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This study provides quantitative data for Ocyropsis spp. feeding mechanisms and in situ data for gut contents during both day and night to begin assessing their trophic role in oceanic waters. Previous studies qualitatively described the feeding pattern of Ocyropsis spp.15 whereby this animal uses a unique capture mechanism among lobate ctenophores: direct transfer from lobe to mouth and encounters involving the mouth actively grabbing copepod prey24. These previous observations are confirmed as Ocyropsis spp. is able to deploy its dexterous, prehensile mouth to effectively capture prey within the lobes (Figs. 2, 3) and quantitative assessments of predation are also provided. It should be noted that while Ocyropsis spp. are known to occasionally consume a wide variety of prey types and sizes15, this study focuses only on copepod prey because our field data showed recognizable prey in Ocyropsis spp. guts was almost exclusively copepods.For example, mean speed of the mouth is less than 6 mm s−1 during predation events on copepods. Thus, while it may look rapid to the human eye, this is far below the escape swimming speeds exhibited by many copepods which are capable of moving at speeds of up to 500 mm s−125,26. Our observations show that the mechanism of capture is thus not reliant on grabbing copepods from the water between the ctenophore lobes with the mouth, but rather aided by copepod contact with the ctenophore lobes. Copepods between the lobes swam only with a speed of 7.94 mm s−1 (S.D. 7.25), to which the average mouth speed (5.83 mm s−1 (S.D. 1.68)) is comparable (Table 1). This suggests that Ocyropsis is able to reduce copepod swimming activity either by trapping them against the lobes (lobes respond to contact by prey) and/or the use of some form of adhesion or chemical that acts to reduce copepod activity. This unusual form of predation using a prehensile mouth allows Ocyropsis to be highly effective predators without the use of prey capturing tentillae seen in other lobate species.The presence of multiple prey has the potential to disrupt a raptorial type feeder such as Ocyropsis spp. more so than other lobates, since they lack tentillae, which would allow them to capture multiple prey simultaneously. Instead Ocyropsis spp. transfer one prey at a time directly from lobe to mouth15,27. So how is this ctenophore able to maintain such a high overall capture rate? The answer appears to be that Ocyropsis will modulate the number of attempts with the prehensile mouth depending on the number of prey present. For example, we did not observe any captures on the first attempt with the mouth with multiple prey, but the animals made up to 8 attempts at capturing the nearest copepod. This is in contrast to single copepod encounters in which ctenophores captured copepods on the first attempt 61% of the time and rarely made over 2 attempts, never exceeding 3 attempts (Figs. 3a, 5a, Table 1). This demonstrates Ocyropsis spp. can adjust its behavior to maintain high overall capture success when presented with multiple simultaneous prey. It is also interesting to note that the resulting increase in handling time due to making more attempts during multiple prey encounters is still lower than the handling time for most other lobates dealing with single prey27,28. It is not clear how often Ocyropsis spp. need to deal with multiple copepods simultaneously in nature, as oceanic waters contain characteristically low ctenophore prey densities compared to coastal zones9,29, however prey can be highly patchy and it appears that the unique prey capture mechanism of Ocyropsis spp. is still able to operate effectively in high density patches by increasing the number of attempts before aborting the attack which could serve as a means to maintain similar ingestion rates to single prey encounters.Typically, the feeding sequence of a ctenophore involves capture of prey in sticky colloblast cells and retraction of tentillae and/or ciliary transport of prey to the mouth15,27,30. These feeding mechanisms result in a range of handling times ranging from 2.5 s for Bolinopsis. infundibulum28 to nearly 22 min for Pleurobrachia bachei27. Capture rates can also be quite high, with overall capture success rates up to 74% for Mnemiopsis leidyi2,3. We found Ocyropsis has a relatively fast mean handling time of 6.3 s when a single copepod was present between the lobes, but handling time increased by approximately 2.5-fold if multiple prey were present. Overall capture success rates were comparable to the highly