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IBM IBM Commerce Solutions Order

IBM Thailand Promotes AI, Blockchain as a way to Create Regional income Hub | killexams.com real Questions and Pass4sure dumps

The Thai fork of U.S. world IT company IBM will promote blockchain and synthetic intelligence (AI) in an endeavor to turn the nation into a tall revenue hub in the surrounding location, English-language daily information outlet The Bangkok submit stories Wednesday, Nov. 14.

in line with the article, IBM goes to advertise blockchain in collaboration with the country’s apposite fiscal institution, fiscal institution of Thailand. A fresh survey with the aid of IDC-IBM shows that world spending on blockchain will attain $9.7 billion by means of 2021.

additionally, IBM is additionally discussing the possibility of blockchain schooling in endemic colleges and universities, aiming to supply satisfactory member of the workforce for the trade in the nearest future.

IBM concurrently plans to exhaust Watson AI, a computer system geared up with AI and able to answering questions by using its database, to locate insights for distinctive areas within the nation, comparable to retail, schooling, finance, company, and power.

Thai officers acquire currently begun applying blockchain in different areas. In early October,  the Thai Ministry of Commerce revealed it began conducting feasibility stories on the exhaust of blockchain in copyright, agriculture, and exchange finance. And later in November, the endemic earnings department announced its plans to tune tax payments the usage of blockchain and maсhine gaining learning of.

As Cointelegraph up to now mentioned, IBM is additionally actively merchandising blockchain expertise, elaborating decentralized solutions for distinctive areas in a great number of patents. In late August, the variety of patents filed via IBM comprised 89, which made the U.S. enterprise probably the most biggest gamers within the area, surpassed simplest by China’s Alibaba with its 90 applications.

IBM has filed several greater blockchain-related patents considering the fact that then, together with patents for a blockchain-driven platform for scientific research and an additional for the decentralized storage of depended on places for augmented fact (AR) games.

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Cloud Computing: IBM Acquires Sterling Commerce | killexams.com real Questions and Pass4sure dumps

via PR Newswire

Article rating:

August 27, 2010 02:forty five PM EDT

Reads:

20,162

IBM on Friday introduced the closing of its acquisition of Sterling Commerce. The enterprise expands IBM's aptitude to support customers speed up their interactions with customers, companions and suppliers via dynamic company networks the usage of both on-premise or cloud birth fashions.

groups are trying to find the way to create greater quick-witted networks of enterprise companions, customers and suppliers so as to extend efficiency and profitability. These interactions are increasing dramatically due to the proliferation of electronic traffic transactions, from banks replacing transaction records and producers sourcing raw substances electronically, to dealers automating inventory replenishment and managing orders online.

Sterling Commerce offers software for move-channel commerce and integration of customer, associate and company networks throughout a tall compass of industries. The aggregate of IBM and Sterling Commerce allows for the mixing of key company strategies across channels and among buying and selling companions - from advertising and selling to order administration and achievement.

"We now proffer a complete platform for multi-business company transactions," said Craig Hayman, regularly occurring supervisor, IBM industry solutions. "In combination with IBM's present offerings, Sterling Commerce, Coremetrics and Unica are expanding IBM's means to mitigate businesses automate, maneuver and accelerate core company methods across advertising and marketing, promoting, order administration and fulfillment."

With the acquisition of Sterling Commerce, IBM advances its skill to mitigate purchasers combine and automate company techniques, leading to improved demand generation, client adventure and achievement. the usage of the mixed applied sciences of IBM and Sterling Commerce, valued clientele acquire the flexibleness to maneuver these tactics - and their networks of enterprise companions - via public or deepest cloud computing environments.

on account that IBM introduced its intent to acquire the enterprise in may additionally, Sterling Commerce has seen persisted momentum with customers in each its enterprise integration and commerce solutions companies. Sterling Commerce currently introduced that Hostess manufacturers has implemented its B2B integration solutions each on-premise and as a carrier to improve Hostess' provide chain efficiency. In June, Cengage gaining learning of went are animate with the newest edition of Sterling Multi-Channel selling to select abilities of latest market segmentation and better promotions functionality that extend the customer adventure of its award-profitable web site, CengageBrain.com.

"We view the IBM acquisition of Sterling Commerce as a auspicious circulate," observed Charles Qian, supervisor of eCommerce techniques at Cengage discovering, a leading international issuer of ingenious educating, studying and research solutions. "Our fresh implementation turned into seamless, and achieved beneath a tight timeframe. I are expecting the impressive options they acquire got from Sterling Commerce will best be better beneath IBM."

besides bettering IBM's integration and commerce choices, Sterling Commerce application too enhances IBM's trade-focused utility together with the enterprise's frameworks supporting the retail, manufacturing, communications, health care and banking industries.

greater than 18,000 international customers rely on Sterling Commerce's choices, including gigantic organizations reminiscent of Boston Market, Honeywell, Monsanto and Pitney Bowes. backyard the U.S., Sterling Commerce's customer checklist includes main manufacturers fancy Toshiba and excellent retailers comparable to Auchan and John Lewis.

The acquisition builds on IBM's turning out to be portfolio of trade utility solutions designed to assist organizations automate, maneuver and accelerate core enterprise procedures throughout advertising, promoting, ordering and fulfillment. IBM's fresh acquisitions of Sterling Commerce and Coremetrics and the supposed acquisition of Unica will extend the enterprise's means to aid valued clientele' needs during this turning out to be market.

