Philippa Kaur

Case-study selectionThe case studies are chosen to include different cities around the globe that are commonly used for urban studies50. From the 18 selected case-study cities, 12 cities are part of the C40 cities initiative, which aims to lead the way in urban sustainability. The selection ranges from large cities strongly following a grid street plan (for example, Mexico City and Tokyo) to smaller cities that are not dominated by a grid-like urban layout (for example, Zürich). This selection is not exhaustive, and comparatively more European cities are analysed. Barcelona is considered to test the methodology for the city where the superblock concept originates. The presented analysis can, however, be applied to further cities and is merely constrained by data availability.Street network data processingThe case-study extent for each city is 25 km2, for which the street network is downloaded with the help of Overpass API51 and represented as graphs with edges and nodes. For the graph-based algorithms and geometric processing steps, the python packages networkX52 and shapely53 are used. The downloaded raw data are processed in several steps to obtain more accurate street length estimations. First, network nodes that are within a 15 m distance of each other are clustered to obtain a simplified network. This street network abstraction particularly reduces the complexity of street intersections. Second, closed detached rings (loops) and isolated subgraphs are removed from the street network as well as long ( >300 m) tunnels and streets intersecting buildings. Further network cleaning is conceivable, depending on the case-study context, such as removing elevated streets. Third, very small network edges not forming part of longer streets are removed as they typically represent driveways (Supplementary Information section 2). The street network was not re-designed by extension. For example, the street network connectivity was not increased by adding new streets, which could potentially help to design more superblocks or miniblocks.Before searching for potential superblocks or miniblocks on the street network, the street network nodes are classified into higher- and lower-level nodes. Whenever a node forms part of an intersection (degree ≥3), the node is classified as a higher-level node. A lower-level node forms part of a street (edge) between two higher-level nodes. Typically, the street between two higher-level nodes consists of multiple lower-level nodes and edges as the streets are typically not perfectly straight. The lower-level nodes are considered for calculating distances on the street network and creating the blocks. However, when checking for the network topology criteria to identify superblocks and miniblocks, only the higher-level street network is considered. This differentiation is necessary as otherwise, superblocks and miniblocks often would not be identified on the street network due to the lower-level nodes (Supplementary Information section 1).Street hierarchyThe complexities surrounding superblock implementation are higher for streets that are essential to urban mobility. Contributors to OpenStreetMap can classify streets and assign different attribute labels to define a street network hierarchy. On the basis of the provided street hierarchy, the assumption is made that streets labelled as primary, secondary and trunk streets are not suitable for superblock design. Similarly, streets that form part of a trolleybus or tram route are excluded as potential superblock locations. For this analysis, all footways and private streets are ignored. Streets categorized as pedestrian or living streets are considered to be already not centred on car-based mobility and are ignored as the focus here is on transforming streets that are currently focused around car-based mobility.Calculation of population density and building coverageTo go beyond relying on street network characteristics for detecting superblock design, density values are calculated for the entire street network. For each network node, average density values are calculated considering a radius of 100 m and then averaged per edge by averaging the density values from the starting and end nodes. Alternatively, the population density could be calculated by buffering the street network edges. The population density calculation is based on population data provided by the Center for International Earth Science Information Network and Meta54, which are population estimates based on satellite data and census data at a ~30 m resolution. With the help of OpenStreetMap building footprints, the building footprint coverage is calculated first for every node on the street network