effective coastal ctenophore, M. leidyi, with a 71% success rate with single prey present and 81% capture rates if multiple prey were present between the lobes. Thus, Ocyropsis spp. are able to capture prey with high efficiency despite the differences in feeding mechanics compared to coastal lobate ctenophores. Additionally, since encounter rates of planktivores are directly related to the time spent searching for prey and time spent handling prey27, the relatively short handling time of Ocyropsis spp. and their direct feeding mechanism may allow them to sample more water and encounter a larger proportion of the available prey population than other species.Diel patterns of prey consumptionMany planktivorous species exhibit higher gut fullness at night31,32, due to higher prey availability in surface waters as a result of a diel vertical migration33,34. In situ gut content images showed that Ocyropsis spp. had a significantly higher gut fullness at night (12.4%) compared to during the day (4.2%) (Fig. 7). Ocyropsis spp. also had higher numbers of prey per individual gut at night, although overall biomass was not significantly different between night and day (Fig. 7). This can be explained by differences in prey characteristics; prey observed in the gut during the day were significantly larger (Table 2). This may be due to an ability to feed more selectively during the day since overall prey densities are lower. It should also be considered that turbulence in surface waters is, on average, much lower at night compared to daytime35 and that even small amounts of turbulence can negatively impact ctenophore feeding36,37. Therefore, smaller prey may have a higher likelihood of evading detection of Ocyropsis during the day compared to night, especially since these animals are most frequently observed in the upper 15 m of oceanic waters.Kremer, et al.38 estimates that O. crystallina requires 252 prey items to sustain itself. On average, Ocyropsis spp. in this study consume over 500 prey d−1. This exceeds their metabolic demands and suggests the observed population, on the western edge of the Gulf Stream, are likely to be actively growing and reproducing. The time required to digest prey items averaged 44 min for Ocyropsis which is faster than many, but not all, gelatinous zooplankton39,40,41. Digestion times of other gelatinous taxa span a range of times from 15 min to over 7 h at 20 °C40 and are impacted by size and number of prey per gut as well as temperature39,42,43. Digestion observations were performed at an ambient temperature of 25 °C and thus, these numbers represent a conservative estimate because the temperature of the water from which the animals were collected was 26.7–27.4 °C. Ocyropsis spp. would likely experience an increase in digestion rate with increased temperature.Digestion time was not impacted by the number of prey in the gut or by ctenophore body length. This differs from trends seen in other gelatinous taxa, such as A. aurita, M. leidyi, and B. infundibulum, where increasing body size resulted in faster digestion time39,40 and where increasing number of prey in the gut leads to longer digestion times39,40,41. In this study however, ctenophores were offered only a few copepods to ingest, thus it is likely they were not fed enough prey to satiate and slow the digestion process. Also worth considering is that the metabolic rate of O. crystallina does not appear to be affected by body size38. Though metabolic rates were not measured, this aligns with our finding that body size had no significant effect on digestion time. Analysis of in situ gut contents showed a significant positive logarithmic relationship between ctenophore length and total prey biomass per gut (Fig. 8). Individuals smaller than 20 mm in this study typically had fewer than the average number of copepods per gut (19), and larger individuals were the main driver of this relationship. This suggests that small Ocyropsis ( More