With the closing of this acquisition about 2,500 Sterling Commerce employees be a allotment of IBM. consistent with IBM's software strategy, IBM will continue to pilot Sterling Commerce's purchasers while permitting them to select capabilities of the broader IBM portfolio.

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P8010-004 IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

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P8010-004 exam Dumps Source : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

Test Code : P8010-004
Test designation : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1
Vendor designation : IBM
: 30 real Questions

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Uniqlo’s unusual Mobile e-Commerce traffic Model | killexams.com real questions and Pass4sure dumps

By Si Chen

Article Rating:

February 20, 2015 01:26 PM EST

Reads:

318

Watch this video — it might just be the future of mobile commerce:

Did you notice

  • A wintry mobile app
  • Creating DIY art
  • Did you too notice that it’s a traffic model without

  • Upfront design
  • Inventory
  • Advertising
  • Uniqlo is not just thinking, Gee how enact they acquire more Instagram followers to sell the selfsame outmoded T-shirts?

    They’re creating a whole unusual traffic model by taking the mobile platform to its analytic conclusion.  Their app, which does a lot more than Instagram’s simple filters, works hand-in-hand with a traffic model to turn a T-shirt (commodity) into your own work of knack (priceless.)

    If tiny Instagram could build a billion-dollar traffic by turning the mobile phone into the ultimate device of self-expression, why couldn’t Uniqlo…or you?

    Read the original blog entry...

    Si Chen is the founder of Open Source Strategies, Inc. and Project Manager for opentaps Open Source ERP + CRM (www.opentaps.org).

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    Modeled larval connectivity of a multi-species reef fish and invertebrate assemblage off the coast of Moloka‘i, Hawai‘i | killexams.com real questions and Pass4sure dumps

    Introduction

    Knowledge of population connectivity is necessary for effectual management in marine environments (Mitarai, Siegel & Winters, 2008; Botsford et al., 2009; Toonen et al., 2011). For many species of marine invertebrate and reef fish, dispersal is mostly limited to the pelagic larval life stage. Therefore, an understanding of larval dispersal patterns is captious for studying population dynamics, connectivity, and conservation in the marine environment (Jones, Srinivasan & Almany, 2007; Lipcius et al., 2008; Gaines et al., 2010; Toonen et al., 2011). Many coastal and reef species acquire a bi-phasic life history in which adults display limited geographic compass and lofty site fidelity, while larvae are pelagic and highly mobile (Thorson, 1950; Scheltema, 1971; Strathmann, 1993; Marshall et al., 2012). This life history strategy is not only common to sessile invertebrates such as corals or limpets; many reef fish species acquire been shown to acquire a home compass of <1 km as adults (Meyer et al., 2000; Meyer, Papastamatiou & Clark, 2010). Depending on species, the mobile planktonic stage can ultimate from hours to months and has the potential to transport larvae up to hundreds of kilometers away from a site of origin (Scheltema, 1971; Richmond, 1987; Shanks, 2009). learning of larval dispersal patterns can be used to inform effectual management, such as marine spatial management strategies that sustain source populations of breeding individuals capable of dispersing offspring to other areas.

    Both biological and physical factors impact larval dispersal, although the relative importance of these factors is likely variable among species and sites and remains debated (Levin, 2006; Paris, Chérubin & Cowen, 2007; Cowen & Sponaugle, 2009; White et al., 2010). In situ data on pelagic larvae are sparse; marine organisms at this life stage are difficult to capture and identify, and are typically establish in low densities across great areas of the open ocean (Clarke, 1991; Wren & Kobayashi, 2016). A variety of genetic and chemistry techniques acquire therefore been developed to assess larval connectivity (Gillanders, 2005; Leis, Siebeck & Dixson, 2011; Toonen et al., 2011; Johnson et al., 2018). Computer models informed by domain and laboratory data acquire too become a valuable tool for estimating larval dispersal and population connectivity (Paris, Chérubin & Cowen, 2007; Botsford et al., 2009; Sponaugle et al., 2012; Kough, Paris & Butler IV, 2013; Wood et al., 2014). Individual-based models, or IBMs, can incorporate both biological and physical factors known to influence larval movement. Pelagic larval duration (PLD), for example, is the amount of time a larva spends in the water column before settlement and can vary widely among or even within species ( Toonen & Pawlik, 2001). PLD affects how far an individual can be successfully transported by ocean currents, and so is expected to directly influence connectivity patterns (Siegel et al., 2003; Shanks, 2009; Dawson et al., 2014). In addition to PLD, adult reproductive strategy and timing (Carson et al., 2010; Portnoy et al., 2013), fecundity (Castorani et al., 2017), larval mortality (Vikebøet al., 2007), and larval developmental, morphological, and behavioral characteristics (Paris, Chérubin & Cowen, 2007) may each and every play a role in shaping connectivity patterns. Physical factors such as temperature, bathymetry, and current direction can too substantially influence connectivity (Cowen & Sponaugle, 2009). In this study, they incorporated both biotic and abiotic components in an IBM coupled with an oceanographic model to forecast fine-scale patterns of larval exchange around the island of Moloka‘i in the Hawaiian archipelago.