considering the same radius of 100 m. Second, the average building coverage per edge is calculated by averaging the value from the start and end node of each edge.Detecting superblocks and miniblocks on the street networkThe street network forms the basis for locating potential superblock and miniblock candidates. Before searching potential candidates, the street network is characterized by street type, population density and building coverage as outlined in the previous two paragraphs for narrowing down the search. Then, the street network is first cleaned for cul-de-sac street elements, and the degree of all street network nodes is calculated. Second, all nodes with degree ≥3 are filtered as they could potentially be part of interior street intersections of superblocks or miniblocks. From this reduced filtered street network, all network cycles are identified as they could potentially be an interior street loop of a superblock. Isolated nodes with degree ≥3 not forming part of a network cycle or interior street loop of a superblock are further evaluated as a potential miniblock. Third, to find the exterior streets, all neighbouring nodes of the considered node(s) are identified, and a shortest-path algorithm is applied on the network where the considered nodes are removed to connect all the neighbouring nodes. If no path is found, the street network typology prevents the design of a miniblock or superblock. For miniblocks, all neighbours of the single node (single interior street intersection) are selected. Similarly, for superblocks, all neighbouring nodes not forming part of the interior street loop are connected along the shortest-path route to form the superblock polygon. If no path is found, or the path crosses a bridge, the node is not further considered. Finally, the geometric properties of the identified potential superblocks or miniblocks are checked to determine whether all the boundary conditions are fulfilled. In case a potential superblock does not fulfil the boundary conditions, the conditions for fulfilling a miniblock are tested. Table 1 lists all conditions, and the geometric scenario calculation is outlined in the next section.Geometric scenario calculationThe geometry of the superblock is calculated on the basis of the assumed 400 m length of the Barcelona superblock (lBCN) consisting of three individual blocks (block side length (l_{{mathrm{block}}} = frac{{l_{{mathrm{BCN}}}}}{3})). When identifying superblocks (S) or miniblocks (M), the following boundary conditions for the exterior street length (lext), interior street length (lint) and length of the interior street loop or ring (r) (for superblocks only) need to be fulfilled:$$l_{{mathrm{ext}},{mathrm{max}}}^{mathrm{M}} = left( {8 times l_{{mathrm{block}}} times z times left( {1 + f} right)} right)$$
(1)
$$l_{{mathrm{ext}},{mathrm{min}}}^{mathrm{M}} = left( {frac{{8 times l_{{mathrm{block}}} times z}}{{1 + f}}} right)$$
(2)
$$l_{{mathrm{int}},{mathrm{max}}}^{mathrm{M}} = left( {4 times l_{{mathrm{block}}} times z times left( {1 + f} right)} right)$$
(3)
$$l_{{mathrm{int}},{mathrm{min}}}^{mathrm{M}} = left( {frac{{3 times l_{{mathrm{block}}} times z}}{{1 + f}}} right)$$
(4)
$$l_{{mathrm{ext}},{mathrm{max}}}^{mathrm{S}} = left( {12 times l_{{mathrm{block}}} times {{{{z}}}} times left( {1 + f} right)} right)$$
(5)
$$l_{{mathrm{ext}},{mathrm{min}}}^{mathrm{S}} = left( {frac{{12 times l_{{mathrm{block}}} times {{{{z}}}}}}{{1 + f}}} right)$$
(6)
$$r_{{mathrm{int}},{mathrm{max}}}^{mathrm{S}} = left( {4 times l_{{mathrm{block}}} times {{{{z}}}} times left( {1 + f} right)} right)$$
(7)
$$r_{{mathrm{int}},{mathrm{min}}}^{mathrm{S}} = left( {frac{{4 times l_{{mathrm{block}}} times {{{{z}}}}}}{{1 + f}}} right)$$
(8)
where f denotes the deviation factor and z incorporates an uncertainty of ±20% of the Barcelona superblock and takes a value of 0.8 (min) or 1.2 (max). Example values for G0 and G1 are provided in Table 1.Street NDIThe Edmonds–Karp algorithm is applied to assess the importance of each network edge concerning traffic flow across the street network. This approach is inspired by ref. 55, where artificial ‘super’ sinks/sources are introduced. Super sinks/sources enable extending a network by adding a single node that feeds all the sources or drains all the sinks56. The following steps are performed to assess the street network edge criticality. In a first step, four supernodes are projected in each direction to the network and placed outside the street network extent. Then, 100 helper nodes are equally distributed along a straight axis on each side of the network extent. An auxiliary linking edge is established between each helper node and the supernode. An additional linking edge is next added between each helper node and the closest node on the street network. A visualization and a more detailed explanation of this step are provided in the Supplementary Information section 3. In a second step, the Edmonds–Karp algorithm is run consecutively for each direction between opposing super sinks and super sources. The resulting flows of each simulation run are summed and averaged for each edge (i) to obtain the average flow ((f_i^{{mathrm{avg}}})) per edge. The average edge flow is then normalized ((f_i^{{mathrm{norm}}})) with the calculated maximum average flow value of the network:$$f_i^{{mathrm{norm}}} = frac{{f_i^{{mathrm{avg}}}}}{{mathop {{max }}limits_j (f_j^{{mathrm{avg}}})}}$$