The data were recorded under the DarwinCore standard55,56 in a matrix named “Benthic biota of CIMAR-Fiordos and Southern Ice Field Cruises”58. The occurrence dataset contains direct basic information (description, scope [temporal, geographic and taxonomic], methodology, bibliography, contacts, data description, GBIF registration and citation), project details, metrics (taxonomy and occurrences classification), activity (citations and download events) and download options. The following data fields were occupied:Column 1: “occurrenceID” (single indicator of the biological record indicating the cruise and correlative record).Column 2: “basisOfRecord” (“PreservedSpecimen” for occurrence records with catalogue number of scientific collection, “MaterialCitation” for any literature record).Column 3: “institutionCode” (The acronym in use by the institution having custody of the sample or information referred to in the record).Column 4: “collectionCode” (The name of the cruise).Column 5: “catalogNumber” (The repository number in museums or correlative number).Column 6: “type” (All records entered as “text”).Column 7: “language” (Spanish, English or both).Column 8: “institutionID” (The identifier for the institution having custody of the sample or information referred to in the record).Column 9: “collectionID” (The identifier for the collection or dataset from which the record was derived).Column 10: “datasetID” (The code “CONA-benthic-biota-database” for entire database).Column 11: “recordedBy” (Author/s who recorded the original occurrence [publication source]).Column 12: “individualCount” (Number of individuals recorded).Column 13: “associatedReferences” (Publication source [report and/or paper/s] for each record).Column 14: “samplingProtocol” (The sampling gear for each record).Column 15: “eventDate” (The date-time or interval during which the record occurred).Column 16: “eventRemarks” (Comments or notes about the event).Column 17: “continent” (Location).Column 18: “country” (Location).Column 19: “countryCode” (The standard code for the country in which the location occurs).Column 20: “stateProvince” (Location, refers to the Administrative Region of Chile).Column 21: “county” (Location, refers to the Administrative Province of Chile).Column 22: “municipality” (Location, refers to the Administrative Commune of Chile).Column 23: “locality” (The specific name of the place).Column 24: “verbatimLocality” (The original textual description of the place).Column 25: “verbatimDepth” (The original description of the depth).Column 26: “minimumDepthInMeters” (The shallowest depth of a range of depths).Column 27: “maximumDepthInMeters” (The deepest depth of a range of depths).Column 28: “locationRemarks” (The name of the sample station of the cruise).Column 29: “verbatimLatitude” (The verbatim original latitude of the location).Column 30: “verbatimLongitude” (The verbatim original longitude of the location).Column 31: “verbatimCoordinateSystem” (The coordinate format for the “verbatimLatitude” and “verbatimLongitude” or the “verbatimCoordinates” of the location).Column 32: “verbatimSRS” (The spatial reference system [SRS] upon which coordinates given in “verbatimLatitude” and “verbatimLongitude” are based)Column 33: “decimalLatitude” (The geographic latitude in decimal degrees).Column 34: “decimalLongitude” (The geographic longitude in decimal degrees).Column 35: “geodeticDatum” (The spatial reference system [SRS] upon which the geographic coordinates given in “decimalLatitude” and “decimalLongitude” was based).Column 36: “coordinateUncertaintyInMeters” (The horizontal distance from the given “decimalLatitude” and “decimalLongitude” describing the smallest circle containing the whole of the location).Column 37: “georeferenceRemarks” (Notes about the spatial description determination).Column 38: “identifiedBy” (Responsible for recording the original occurrence [publication source]).Column 39: “dateIdentified” (The date-time or interval during which the identification occurred.)Column 40: “identificationQualifier” (A taxonomic determination [e.g., “sp.”, “cf.”]).Column 41: “scientificNameID” (An identifier for the nomenclatural details of a scientific name).Column 42: “scientificName” (The name of species or taxon of the occurrence record).Column 43: “kingdom” (The scientific name of the kingdom in which the taxon is classified).Column 44: “phylum” (The scientific name of the phylum or division in which the taxon is classified).Column 45: “class” (The scientific name of the class in which the taxon is classified).Column 46: “order” (The scientific name of the order in which the taxon is classified).Column 47: “family” (The scientific name of the family in which the taxon is classified).Column 48: “genus” (The scientific name of the genus in which the taxon is classified).Column 49: “subgenus” (The scientific name of the subgenus in which the taxon is classified).Column 50: “specificEpithet” (The name of the first or species epithet of the “scientificName”).Column 51: “infraspecificEpithet” (The name of the lowest or terminal infraspecific epithet of the “scientificName”).Column 52: “taxonRank” (The taxonomic rank of the most specific name in the “scientificName”).Column 