    The main Hawaiian Islands are located in the middle of the North Pacific Subtropical Gyre, and are bordered by the North Hawaiian Ridge current along the northern coasts of the islands and the Hawaii Lee Current along the southern coasts, both of which dash east to west and are driven by the current easterly trade winds (Lumpkin, 1998; Friedlander et al., 2005). The Hawai‘i Lee Countercurrent, which runs along the southern perimeter of the chain, flows west to east (Lumpkin, 1998). The pattern of mesoscale eddies around the islands is intricate and varies seasonally (Friedlander et al., 2005; Vaz et al., 2013).

    Hawaiian marine communities face unprecedented pressures, including coastal development, overexploitation, disease, and increasing temperature and acidification due to climate change (Smith, 1993; Lowe, 1995; Coles & Brown, 2003; Friedlander et al., 2003; Friedlander et al., 2005; Aeby, 2006). Declines in Hawaiian marine resources argue for implementation of a more holistic approach than traditional single-species maximum sustainable yield techniques, which acquire proven ineffective (Goodyear, 1996; Hilborn, 2011). There is a generic movement toward the exhaust of ecosystem-based management, which requires learning of ecosystem structure and connectivity patterns to establish and manage marine spatial planning areas (Slocombe, 1993; Browman et al., 2004; Pikitch et al., 2004; Arkema, Abramson & Dewsbury, 2006). Kalaupapa National Historical Park is a federal marine protected region (MPA) located on the north shore of Moloka‘i, an island in the Maui Nui intricate of the Hawaiian archipelago, that includes submerged lands and waters up to 1 4 mile offshore (NOAA, 2009). At least five IUCN red-listed coral species acquire been identified within this area (Kenyon, Maragos & Fenner, 2011), and in 2010 the Park showed the greatest fish biomass and species diversity out of four Hawaiian National Parks surveyed (Beets, Brown & Friedlander, 2010). One of the major benefits expected of MPAs is that the protected waters within the region provide a source of larval spillover to other sites on the island, seeding these areas for commercial, recreational, and subsistence fishing (McClanahan & Mangi, 2000; Halpern & Warner, 2003; Lester et al., 2009).

    In this study, they used a Lagrangian particle-tracking IBM (Wong-Ala et al., 2018) to simulate larval dispersal around Moloka‘i and to assess the larval exchange among sites at the scale of an individual island. They acquire parameterized their model with biological data for eleven species covering a breadth of Hawaiian reef species life histories (e.g., habitat preferences, larval behaviors, and pelagic larval durations, Table 1), and of interest to both the local community and resource managers. Their goals were to examine patterns of species-specific connectivity, characterize the location and relative magnitude of connections around Moloka‘i, portray sites of potential management relevance, and address the question of whether Kalaupapa National Historical Park provides larval spillover for adjacent sites on Moloka‘i, or connections to the adjacent islands of Hawai‘i, Maui, O‘ahu, Lana‘i, and Kaho‘olawe.

    Table 1:

    Target taxa selected for the study, based on cultural, ecological, and/or economic importance.

    PLD = pelagic larval duration. Short dispersers (3–25 day minimum PLD) in white, medium dispersers (30–50 day minimum PLD) in light gray, and long dispersers (140–270 day minimum PLD) in dim gray. Spawn season and timing from traditional ecological learning shared by cultural practitioners on the island. Asterisk indicates that congener-level data was used. Commonname Scientific name Spawn type # of larvae spawned Spawningday of year Spawning hour of day Spawning moon phase Larval depth (m) PLD (days) Habitat ’Opihi/ Limpet Cellana spp. Broadcast1 861,300 1–60 & 121–181 – New 0–5 3–181,2 Intertidal1 Ko’a/ Cauliflower coral Pocillopora meandrina Broadcast3 1,671,840 91–151 07:15–08:00 Full 0–54 5–90*5 Reef He’e/ Octopus Octopus cyanea Benthic6 1,392,096 1–360 – – 50–100 216 Reef, rubble7 Moi/ Pacific threadfin Polydactylus sexfilis Broadcast 1,004,640 152–243 – – 50–1008 259 Sand10 Uhu uliuli/ Spectacled parrotfish Chlorurus perspicillatus Broadcast 1,404,792 152–212 – – 0–120*11 30*12 Reef10 Uhu palukaluka/ Reddlip parrotfish Scarus rubroviolaceus Broadcast 1,404,792 152–212 – – 0–120*11 30*12 Rock, reef10 Kumu/ Whitesaddle Goatfish Parupeneus porphyreus Broadcast 1,071,252 32–90 – – 0–50*11 41–56*12 Sand, rock, reef10 Kole/ Spotted surgeonfish Ctenochaetus strigosus Broadcast 1,177,200 60–120 – – 50–10011 50*12 Rock, reef, rubble10 ‘Ōmilu/ Bluefin trevally Caranx melampygus Broadcast 1,310,616 121–243 – – 0–80*11 140*13,14 Sand, reef10 Ulua/ Giant trevally Caranx ignoblis Broadcast 1,151,040 152–243 – Full 0–80*11 14013,14 Sand, rock, reef10 Ula/ Spiny lobster Panulirus spp. Benthic15 1,573,248 152–243 – – 50–10016 27017 Rock, pavement16 Methods Circulation model