(9)
To consider local as well as regional network impacts, these outlined steps are performed on a raster with a resolution of 2.5 km as well as for the entire case-study area. The calculations on the raster provide local flows ((f_i^{{mathrm{norm}}})) as outlined in the preceding. As the overall analysed city extent is 25 km2, local flows are calculated across four regional cells across the city. In addition to these local flows, the same calculation is performed once for the entire case-study area using the 5 × 5 km2 as the input for the flow calculations in equation (9). This calculation using the entire street network reflects flows at a higher geographical level and is termed (F_i^{{mathrm{norm}}}). In a third step, the two calculations are equally weighted and combined into a single indicator (NDI) to calculate the relative importance of each edge concerning traffic flow:$${mathrm{NDI}}_i = frac{{f_i^{{mathrm{norm}}} + F_i^{{mathrm{norm}}}}}{2}$$
(10)
Edges with high NDI are edges that are critical to the street network; edges with low NDI have a low disruption potential as they do not form part of a critical network element and alternative paths exist for rerouting traffic. Calculating the NDI provides an approximate indication of the street disruption of a network towards urban mobility. The NDI is further used to derive classes indicating the street network disruption (low, middle, high). As identical geographical extents have been selected across our case studies, the obtained NDI values are comparable. More

Genetic diversity and population structureThe 614 bp length of mtCO1 sequences was successfully amplified and sequenced from 75 individuals of E. tetradactylum and 89 individuals of E. rhadinum from different sites. Based on the CO1 analysis, we detected 5 and 16 haplotypes, respectively, from E. tetradactylum and E. rhadinum (Table 1). Only one haplotype was inter-specifically shared in E. tetradactylum populations, as showed in the TCS haplotype networks (Fig. 2a). A total of 77 polymorphic sites was identified in E. rhadinum but 3 polymorphic sites in E. tetradactylum. Among these sites, a total of 3 and 11 parsimoniously informative sites was detected in E. tetradactylum and E. rhadinum, respectively. In E. tetradactylum, the number of CO1 haplotypes was 2 in ZS and 3 in PA and ZJ. The haplotype diversity was also much higher in ZJ (0.211) and PA (0.197) than ZS (0.105). In E. rhadinum, CO1 haplotypes varied from 3 (JH) to 8 (ZZ). The haplotype diversity was the highest in ZZ (0.663). The populations of ZJ and ZZ showed the statistically negative Tajima’s D value, which could signify the demographic expansion. The MDA revealed similar results (Fig. S3).Table 1 Genetic polymorphisms and neutrality tests of Eleutheronema tetradactylum and Eleutheronema rhadinum inferred from CO1 and 16s rRNA.Full size tableFigure 2The unrooted TCS haplotype networks were constructed based on the haplotypes of CO1 (a) and 16s rRNA (b) of Eleutheronema tetradactylum (left) and Eleutheronema rhadinum (right). Haplotype frequency was related to the size of the circle. Different colors within the nodes refer to various sampling sites.Full size imageThe mitochondrial 16s rRNA (574 bp in length) was also successfully sequenced from 75 and 89 individuals of E. tetradactylum and E. rhadinum (Table 1), which yielded 5 and 6 haplotypes, respectively (Fig. 2b). No haplotype was interspecifically shared of 16s rRNA both in E. tetradactylum and E. rhadinum. A total of 4 and 14 polymorphic sites of E. tetradactylum and E. rhadinum were identified, respectively, of which 3 and 4 were parsimoniously informative sites. Table 1 shows that only four haplotypes with 0.200 haplotype diversity were identified in E. tetradactylum from PA. In E. rhadinum, relatively high haplotype diversity (H = 0.481) and nucleotide diversity (π = 0.00170) were found in populations SA. Overall, the populations from Thailand showed higher genetic diversity than the China population both for