin -interquartile distance; thin black line -95% CI; black dot -median. Archipelagos with no violin plot or dots are cases for which no species of our focal assemblage were present at the time of human arrival, or none were sampled using molecular data.
Physical and geological data Full archipelago data is given in Supplementary Data 2 and Extended Data Table  1. We obtained data on total contemporary landmass area for each archipelago. For our isolation metric, we computed the minimum round earth distance to the nearest mainland (Dm) in km using Google Earth. We considered 'nearest mainland' to be the nearest probable source of colonists (but see 'Sensitivity analyses' section for different isolation metrics). This is the nearest continent except for island groups that were closer to Madagascar, New Guinea or New Zealand than to the continent, in which case we assigned these large continent-like islands as the mainland. This is supported by our phylogenetic data -for example, many Indian Ocean island taxa have closest relatives on Madagascar rather than mainland Africa.
Island palaeo-areas and past archipelago configurations have been shown to be better predictors of endemic insular diversity than contemporary area  14, 34. In contrast, island total native and non-endemic richness is better predicted by present island characteristics  14, 34. With insufficient data on island ontogeny being available (i. e. describing empirical area trajectories from island birth to present) we therefore analysed contemporary area and isolation as currently the most appropriate units for our dataset. We conducted an extensive survey of the literature and consulted geologists to obtain archipelago geological ages (Extended Data Table  1 ), treating the age of the oldest currently emerged island as an upper bound for colonisation. Islands may have been submerged and emerged multiple times and we consider the age of the last known emergence.
For the Aldabra Group we used an age older than the published estimate. The current estimated age of re-emergence of Aldabra is 0. 125 Ma  35  , but nine out of 12 Aldabra colonisation events in our dataset are older, suggesting the archipelago was not fully submerged prior to this and may have been available for colonisation for a longer period. Therefore, for Aldabra we used an older upper bound of 1 Ma for colonisation, although we acknowledge that the mitochondrial markers used for dating may not provide sufficient resolution at the shallow temporal scale of the published age. For Hawaii, the colonisation times we obtained for more than half of the colonisation events were older than the age of the current high islands that is often used as a maximum age for colonisation (~5 Ma). Therefore, instead of this age, we used the much older estimate of 29.
For the focal avian groups, we compiled complete taxon lists for each of the 41 archipelagos based on recent checklists from Avibase (http://avibase. bsc-eoc. org), which we cross-checked with the online version of the Handbook of the Birds of the World (HBW 37 ). We followed HBW's nomenclature and species assignations, except for 12 cases where our phylogenetic data disagree with HBW's scheme (noted in the column 'Taxonomy' of Supplementary Data 1). For example, in eleven cases phylogenetic trees support raising endemic island subspecies to species status (we sampled multiple samples per island taxon and outgroup, and the island individuals form a reciprocally monophyletic well-supported clade), and for these taxa we decided it was more appropriate to use a phylogenetic species concept so as not to underestimate endemicity and rates of speciation (Supplementary Data 1).
We re-ran DAISIE analyses using HBW's classification and found that the maximum likelihood parameters are very similar and thus we report only the results using the scheme based on the phylogenies produced for this study. For each bird species found on each archipelago we aimed to sample sequence data for individuals on the archipelago and the closest relatives outside the archipelago (outgroup taxa). Our sampling success per archipelago is shown on Fig. 1  and Extended  Data Table  1.
Our dataset accounts for 153 taxa that were present upon first human contact and have gone extinct since, probably because of human activities including human introduction of invasive species. To our knowledge 71 of these taxa have previously been sequenced using ancient DNA or belong to clades present in our trees, and we were thus able to include them in the phylogenetic analyses as regular data (n = 54), or as missing species by adding them as unsampled species to a designated clade (n = 17). For the remaining 82 extinct taxa, sequences were not available and we were unable to obtain samples and to allocate them to clades. We assume that these taxa represent extinct independent colonisations and we included them in the analyses using the "Endemic_MaxAge" and "Non_endemic_MaxAge" options in DAISIE, which assume that they have colonised at any given time since the birth of the archipelago (but before any in situ cladogenesis event).