    We selected the hydrodynamic model MITgcm, which is designed for the study of dynamical processes in the ocean on a horizontal scale. This model solves incompressible Navier–Stokes equations to portray the motion of viscous fluid on a sphere, discretized using a finite-volume technique (Marshall et al., 1997). The one-km resolution MITgcm domain for this study extends from 198.2°E to 206°E and from 17°N to 22.2°N, an region that includes the islands of Moloka‘i, Maui, Lana‘i, Kaho‘olawe, O‘ahu, and Hawai‘i. While Ni‘ihau and southern Kaua’i too drop within the domain, they discarded connectivity to these islands because they prevaricate within the 0.5° confine zone of the current model. confine conditions are enforced over 20 grid points on each and every sides of the model domain. Vertically, the model is divided into 50 layers that extend in thickness with depth, from five m at the surface (0.0–5.0 m) to 510 m at the groundwork (4,470 –4,980 m). Model variables were initialized using the output of a Hybrid Coordinate Ocean Model (HYCOM) at a horizontal resolution of 0.04° (∼four km) configured for the main Hawaiian Islands, using the generic Bathymetric Chart of the Oceans database (GEBCO, 1/60°) (Jia et al., 2011).

    The simulation runs from March 31st, 2011 to July 30th, 2013 with a temporal resolution of 24 h and shows seasonal eddies as well as persistent mesoscale features (Fig. S1). They enact not include tides in the model due to temporal resolution. Their model term represents a neutral ocean state; no El Niño or La Niña events occurred during this time period. To ground-truth the circulation model, they compared surface current output to real-time trajectories of surface drifters from the GDP Drifter Data Assembly hub (Fig. S2) (Elipot et al., 2016), as well as other current models of the region (Wren et al., 2016; Storlazzi et al., 2017).

    Biological model

    To simulate larval dispersal, they used a modified version of the Wong-Ala et al. (2018) IBM, a 3D Lagrangian particle-tracking model written in the R programming language (R Core Team, 2017). The model takes the aforementioned MITgcm current products as input, as well as shoreline shapefiles extracted from the complete resolution NOAA Global Self-consistent Hierarchical High-resolution Geography database, v2.3.0 (Wessel & Smith, 1996). Their model included 65 land masses within the geographic domain, the largest being the island of Hawai‘i and the smallest being Pu‘uki‘i Island, a 1.5-acre islet off the eastern coast of Maui. To model depth, they used the one arc-minute-resolution ETOPO1 bathymetry, extracted using the R package ‘marmap’ (Amante & Eakins, 2009; Pante & Simon-Bouhet, 2013).

    Each species was simulated with a separate model run. Larvae were modeled from spawning to settlement and were transported at each timestep (t = 2 h) by advection-diffusion transport. This transport consisted of (1) advective displacement caused by water flow, consisting of east (u) and north (v) velocities read from daily MITgcm files, and (2) additional random-walk displacement, using a diffusion constant of 0.2 m2/s−1 (Lowe et al., 2009). Vertical velocities (w) were not implemented by the model; details of vertical larval movement are described below. Advection was interpolated between data points at each timestep using an Eulerian 2D barycentric interpolation method. They chose this implementation over a more computationally intensive interpolation system (i.e., fourth-order Runge–Kutta) because they did not keep a contrast at this timestep length. Biological processes modeled include PLD, reproduction timing and location, mortality, and ontogenetic changes in vertical distribution; these qualities were parameterized via species-specific data obtained from previous studies and from the local fishing and management community (Table 1).

    Larvae were released from habitat-specific spawning sites and were considered settled if they fell within a roughly one-km contour around reef or intertidal habitat at the quit of their pelagic larval duration. Distance from habitat was used rather than water depth because Penguin Bank, a relatively shallow bank to the southwest of Moloka‘i, does not picture suitable habitat for reef-associated species. PLD for each larva was a randomly assigned value between the minimum and maximum PLD for that species, and larvae were removed from the model if they had reached their PLD and were not within a settlement zone. No data on pre-competency term were available for their study species, so this parameter was not included. Mortality rates were calculated as larval half-lives; e.g., one-half of each and every larvae were assumed to acquire survived at one-half of the maximum PLD for that species (following Holstein, Paris & Mumby, 2014). Since their focus was on potential connectivity pathways, reproductive rates were calibrated to allow for saturation of possible settlement sites, equating from ∼900,000 to ∼1,7000,000 larvae released depending on species. Fecundity was therefore derived not from biological data, but from computational minimums.

    Development, and resulting ontogenetic changes in behavior, is specific to the life history of each species. Broadcast-spawning species with weakly-swimming larvae (P. meandrina and Cellana spp., Table 1) were transported as passive particles randomly distributed between 0–5 m depth (Storlazzi, Brown & Field, 2006). Previous studies acquire demonstrated that fish larvae acquire a lofty degree of control over their vertical position in the water column (Irisson et al., 2010; Huebert, Cowen & Sponaugle, 2011). Therefore, they modeled broadcast-spawning fish species with a 24-hour passive buoyant facet to simulate eggs pre-hatch, followed by a pelagic larval facet with a species-specific depth distribution. For C. ignoblis, C. melampygus, P. porphyreus, C. perspicillatus, and S. rubroviolaceus, they used genus-level depth distributions (Fig. S3) obtained from the 1996 NOAA ichthyoplankton vertical distributions data report (Boehlert & Mundy, 1996). P. sexfilis and C. strigosus larvae were randomly distributed between 50–100 m (Boehlert, Watson & Sun, 1992). Benthic brooding species (O. cyanea and Panulirus spp.) enact not acquire a passive buoyant phase, and thus were released as larvae randomly distributed between 50–100 m. At each time step, a larva’s depth was checked against bathymetry, and was assigned to the nearest available layer if the species-specific depth was not available at these coordinates.