E. tetradactylum and E. rhadinum.The TCS network was constructed to identify the phylogenetic relationships in E. tetradactylum and E. rhadinum between China and Thailand populations, as shown in Fig. 2. In E. tetradactylum, 5 haplotypes were closely related to a small number of mutation steps, and the Hap_1 was likely the most primitive haplotype, which evolved into others. In E. rhadinum, 16 haplotypes were distributed between the two branches, including China and Thailand branches. Only the Hap_7 was shared in ZJ and SA of the Thailand branch. One (hap_1) in E. tetradactylum and two (Hap_2 and Hap_8) in E. rhadinum were used as the central radiation distribution for most haplotypes. Other haplotypes were formed by a small number of mutations of these haplotypes. As shown in the TCS network of 16s rRNA haplotypes, the Hap_1 in E. tetradactylum and Hap_4 in E. rhadinum were the most primitive haplotype, which showed central radiation distributions. Also, in E. rhadinum, the haplotypes of China and Thailand populations were divided into two branches; only Hap_2 was shared in ZJ and SA.The level of population genetic differentiation between China and Thailand populations was also evaluated (Table S3). In E. tetradactylum, the average Fixation index (Fst) between PA and the other two sites was 0.81344 in ZS (p  More

Scat sample collection and preparationAt known harbour seal haulout sites individual scat samples were collected using a standardized protocol (Fig. 1). Disposable wooden tongue depressors were used to transfer deposited scats into 500 ml single-use jars or zip-style bags lined with 126 µm nylon mesh paint strainers18. Samples were either preserved immediately in the field by adding 300 ml 95% ethanol to the collection jar, or were taken to the lab and frozen at −20 °C within 6 hours of collection19. Later, samples were thawed and filled with ethanol before being manually homogenized with a disposable wooden depressor inside the paint strainer to separate the scat matrix material from hard prey remains (e.g. bones, cephalopod beaks). The paint strainer containing prey hard parts was then removed from the jar leaving behind the ethanol preserved scat matrix for genetic analysis20. The paint strainer containing prey hard parts was refrozen for subsequent parallel morphological prey ID.Fig. 1The 52 harbour seal scat collection sites in the Salish Sea represented in this dataset.Full size imageMolecular laboratory processingScat matrix samples were subsampled (approximately 20 mg), centrifuged and dried to remove ethanol prior to DNA extraction. DNA was extracted from scat with the QIAGEN QIAamp DNA Stool Mini Kit according to the manufacturer’s protocols. For additional details on the extraction process see Deagle et al.21 and Thomas et al.20.The metabarcoding marker we used to quantify fish and cephalopod proportions was a 16S mDNA fragment (~260 bp) previously described in Deagle et al.15 for pinniped scat analysis. We used the combined Chord/Ceph primer sets: Chord_16S_F (GATCGAGAAGACCCTRTGGAGCT), Chord_16S_R (GGATTGCGCTGTTATCCCT), and Ceph_16S_F (GACGAGAAGACCCTAWTGAGCT), Ceph_16S_R (AAATTACGCTGTTATCCCT). This multiplex PCR reaction is designed to amplify both chordate and cephalopod prey species DNA. A blocking oligonucleotide was included in the all 16S PCRs to limit amplification of seal DNA22. The oligonucleotide (32 bp: ATGGAGCTTTAATTAACTAACTCAACAGAGCA-C3) matches harbour seal sequence (GenBank Accession AM181032) and was modified with a C3 spacer so it is non-extendable during PCR22.A secondary metabarcoding marker was used in a separate PCR reaction to quantity the salmon portion of seal diet, because the primary 16S marker was unable to reliably differentiate between coho and steelhead DNA sequences. This marker was a COI “minibarcode” specifically for salmonids within the standard COI barcoding region: Sal_COI_F (CTCTATTTAGTATTTGGTGCCTGAG), Sal_COI_R (GAGTCAGAAGCTTATGTTRTTTATTCG). The COI amplicons were sequenced alongside 16S such that the overall salmonid fraction of the diet was quantified by 16S, and the salmon species proportions within that fraction were quantified by COI.To take full advantage of sequencing throughput, we used a two-stage labeling scheme to identify individual samples that involved both PCR primer tags and labeled MiSeq adapter sequences. The open source software package EDITTAG was used to create 96 primer sets each with a unique 10 bp primer tag and an edit distance of 5; meaning that to mistake one sample’s sequences for another, 5 insertions, substitutions or deletions would have to occur23.All PCR amplifications were performed in 20 μl volumes using the Multiplex PCR Kit (QIAGEN). Reactions contained 10 μl (0.5 