53: “scientificNameAuthorship” (The authorship information for the “scientificName” formatted according to the conventions of the applicable nomenclatural Code).Column 54: “verbatimIdentification” (A string representing the taxonomic identification as it appeared in the original record).The information sources (see Fig. 2b) provided a total of 107 publications (22 cruise reports and 85 scientific papers; see Fig. 2c). Nineteen of the 22 cruise reports reviewed provided species occurrence records8,28,29,30,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46, one provided qualitative or descriptive data, with no recorded occurrences31, and two did not provide information on benthic biota (CIMAR-9 and −23 cruises). Of all the scientific papers reviewed, 74 provided records of species occurrences (Table 2), while 11 did not provide any record, as they were data without occurrences of geographically referenced species or with descriptive or qualitative information: Foraminifera59,60,61,62, Annelida63,64,65,66, Fishes67, Mollusca68 and Echinodermata69. The phyla with the highest number of publications were the following: Annelida (present in 18 reports and 21 papers), Mollusca (in 14 and 20), Arthropoda (in 10 and 18), Echinodermata (in 10 and 9), Chordata (in 10 and 9) and Foraminifera (in 4 and 10).Table 2 Publications with >100 occurrences, indicating the main recorded taxa.Full size tableThe information registry includes data on occurrences and number of individuals for 8,854 records (files in the database), representing 1,225 species (Fig. 3). The main taxa in terms of occurrence and number of species were Annelida (mainly Polychaeta), Foraminifera, Mollusca and Arthopoda (mainly Crustacea), together accumulating ~70% of total occurrences and ~73% of the total species (Fig. 3). The large number of recorded occurrences of Myzozoa (10%) should be highlighted, which, however, only represent about 32 species. Echinodermata represented ~8% of occurrences and 7% of species.Fig. 3Occurrences and total species by taxon, considering large taxonomic groups of the benthic biota recorded in the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences and species are represented in parentheses.Full size imageThe cruises with the highest number of occurrences were CIMAR-2 (with 1,424), followed by CIMAR-8 (1,040) and CIMAR-16 (813) (Fig. 4). Three dominant taxonomic groups were recorded in most cruises, except for cruises CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 (Fig. 4). The cruises with the highest number of species recorded were CIMAR-2 (with 335), CIMAR-3 (328) and CIMAR-8 (323) (Fig. 5). Three or fewer dominant taxonomic groups were recorded only in the CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 cruises (Fig. 5).Fig. 4Total occurrences and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences per dominant taxon are represented in parentheses.Full size imageFig. 5Total species and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of species per dominant taxon are represented in parentheses.Full size imageThe latitudinal bands 42°S and 45°S are those with the highest number of occurrences (Fig. 6), while the 56°S and 46°S bands had the fewest. The highest number of species was recorded in the 52°S and 50°S latitudinal bands, while, as with the occurrences, the lowest values corresponded to the 56°S and 46°S latitudinal bands (Fig. 6).Fig. 6Occurrences and number of species recorded by latitudinal band from the CIMAR 1 to 25 and CDHS-1995 cruises. SEP: South-eastern Pacific.Full size image More

This paper describes the complexity of gravitational fractals in terms of global and local dimensions. They are presented in Table 1.Table 1 Global and local dimensions of gravitational fractals and attraction basins.Full size tableThe fractal in hexagonal CPT space, shown in Fig. 1, has a very rich structure, and therefore its characterization by means of fractal dimensions requires two approaches: (1) a global approach treating the fractal as a complex whole and (2) a local approach which allows us to determine the dimension of its fragments which are particularly interesting from a research perspective (see also Table 1). In the subsequent part of the paper, the results obtained are presented and interpreted according to the division in the table.Global dimension of boundaries of gravity attraction basinsTwo types of fractal dimensions have been thus far used in this analysis, i.e., the box and ruler dimensions. Figure 3 shows the distribution of the values of these dimensions determined for the boundaries