    For data-poor species, they used congener-level estimates for PLD (see Table 1). For example, there is no assess of larval duration for Caranx species, but in Hawai‘i peak spawning occurs in May–July and peak recruitment in August–December (Sudekum, 1984; Longenecker, Langston & Barrett, 2008). In consultation with resource managers and community members, a PLD of 140 days was chosen pending future data that indicates a more accurate pelagic period.

    Habitat selection

    Spawning sites were generated using data from published literature and modified after input from endemic Hawaiian cultural practitioners and the Moloka‘i fishing community (Fig. 1). Species-specific habitat suitability was inferred from the 2013–2016 Marine Biogeographic Assessment of the Main Hawaiian Islands (Costa & Kendall, 2016). They designated coral habitat as areas with 5–90% coral cover, or ≥1 site-specific coral species richness, for a total of 127 spawning sites on Moloka‘i. Habitat for reef invertebrates followed coral habitat, with additional sites added after community feedback for a total of 136 sites. Areas with a predicted reef fish biomass of 58–1,288 g/m2 were designated as reef fish habitat (Stamoulis et al., 2016), for a total of 109 spawning sites. Sand habitat was designated as 90–100% uncolonized for a total of 115 sites. Intertidal habitat was designated as any rocky shoreline region not covered by sand or mud, for a total of 87 sites. Number of adults was assumed equal at each and every sites. For regional analysis, they pooled sites into groups of two to 11 sites based on benthic habitat and surrounding geography (Fig. 1A). Adjacent sites were grouped if they shared the selfsame benthic habitat classification and current wave direction, and/or were allotment of the selfsame reef tract.

    Figure 1: Spawning sites used in the model by species. (A) C. perspicillatus, S. rubroviolaceus, P. porphyreus, C. strigosus, C. ignoblis, and C. melampygus, n = 109; (B) P. meandrina, n = 129;(C) O. cyanea and Panulirus spp., n = 136; (D) P. sexfilis, n = 115; and (E) Cellana spp., n = 87. Region names are displayed over associated spawning sites for fish species in (A). Regions are made up of two to 11 sites, grouped based on coastal geography and surrounding benthic habitat, and are designated in (A) by adjacent colored dots. Kalaupapa National Historical Park is highlighted in light green in (A). Source–sink dynamics and local retention

    Dispersal distance was measured via the distm office in the R package ‘geosphere’, which calculates distance between geographical points via the Haversine formula (Hijmans, 2016). This distance, measured between spawn and settlement locations, was used to reckon dispersal kernels to examine and compare species-specific distributions. They too measured local retention, or the percentage of successful settlers from a site that were retained at that site (i.e., settlers at site A that originated from site A/total successful settlers that originated from site A). To assess the role of specific sites around Moloka‘i, they too calculated a source–sink index for each species (Holstein, Paris & Mumby, 2014; Wren et al., 2016). This index defines sites as either a source, in which a site’s successful export to other sites is greater than its import, or a sink, in which import from other sites is greater than successful export. It is calculated by dividing the contrast between number of successfully exported and imported larvae by the sum of each and every successfully exported and imported larvae. A value <0 indicates that a site acts as a net sink, while a value >0 indicates that a site acts as a net source. While they measured successful dispersal to adjacent islands, they did not spawn larvae from them, and therefore these islands picture exogenous sinks. For this reason, settlement to other islands was not included in source–sink index calculations.

    We too calculated settlement proportion between different regions for each species (Calabrese & Fagan, 2004). They calculated the forward settlement proportion, i.e., the proportion of settlers from a specific settlement site (s) originating from an observed root site (o), by scaling the number of successful settlers from site o settling at site s to each and every successful settlers originating from site o. Forward proportion can be represented as Pso = Sos∕∑So. They too calculated rearward settlement proportion, or the proportion of settlers from a specific root site (o) observed at settlement site (s), by scaling the number of settlers observed at site s originating from site o to each and every settlers observed at site s. The rearward proportion can be represented as Pos = Sos∕∑Ss.

    Graph-theoretic analysis

    To quantify connections between sites, they applied graph theory to population connectivity (Treml et al., 2008; Holstein, Paris & Mumby, 2014). Graph theoretic analysis is highly scalable and can be used to examine fine-scale networks between reef sites up to broad-scale analyses between islands or archipelagos, mapping to both local and regional management needs. It too allows for both network- and site-specific metrics, enabling the comparison of connectivity between species and habitat sites as well as highlighting potential multi-generational dispersal corridors. Graph theory too provides a powerful tool for spatial visualization, allowing for rapid, intuitive communication of connectivity results to researchers, managers, and the public alike. This type of analysis can be used to model pairwise relationships between spatial data points by breaking down individual-based output into a series of nodes (habitat sites) and edges (directed connections between habitat sites). They then used these nodes and edges to examine the relative importance of each site and dispersal pathway to the greater pattern of connectivity around Moloka‘i, as well as differences in connectivity patterns between species (Treml et al., 2008; Holstein, Paris & Mumby, 2014). They used the R package ‘igraph’ to examine several measures of within-island connectivity (Csardi & Nepusz, 2006). Edge density, or the proportion of realized edges out of each and every possible edges, is a multi-site measure of connectivity. Areas with a higher edge density acquire more direct connections between habitat sites, and thus are more strongly connected. They measured edge density along and between the north, south, east, and west coasts of Moloka‘i to examine possible population structure and degree of exchange among the marine resources of local communities.