Sequence data: GenBank We conducted an extensive search of GenBank for available DNA sequences from the 596 island bird taxa fitting our sampling criteria and from multiple outgroup taxa, using software Geneious 11  47. The molecular markers chosen varied from species to species, depending on which marker was typically sequenced for the taxon in question, the commonest being cytochrome b (cyt-b). In total, we downloaded 3155 sequences from GenBank. For some taxa, sequences from both archipelago and close relatives from outside the archipelago were already available from detailed phylogenetic or phylogeographic analyses. In some cases, a target species had been sampled, but only from populations outside the archipelago. In other cases, the species on the archipelago had been sampled, but the sampling of the relatives outside of the archipelago was lacking or only from distant regions, which meant a suitable outgroup was not available on GenBank.
Sequence data: new samples Sequences available on GenBank covered only 54% (269/502) of the total independent colonisation events. We improved the sampling by obtaining new sequences for many   3. Sample information and GenBank accession numbers for all new specimens are provided in Supplementary Information Table  3. DNA was extracted from blood, feathers and museum toe-pad samples using QIAGEN DNeasy Blood and Tissue kits (QIAGEN, USA). For museum samples, we used a dedicated ancient DNA lab facility at the University of Potsdam to avoid contamination. The cyt-b region (1100 base pairs) was amplified using the primers shown in Extended Data Table  2. DNA from historical museum samples was degraded and cyt-b could not be amplified as a single fragment. We thus designed internal primers to sequence different overlapping fragments in a stepwise fashion (Extended Data Table  2 ).
Most alignments include taxa from a variety of archipelagos. Alignments were based on a variety of markers, according to which marker had been mostly sequenced for a given group. For the new dating analyses conducted for this study, we created 80 separate alignments for different groups using a combination of sequences from Genbank (n = 3155) and new sequences (n = 252) produced for this study. In some cases, we obtained alignments directly from authors of previous studies and these are credited in Extended Data Table  3. Phylogenetic divergence dating analyses were performed in BEAST 2  48. For each alignment we performed substitution model selection in jModeltest  49  using the Bayesian information criterion. We used rates of molecular evolution for avian mitochondrial sequences, which have been shown to evolve in a clock-like fashion at an average rate of ~2% per Ma  50.
Molecular rate calibrations can be problematic for ancient clades, due to high levels of heterotachy in birds  51. In addition, mitochondrial DNA saturates after about 10 to 20 million years, and genetic distances of more than 20% may provide limited information regarding dating  52. Therefore, we only used molecular rate dating to extract node ages for branching events at the tips of the trees, at the species or population level (oldest colonisation time in our dataset is 15. 3 Ma, but most are much younger). Rates of evolution were obtained from the literature and varied between different markers and taxonomic group (Supplementary Information Table  4 ). We applied the avian mitochondrial rates estimated from cyt-b by Weir and Schluter  50  (but see 'Sensitivity to alternative branching times and tree topologies' section for different rates).
We applied a Bayesian uncorrelated lognormal relaxed clock model. For each analysis, we ran two independent chains of between 10 and 40 million generations, with a birth-death tree prior. We assessed convergence of chains and appropriate burnins with Tracer, combined runs using LogCombiner, and produced maximum clade credibility trees with mean node heights in Tree Annotator. We produced a total of 80 maximum clade credibility trees. For 11 groups (Extended Data Table  4 ), well-sampled and rigorously-dated phylogenies were already available from recent publications, all of which conducted Bayesian divergence dating using a variety of calibration methods, including fossils and molecular rates. We obtained maximum clade credibility trees from these studies from online repositories or directly from the authors (Extended Data Table  4 ).