of attraction as a function of space friction μ.Figure 3Comparison of the variability of the global ruler and box dimensions. Legend: The edge of all attraction basins is a function of the μ coefficient; 1–edges of all basins, 2–entire basins.Full size imageFigure 3 empirically confirms a fact known from chaos theory that whenever a fractal represents full chaos, the ruler dimension may be greater than 2 (Peitgen et al.33, 192–209), whereas the box dimension never exceeds this extreme value. Clearly, for a certain value of μ (in this case μ = 0.19), the numerical values of both types of dimensions are identical.In the bottom part of Fig. 3, line 1 illustrates the variability of the shapes of the attraction basins of individual cities depending on the value of μ, i.e., space resistance. The initially extremely complex shapes of the boundaries are smoothed to take the form of straight lines in the case of a large value of μ (μ = 0.52).In turn, line 2 illustrates not only the boundaries of the attraction basins, but also their internal structure. Clearly, the initially chaotic impacts of individual cities on the agent (μ = 0.005) are gradually smoothed out, so that in the final stage of the process they fully stabilize. This means that each city has a geometrically identical basin of attraction. Hence, if the agent is in the attraction basin of city 1 (purple color), it will always be attracted only by that city. This rule also applies to the other cities. It is obvious that the random process occurring at μ = 0.09 is then replaced by a strictly deterministic one. When chaos becomes complete order (Banaszak et al.15, the numerical values of both types of dimensions appear to stabilize at the level of 1.Global dimension of the boundary of each separate attraction basinFigure 1 also shows the geometric image of the attraction basins of individual cities. They were almost identical, and therefore also the fractal dimensions of the boundaries of these basins must match. The validity of this proposition is confirmed by Fig. 4. Six lines representing the distribution of the fractal dimension of the boundaries of the six basins coincide with almost full accuracy. Further analysis of Fig. 4 allows us to infer the conclusion that there is almost total chaos at the value db = 1.9021 (μ = 0.005). On the other hand, as space resistance increases to the value of μ = 0.22, there is a rapid decrease in the value of the fractal dimension of the boundary of each basin to the level of 1.2628; when μ = 0.34, then db = 1.2382. In that case, the value of the fractal dimension stabilizes, and at μ = 0.46, db = 1.2444 and finally for μ = 0.52, db reaches the value of 1.0412. The icons presented in Fig. 4 in lines 1 and 2 have slightly different structures than the icons in Fig. 3, due to different values of μ in certain cases.Figure 4The box dimension of the edges of the attraction basins depending on the μ coefficient (separately for each attractor). Legend: 1–boundaries of single attraction basins, 2–entire basins.Full size imageThe global dimension of the attraction basin of each city as an irregular geometric figureThe full symmetry of the basins of attraction of individual cities can be disturbed by the shape of the geometric figure on which the deterministic fractal is modeled. Such a situation occurs in the present case. Due to the fact that the fractal in Fig. 1 is formed on the surface of a square, the final basins of attraction of cities 1, 3, 4 and 6 are obviously larger than those of cities 2 and 5. Of course, these differences do not occur when considering the surface inside the hexagon.In Fig. 5, the line marked in black color represents the average value of the fractal dimension of the basins of attraction of individual cities, the value of which is (overline{{d }_{b}}=1.77). It can be seen that at very high values of the fractal dimension in the range (1.750, 1.775), there are db oscillations around this line. This is precisely the effect of modeling the fractal on the surface of the square, rather than the properties of this fractal. Therefore, (overline{{d }_{b}}=1.77) should be regarded as the global dimension of the basin of attraction (of each city) treated as an irregular figure.Figure 5Box dimension of the attraction basins as a geometric irregular figure in the gravitational fractal. Legend: 1-basins of the first city, 2-basins of the second city, 7-basins of all cities.Full size imageLocal dimensions of the boundary of the selected characteristic fragmentsFigure 6 presents fractal dimensions, with the Box and Ruler as functions