    The distribution of shortest path length is too informative for comparing overall connectivity. In graph theory, a shortest path is the minimum number of steps needed to connect two sites. For example, two sites that exchange larvae in either direction are connected by a shortest path of one, whereas if they both share larvae with an intermediate site but not with each other, they are connected by a shortest path of two. In a biological context, shortest path can correspond to number of generations needed for exchange: sites with a shortest path of two require two generations to originate a connection. tolerable shortest path, therefore, is a descriptive statistic to assess connectivity of a network. If two sites are unconnected, it is possible to acquire infinite-length shortest paths; here, these infinite values were famed but not included in final analyses.

    Networks can too be broken in connected components (Csardi & Nepusz, 2006). A weakly connected component (WCC) is a subgraph in which each and every nodes are not reachable by other nodes. A network split into multiple WCCs indicates separate populations that enact not exchange any individuals, and a great number of WCCs indicates a low degree of island-wide connectivity. A strongly connected component (SCC) is a subgraph in which each and every nodes are directly connected and indicates a lofty degree of connectivity. A region with many small SCCs can betoken lofty local connectivity but low island-wide connectivity. Furthermore, component analysis can identify sever nodes, or nodes that, if removed, wreck a network into multiple WCCs. Pinpointing these sever nodes can identify potential primary sites for preserving a population’s connectivity, and could inform predictions about the impact of site loss (e.g., a large-scale coral bleaching event) on overall connectivity.

    On a regional scale, it is primary to note which sites are exporting larvae to, or importing larvae from, other sites. To this end, they examined in-degree and out-degree for each region. In-degree refers to the number of inward-directed edges to a specific node, or how many other sites provide larvae into site ‘A’. Out-degree refers to the number of outward-directed edges from a specific node, or how many sites receive larvae from site ‘A’. Habitat sites with a lofty out-degree seed a great number of other sites, and betoken potentially primary larval sources, while habitat sites with a low in-degree rely on a limited number of larval sources and may therefore be relative on connections with these few other sites to maintain population size. Finally, betweenness centrality (BC) refers to the number of shortest paths that pass through a given node, and may therefore betoken connectivity pathways or ‘chokepoints’ that are primary to overall connectivity on a multigenerational timescale. BC was weighted with the proportion of dispersal as described in the preceding section. They calculated in-degree, out-degree, and weighted betweenness centrality for each region in the network for each species.

    As with the source–sink index, they did not include sites on islands other than Moloka‘i in their calculations of edge density, shortest paths, connected components, sever nodes, in- and out-degree, or betweenness centrality in order to focus on within-island patterns of connectivity.

    Results Effects of biological parameters on fine-scale connectivity patterns

    The species-specific parameters that were available to parameterize the dispersal models substantially influenced final output (Fig. 2). The proportion of successful settlers (either to Moloka‘i or to neighboring islands) varied widely by species, from 2% (Panulirus spp.) to 25% (Cellana spp.). Minimum pelagic duration and settlement success were negatively correlated (e.g., an estimated −0.79 Pearson correlation coefficient). Species modeled with batch spawning at a specific moon facet and/or time of day (Cellana spp., P. meandrina, and C. ignoblis) displayed slightly higher settlement success than similar species modeled with constant spawning over specific months. On a smaller scale, they too examined tolerable site-scale local retention, comparing only retention to the spawning site versus other sites on Moloka‘i (Fig. 2). Local retention was lowest for Caranx spp. (<1%) and highest for O. cyanea and P. sexfilis (8.1% and 10%, respectively).

    Figure 2: Summary statistics for each species network. Summary statistics are displayed in order of increasing minimum pelagic larval duration from left to right. Heatmap colors are based on normalized values from 0–1 for each analysis. Successful settlement refers to the proportion of larvae settled out of the total number of larvae spawned. Local retention is measured as the proportion of larvae spawned from a site that settle at the selfsame site. Shortest path is measured as the minimum number of steps needed to connect two sites. Strongly connected sites refers to the proportion of sites in a network that belong to a strongly connected component. be substantive dispersal distance is measured in kilometers from spawn site to settlement site.

    We measured network-wide connectivity via distribution of shortest paths, or the minimum number of steps between a given two nodes in a network, only including sites on Moloka‘i (Fig. 2). O. cyanea and P. sexfilis showed the smallest shortest paths overall, signification that on average, it would select fewer generations for these species to demographically bridge any given pair of sites. Using maximum shortest path, it could select these species three generations at most to connect sites. Cellana spp. and P. meandrina, by comparison, could select as many as five generations. Other medium- and long-dispersing species showed relatively equivalent shortest-path distributions, with trevally species showing the highest be substantive path length and therefore the lowest island-scale connectivity.