X) master mix, 0.25 μM of each primer, 2.5 μM blocking oligonucleotide and 2 μl template DNA. Thermal cycling conditions were: 95 °C for 15 min followed by 34 cycles of: 94 °C for 30 s, 57 °C for 90 s, and 72 °C for 60 s.Amplicons from 96 individually labeled samples were pooled by running all samples on 1.5% agarose gels, and the luminosity of each sample’s PCR product was quantified using Image Studio Lite (Version 3.1). To combine all samples in roughly equal proportion (normalization), we calculated the fraction of each sample’s PCR product added to the pool based on the luminosity value relative to the brightest band. After 2013, amplicon normalization was performed using SequalPrep™ Normalization Plate Kits, 96-well.Sequencing libraries were prepared from pools of 96 samples using an Illumina TruSeq DNA sample prep kit which ligated uniquely labeled adapter sequences to each pool. Libraries were then pooled and DNA sequencing was performed on Illumina MiSeq using the MiSeq Reagent Kit v2 (300 cycle) for SE 300 bp reads. Samples were sequenced on multiple different runs as part of the larger study; however, typically between 4 and 6 libraries (each a pool of 96 individually identifiable samples) were sequenced on a single MiSeq run.BioinformaticsTo assign DNA sequences to a fish or cephalopod species, we created a custom BLAST reference database of 16S sequences by an iterative process. First, using a list of the fish species of Puget Sound, we searched Genbank for the 16S sequence fragment of all fishes known to occur in the region (71 fish families 230 species)24,25. Reference sequences for each prey species were included in the database if the entire fragment was available, and preference was given to sequences of voucher specimens. When the database was first generated (November, 2012) Genbank contained 16S sequences for 192 of the 230 fish species in the region, and the remaining 38 species were mostly uncommon species unlikely to occur in seal diets. Following a similar procedure, we added to this database sequences for all of the regional cephalopods for which 16S data were available (7 squid species, 2 octopus species). A separate reference database was generated for the COI salmon marker containing Genbank sequences for the nine salmonid species known to occur regionally: Oncorhynchus gorbuscha (Pink Salmon), Oncorhynchus keta (Chum Salmon), Oncorhynchus kisutch (Coho Salmon), Oncorhynchus mykiss (Steelhead), Oncorhynchus nerka (Sockeye Salmon), Oncorhynchus tshawytscha (Chinook Salmon), Oncorhynchus clarkii (Cutthroat Trout), Salmo salar (Atlantic Salmon), Salvelinus malma (Dolly Varden)24.To determine if some species in the database cannot be distinguished from each other at 16S (i.e. have identical sequences in the reference database) a distance matrix was performed on the complete database using the DistanceMatrix function in the R package DECIPHER26. Species with identical sequences were identified as having a distance of “0.00”. In some cases, one haplotype for a species was identical to another species but other haplotypes were not. When two species’ sequences were identical, we ultimately reported both species in the prey_ID field.Sequences were automatically sorted (MiSeq post processing) by amplicon pool using the indexed TruSeqTM adapter sequences. FASTQ sequence files for each library were imported into MacQIIME (version 1.9.1-20150604) for demultiplexing and sequence assignment to species27. For a sequence to be assigned to a sample, it had to match the full forward and reverse primer sequences and match the 10 bp primer tag for that sample (allowing for up to 2 mismatches in either primers or tag sequence).Next, we clustered the DNA sequences that were assigned to scat or tissue samples with USEARCH (similarity threshold = 0.99; minimum cluster size = 3; de novo chimera detection), and entered a representative sequence from each cluster into a GenBank nucleotide BLAST search28,29. If the top matching species for any cluster was not included in the existing database (or the sequence differed indicating haplotype variation), we put the top matching entry in the reference database. We repeated this procedure with every new batch of sequence data to minimize the potential for incorrect species assignment or prey species exclusion. This process was conducted for both the 16S and COI reference databases with each new batch of samples.For all DNA sequences successfully assigned to a sample, a BLAST search was performed against our custom 16S or COI reference databases. A sequence was assigned to a species based on the best match in the database (threshold BLASTN e-value  More

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