Colonisation and branching times The nodes selected in the dated trees for estimates of colonisation and branching times are given for each taxon in Supplementary Data 1. Our node selection approach was as follows. For cases in which archipelago individuals formed a monophyletic clade consisting exclusively of archipelago individuals, we used the stem age of this clade as colonisation time. For cases in which only one individual of the archipelago was sampled, we used the length of the tip leading to that individual, which is equivalent to the stem age. For cases in which the archipelago individuals were embedded in a clade containing mainland individuals of the same species, i. e. paraphyly or polyphyly; we assumed that this is due to incomplete lineage sorting of the insular and mainland lineages, and we therefore used the MRCA of the archipelago individuals, or the crown node when the MRCA coincides with the crown.
For these cases we used the crown or the stem age of the species as an upper bound for the age of the colonisation event, using the ''Non_endemic_MaxAge'' option in DAISIE. Finally, for 124 taxa (20. 8 %) no sequences of individuals from the archipelago were available on GenBank and we were not able to obtain samples for sequencing from the species or from close relatives. We assumed these cases constituted independent colonisations that could have taken place any time since the origin of the archipelago and the present, and applied the ''Non_endemic_MaxAge'' and "Endemic_MaxAge" options in DAISIE with a maximum age equal to the archipelago age. DAISIE makes use of this information  53.
Global dataset characteristics Data points from taxa of the same archipelago were assembled into 41 archipelagospecific datasets. These 41 datasets were in turn were assembled into a single datasethereafter the "global dataset" -which was analysed with DAISIE (DAISIE R object deposited in Mendeley). The global dataset (information Supplementary Data 1) has a total of 596 taxa (independent colonisation events plus species within radiations), covering 491 species from 203 different genera and eight orders. All taxa were included in the analyses: those which we sampled in phylogenies, but also those for which sequences or phylogenies could not be obtained and which were included following the approaches described in the colonisation and branching time methods. A summary of diversity and sampling per archipelago is given in Extended Data Table  1.
Sampling completeness In total, we produced new sequences from 252 new individuals, comprising 90 different species from 45 different genera, covering an additional 110 colonisation events that had never before been sampled (i. e. populations from islands where the species had not been sampled before). For at least 12 of these 90 species, we found no previous sequences on Genbank, including island endemics from Comoros, Galápagos, Rodrigues and São Tomé (Supplementary Information Table  5 ). The new sequences from 252 individuals increase the molecular sampling for extant colonisation events from 60% (223/373) to 89% (332/373). If we include historically extinct colonisations, we increased the molecular sampling from the existing 54% (269/502) of colonisation events to 75% (379/502). We also substantially increased molecular sampling of continental relatives, adding 78 new individuals from the continent or islands surrounding our archipelagos, covering 43 different species.
Estimating global hyperparameters Our aim is to examine the dependencies of the parameters that govern species assembly (colonisation, extinction, cladogenesis, anagenesis (CES rates), and carrying capacity) on the features of archipelagos (area, isolation). We developed a new method to estimate global hyperparameters that control the relationship between two key archipelago features (area and isolation) and archipelago-specific (local) CES rates. One can estimate directly from the global dataset the shape of the relationship between isolation and colonisation rate that maximizes the likelihood for the entire dataset. Our method finds the hyperparameters that maximize the likelihood of the entire dataset, i. e. the sum of the log likelihoods for each archipelago. We tested the hypothesis that area and distance from the nearest mainland have an effect on CES rates (cladogenesis, anagenesis, extinction and colonisation).
We fitted nine different sigmoid models (Supplementary Information Table  1 ), allowing cladogenesis, anagenesis and colonisation to vary with distance following a sigmoid function. The sigmoid function we used has an additional parameter in comparison to power functions. In total we fitted 28 candidate models (14 a priori, 14 post hoc) to the global dataset using maximum likelihood (ML). We fitted each model using 20 initial sets of random starting parameters to reduce the risk of being trapped in local likelihood suboptima. We used the age of each archipelago (Extended Data Table  1 ) as the maximum age for colonisation. We assumed a global mainland species pool M of 1000 species. The product of M and the intrinsic rate of colonisation ( " ) is constant as long as M is large enough (larger than the number of island species), and thus the chosen value of M does not affect the results.