    The number and size of weakly-connected and strongly-connected components in a network is too an informative measure of connectivity (Fig. 2). No species in their study group was broken into multiple weakly-connected components; however, there were species-specific patterns of strongly connected sites. O. cyanea and P. sexfilis were the most strongly connected, with each and every sites in the network falling into a single SCC. Cellana spp. and P. meandrina each had approximately 60% of sites included in a SCC, but both point to fragmentation with seven and six SCCs respectively, ranging in size from two to 22 sites. This SCC pattern suggests low global connectivity but lofty local connectivity for these species. Medium and long dispersers showed larger connected components; 70% of parrotfish sites fell within two SCCs; 40% of P. porphyreus sites fell within two SCCs; 70% of C. strigosus sites, 55% of C. melampygus sites, and 40% of Panulirus sites fell within a single SCC. In contrast, only 26% of C. ignoblis sites fell within a single SCC. It is too primary to note that the lower connectivity scores observed in long-dispersing species likely reflect a larger scale of connectivity. Species with a shorter PLD are highly connected at reef and island levels but may point to weaker connections between islands. Species with a longer PLD, such as trevally or spiny lobster, are likely more highly connected at inter-island scales which reflects the lower connectivity scores per island shown here.

    Figure 3: Dispersal distance density kernels. Dispersal distance is combined across species by minimum pelagic larval duration (PLD) length in days (short, medium, or long). Most short dispersers settle nigh to home, while few long dispersers are retained at or near their spawning sites.

    Minimum PLD was positively correlated with be substantive dispersal distance (e.g., an estimated 0.88 Pearson correlation coefficient with minimum pelagic duration loge-transformed to linearize the relationship), and dispersal kernels differed between species that are short dispersers (3–25 days), medium dispersers (30–50 days), or long dispersers (140–270 days) (Fig. 3). Short dispersers travelled a be substantive distance of 24.06 ± 31.33 km, medium dispersers travelled a be substantive distance of 52.71 ± 40.37 km, and long dispersers travelled the farthest, at a be substantive of 89.41 ± 41.43 km. However, regardless of PLD, there were essentially two peaks of be substantive dispersal: a short-distance peak of <30 km, and a long-distance peak of roughly 50–125 km (Fig. 3). The short-distance peak largely represents larvae that settle back to Moloka‘i, while the long-distance peak largely represents settlement to other islands; the low point between them corresponds to deep-water channels between islands, i.e., unsuitable habitat for settlement. Median dispersal distance for short dispersers was substantially less than the be substantive at 8.85 km, indicating that most of these larvae settled relatively nigh to their spawning sites, with rare long-distance dispersal events bringing up the average. Median distance for medium (54.22 km) and long (91.57 km) dispersers was closer to the mean, indicating more even distance distributions and thus a higher probability of long-distance dispersal for these species. Maximum dispersal distance varied between ∼150–180 km depending on species, except for the spiny lobster Panulirus spp., with a PLD of 270 d and a maximum dispersal distance of approximately 300 km.

    Settlement to Moloka‘i and other islands in the archipelago

    Different species showed different forward settlement proportion to adjacent islands (Fig. 4), although every species in the study group successfully settled back to Moloka‘i. P. meandrina showed the highest percentage of island-scale local retention (82%), while C. ignoblis showed the lowest (7%). An tolerable of 74% of larvae from short-dispersing species settled back to Moloka‘i, as compared to an tolerable of 41% of medium dispersers and 9% of long dispersers. A great proportion of larvae too settled to O‘ahu, with longer PLDs resulting in greater proportions, ranging from 14% of O. cyanea to 88% of C. ignoblis. Moloka‘i and O‘ahu were the most commonly settled islands by percentage. Overall, settlement from Moloka‘i to Lana‘i, Maui, Kaho‘olawe, and Hawai‘i was to some extent lower. Larvae of every species settled to Lana‘i, and settlement to this island made up less than 5% of settled larvae across each and every species. Likewise, settlement to Maui made up less than 7% of settlement across species, with P. meandrina as the only species that had no successful paths from Moloka‘i to Maui. Settlement to Kaho‘olawe and Hawai‘i was less common, with the exception of Panulirus spp., which had 16% of each and every settled larvae on Hawai‘i.

    Figure 4: Forward settlement from Moloka’i to other islands. Proportion of simulated larvae settled to each island from Moloka‘i by species, organized in order of increasing minimum pelagic larval duration from left to right.

    We too examined coast-specific patterns of rearward settlement proportion to other islands, discarding connections with a very low proportion of larvae (<0.1% of total larvae of that species settling to other islands). Averaged across species, 83% of larvae settling to O‘ahu from Moloka‘i were spawned on the north shore of Moloka‘i, with 12% spawned on the west shore (Fig. S4). Spawning sites on the east and south shores contributed <5% of each and every larvae settling to O‘ahu from Moloka‘i. The east and south shores of Moloka‘i had the highest tolerable percentage of larvae settling to Lana‘i from Moloka‘i, at 78% and 20% respectively, and to Kaho‘olawe from Moloka‘i at 63% and 34%. Of the species that settled to Maui from Moloka‘i, on tolerable most were spawned on the east (53%) or north (39%) shores, as were the species that settled to Hawai‘i Island from Moloka‘i (22% east, 76% north). These patterns betoken that multiple coasts of Moloka‘i acquire the potential to export larvae to neighboring islands.

    Temporal settlement profiles too varied by species (Fig. 5). Species modeled with moon-phase spawning and relatively short settlement windows (Cellana spp. and C. ignoblis) were characterized by discrete settlement pulses, whereas other species showed settlement over a broader term of time. Some species too showed distinctive patterns of settlement to other islands; their model suggests specific windows when long-distance dispersal is possible, as well as times of year when local retention is maximized (Fig. 5).