of μ, and the boundaries of the attraction basins of individual cities occurring in all fragments A, …, E.Figure 6Distribution of the values of fractal dimensions of the boundaries of the attraction basins identified in selected fragments of a fractal; Legend: (A, D)-fragments marked in Fig. 1.Full size imageIt is evident that the structures of Fig. 6 (Box and Ruler) are almost identical. This means that, as has been stated earlier, when describing complex fractal objects, it does not really matter which type of dimension is used.Of interest here is the variability of the structure of both figures along with the increase in the value of the parameter μ. Fragments A, …, E (see Fig. 1) are characterized by high complexity, i.e. the intertwining attraction basins of the individual attractors (cities). This observation is confirmed by the numerical results of both fractal dimensions whose values are in the range (1.68–1.82). To illustrate the spatial complexity of these fragments, and thus their dimensions, by way of example, two fractal fragments are considered below: fragments A and D (see also Fig. 7).Figure 7Box dimension of the edge of each gravitation basin in A and D. Legend: The icons show the variability of the fragments A and D due to the share of the attraction basins of individual cities (3, 4 and 6).Full size imageFigure 6 offers important conclusions concerning the organization of social and economic life in the geographical area surrounding individual cities (attractors).

1.

Out of all the separated fragments, only in fragment A do we find the attraction basins of all the cities intertwined across the entire range of variation μ, i.e. (0.00–0.48). Hence, the graph of fractal dimension (db) (blue line) as a function of μ is continuous, and when the resistance of space is the greatest (μ = 0.48), the fractal dimension d = 1.00. This means that chaos has given way to total order, and fragment A has been symmetrically divided between cities 1 and 6. Hence, there are two colors left, namely red and purple.

2.

A similar situation occurs in the case of fragment D (yellow line), where the attraction basins of individual cities intertwine continuously within the range: 0.00 ≤ μ ≤ 0.46. Beyond the value of 0.46, the entire fragment D is filled with purple: the closest city 1 dominates it.

The research conducted here also confirms the conclusions presented in previous works by Banaszak et al.15,16 concerning the transformation of chaos into spatial order, which means the stabilization of permanent dominance, usually of one attractor (city). Thus, with regard to fragments A and D, in fragment A there is a constant dominance (in half of the area) of cities 1 and 6, from the limit value of μ = 0.24 onward. In the case of fragment D, beginning with the value of μ = 0.36, only city 1 dominates (purple). That is, in the final phase of establishing the order in spatial interactions in the arrangement of areas A and D, the role of the dominant attractor (city) is played by city 1 (purple).Due to the symmetry of Fig. 1, similar effects can be observed in other parts of this fractal, located symmetrically in relation to A, …, E (see Supplementary Material).Figures 1 and 6 confirm the findings, known in the theory of city development, that urban (and other) centers rise in the hierarchy (or their rank decreases), depending on the external and internal factors conditioning their development. In the model used in this study, the parameter μ represents external factors (space resistance). If μ values are low, all cities are attractive from the point of view of spatial interactions and create their own but symmetrical basins of attraction. When the resistance of space increases, one city becomes the dominant center, and its basin of attraction is a uniform compact isotropic surface.However, this is not a simple mechanism, since, as has been demonstrated by simulation experiments described in this paper, within a certain range of μ values, another city (attractor) may dominate the others during chaotic interactions. The dynamic history of urban development confirms this observation, for example, in relation to historical capitals of some countries that have lost their functions as administrative capitals.Local dimension of the boundary of each attraction basin in a selected fragment of a fractalFragments A, …, E (Fig. 1 and the Supplementary Material) consist of mutually intertwined basins of attraction (six cities) whose boundaries with complicated courses have a fractal dimension, e.g. a box dimension.Figure 7(fragment A) shows the