To decide which information criterion to choose to select between different models we compared the performance of the BIC and the Akaike information criterion (AIC). We simulated 1,000 datasets each with models M9 and M19 and then fitted the M9, M14, M17 and M19 models to each of these datasets using two initial sets of starting parameters for each optimisation. We found that for datasets simulated using M9 an incorrect model was preferred using AIC in 10. 4 % of cases, but only in 0. 11 % of cases when using BIC. For datasets simulated using M19 an incorrect model was preferred 12. 8 % of cases using AIC and 11. 1 % of cases using BIC. We thus compared models using BIC, as this model has lower error rates. An alternative approach to estimating hyperparameters would be to calculate CES rates and their uncertainty independently for each archipelago and to then conduct a meta-analysis of the resulting data, including archipelago area and isolation as predictors.
Randomization analysis We conducted a randomization analysis to evaluate whether there is significant signal of a relationship between area and distance and local CES rates in our global dataset. We produced 1,000 datasets with the same phylogenetic data and archipelago ages as the global dataset, but randomly reshuffled archipelago area and Dm in each dataset. We then fitted the best post hoc model to each of these 1,000 randomized datasets. If the maximum likelihood estimates of exponent hyperparameters (i. e. the strength of the relationship) in the randomized datasets were non-zero this would indicate that the method is finding evidence for a relationship even if there is none. If, on the other hand, non-zero hyperparameters are estimated in the real data but not in the randomized datasets, this would mean that there is information in the data regarding the putative relationships.
A posteriori simulations We simulated 1,000 phylogenetic global datasets (41 archipelagos each) with the ML hyperparameters of the best a priori (M14) and post hoc models (M19). We first calculated the local CES rates for each archipelago based on their area and isolation and the hyperparameters for the model, and then used these CES rates as the parameters for the simulations using the DAISIE R package. The simulated data were used to measure bias and accuracy of the method, goodness of fit and the ability of our method to recover observed island biogeographic diversity patterns (see below).
Measuring precision and accuracy of method DAISIE estimates CES rates with high precision and little bias  9, 53. We conducted parametric bootstrap analyses to assess whether the ability to estimate hyperparameters from global datasets is also good (Extended Data Fig. 2  and 3 ), and to obtain confidence intervals on parameter estimates (Extended Data Table  5 ). We used DAISIE to estimate hyperparameters from the M14 and M19 simulated datasets (1,000 replicates each). We measured precision and accuracy by comparing the distribution of parameters estimated from the 1,000 simulated data set with the real parameters used to simulate the same datasets. To check whether ML optimisations of the simulated global datasets converge to the same point in parameter space, we first performed a test on a subset of the simulated data.
Measuring goodness of fit We measured how well the preferred models fitted the data using different approaches. First, we examined whether our models successfully reproduce the diversity patterns found on individual archipelagos. We calculated the total number of species, cladogenetic species and independent colonisations in each archipelago for each of the 1,000 simulated datasets. We then plotted these metrics versus the observed values in the empirical data (Fig. 3 ). Our preferred models have a slight tendency to overpredict Despite having sampled several new individuals from islands worldwide, given the wide geographical scale of our study we still rely on sequence data for thousands of individuals which has been submitted to Genbank over the years. Whenever multi-loci analyses including our focal taxa were available we used them, but these are rare (Extended Data Table  4 ).
Therefore, the majority of our phylogenies are based on a small number of genes, and most on a single gene, cyt-b, which is the most widely sequenced mitochondrial marker in birds. Although some studies on island birds have shown that colonisation times derived from mitochondrial trees often do not differ much from those obtained using multiple loci (Stervander et al, MORE REFS?), it is possible that for some cases the scaling and topologies of the trees might have been more accurate had we used multiple loci  57. This is particularly relevant for recent island colonists, given incomplete lineage sorting  58. An additional shortcoming of relying on published sequence data is that many of our DNA alignments often have substantial sections with missing data (e. g. because only one small section of the gene could be sequenced and was uploaded to Genbank), which has been shown to lead to biases in branch lengths and topology  59.