distribution of db as a function of μ in this fragment. In the case of total internal chaos, the fractal dimension of the boundaries of the attraction basins of all cities is identical and amounts to 1.9152. A clear differentiation of db is noticeable from μ = 0.1 onward. It should also be noted that orange and blue, red and purple, yellow and green lines mutually coincide. The red–purple line tend towards db = 1 as μ increases. However, orange, blue, yellow and green lines reach a value of db = 0.The fractal dimension db = 1.0 is most closely represented by the blue line (city 2), then the red line (city 6) and the purple line (city 1). Since these lines almost coincide, and the red and purple lines are the last to reach the value db = 1, at μ = 0.48, fragment A is symmetrically covered in red and purple. Therefore, with very high spatial resistance, fragment A is dominated by two cities, namely by 1 and 6.In turn, Fig. 7(fragment D) illustrates the variability of the fractal dimension of boundaries of the attraction basins in this fragment. This dimension depends on the complexity of the mosaic patterns formed in this fragment, with varying μ values. When the values of μ are close to zero, all cities contribute to filling the space of fragment D. When μ = 0.18, city 1 (purple color) falls out of the competition for space, but only up to the value of μ = 0.24, when it starts to compete again with other cities. From the point of view of spatial interactions, in the final phase of this process (μ = 0.44), city 2 (blue) and city 6 (red) dominate to a small extent, because cities 3, 4 and 6, starting from μ = 0.3, do not play any role in fragment D.Figure 7 shows that the value μ = 0.3 is a characteristic point. It is a locus where all the curves representing the attraction basins of individual cities meet. As has already been stated, three of them lose their influence over the space of fragment D.Local dimensions of parts of the attraction basins treated as an irregular geometric figureIn each of the selected fragments A, …, E, some of the boundaries of the attraction basins of individual cities are distributed differently. They create certain holes in the form of irregularly colored mosaic patterns that have a certain fractal dimension. To present its variability, fragments A and D were used again. Figure 8 shows the distribution of db values depending on the value of μ.Figure 8Local dimensions of parts of the attraction basins treated as an irregular geometric figure in (A) and (D). Legend: The icons illustrate the variability of the shape of some of the attraction basins of individual cities in fragment (A) and (D) for cities 3, 4 and 6.Full size imageThe function has several characteristic points. Up to the value of μ = 0.04, attraction basins show a jumble in which no predominant color or shape can be identified. The fractal dimension is then: db = 1.7697. From this value onwards, where μ = 0.042, the interior of fragment A becomes increasingly ordered. With a value of μ = 0.125, the city’s attraction basins 3 and 4 begin to disappear in fragment A. The same happens to the city attraction basins 2 and 5 for the value of μ = 0.24.The final effect of the increase in space resistance (with μ = 0.50) leads to the filling of fragment A with two colors, i.e., purple and red. This means that cities 1 and 6, have won the competition for the space of fragment A. In this case, the fractal dimensions db equal 1.90.Figure 8 presents the variability of the fractal dimension and the effects of the competition for space between cities in fragment D. As is the case in fragment A and all others, i.e. B, C and E (see the Annex with Supplementary Material), the intertwined attraction basins are represented by the area consisting of an endless number of differently colored dots. Hence, up to the value of μ = 0.042, fragment D is dominated by pure spatial chaos that extends over its entire area. It is characterized by the fractal dimension db = 1.7697. This means that with an increase in the value of μ, for the emergence of an irregular shape of a geometric figure, chaos must be accompanied by an increase in the value of the fractal dimension. Its limiting value is number 2. Then, spatial dominance is usually gained by one city and the examined fragment is filled with one color (‘the winner takes it all’).This is precisely the situation in Fig. 8 where city 1 (purple color) has apparently won the competition. Since this color fills area D completely, we find the plausible result db = 2.0. More

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