    Figure 5: Species-specific temporal recruitment patterns. Proportion densities of settlement to specific islands from Moloka‘i based on day of year settled, by species. Rare dispersal events (e.g., Maui or Lana‘i for Cellana spp.) materialize as narrow spikes, while broad distributions generally betoken more common settlement pathways. Regional patterns of connectivity in Moloka‘i coastal waters

    Within Moloka‘i, their model predicts that coast-specific population structure is likely; averaged across each and every species, 84% of individuals settled back to the selfsame coast on which they were spawned rather than a different coast on Moloka‘i. Excluding connections with a very low proportion of larvae (<0.1% of total larvae of that species that settled to Moloka‘i), they establish that the proportion of coast-scale local retention was generally higher than dispersal to another coast, with the exception of the west coast (Fig. 6A). The north and south coasts had a lofty degree of local retention in every species except for the long-dispersing Panulirus spp., and the east coast too had lofty local retention overall. Between coasts, a lofty proportion of larvae that spawned on the west coast settled on the north coast, and a lesser amount of larvae were exchanged from the east to south and from the north to east. With a few species-specific exceptions, larval exchange between other coasts of Moloka‘i was negligible.

    Figure 6: Coast-by-coast patterns of connectivity on Moloka‘i. (A) tolerable rearward settlement proportion by species per pair of coastlines, calculated by the number of larvae settling at site s from site o divided by each and every settled larvae at site s. Directional coastline pairs (Spawn > Settlement) are ordered from left to birthright by increasing median settlement proportion. (B) Heatmap of edge density for coast-specific networks by species. Density is calculated by the number of each and every realized paths out of total possible paths, disregarding directionality.

    We too calculated edge density, including each and every connections between coasts on Moloka‘i regardless of settlement proportion (Fig. 6B). The eastern coast was particularly well-connected, with an edge density between 0.14 and 0.44, depending on the species. The southern shore showed lofty edge density for short and medium dispersers (0.16–0.39) but low for long dispersers (<0.005). The north shore too showed relatively lofty edge density (0.20 on average), although these values were smaller for long dispersers. The west coast showed very low edge density, with the exceptions of O. cyanea (0.37) and P. sexfilis (0.13). Virtually each and every networks that included two coasts showed lower edge density. One exception was the east/south shore network, which had an edge density of 0.10–0.65 except for Cellana spp. Across species, edge density between the south and west coasts was 0.12 on average, and between the east and west coasts was 0.04 on average. Edge density between north and south coasts was particularly low for each and every species (<0.05), a divide that was especially discrete in Cellana spp. and P. meandrina, which showed zero realized connections between these coasts. Although northern and southern populations are potentially weakly connected by sites along the eastern ( P. meandrina) or western (Cellana spp.) shores, their model predicts very little, if any, demographic connectivity.

    To explore patterns of connectivity on a finer scale, they pooled sites into regions (as defined in Fig. 1) in order to resolve relationships between these regions. Arranging model output into node-edge networks clarified pathways and regions of note, and revealed several patterns which did not result simple predictions based on PLD (Fig. 7). Cellana spp. and P. meandrina showed the most fragmentation, with several SCCs and low connectivity between coasts. Connectivity was highest in O. cyanea and P. sexfilis, which had a single SCC containing each and every regions. Medium and long dispersers generally showed fewer strongly connected regions on the south shore than the north shore, with the exception of C. strigosus. P. porphyreus showed more strongly connected regions east of Kalaupapa but lower connectivity on the western half of the island.

    Figure 7: Moloka’i connectivity networks by species. Graph-theoretic networks between regions around Moloka’i by species arranged in order of minimum pelagic larval duration. (A–D) Short dispersers (3–25 days), (E–G) medium dispersers (30–50 days), and (H–J) long dispersers (140–270 days). Node size reflects betweenness centrality of each region, scaled per species for visibility. Node color reflects out-degree of each region; yellow nodes acquire a low out-degree, red nodes acquire a medium out-degree, and black nodes acquire a lofty out-degree. Red edges are connections in a strongly connected component, while gray edges are not allotment of a strongly connected component (although may still picture substantial connections). Edge thickness represents log-transformed proportion of dispersal along that edge.

    Region-level networks showed both species-specific and species-wide patterns of connectivity (Fig. 8). With a few exceptions, sites along the eastern coast—notably, Cape Halawa and Pauwalu Harbor—showed relatively lofty betweenness centrality, and may therefore act as multigenerational pathways between north-shore and south-shore populations. In Cellana spp., Leinapapio Point and Mokio Point had the highest BC, while in high-connectivity O. cyanea and P. sexfilis, regions on the west coast had lofty BC scores. P. meandrina and C. strigosus showed several regions along the south shore with lofty BC. For Cellana spp. and P. meandrina, regions in the northeast had the highest out-degree, and therefore seeded the greatest number of other sites with larvae (Fig. 8). Correspondingly, regions in the northwest (and southwest in the case of P. meandrina) showed the highest in-degree. For O. cyanea and P. sexfilis, regions on the western and southern coasts showed the highest out-degree. For most species, both out-degree and in-degree were generally highest on the northern and eastern coasts, suggesting higher connectivity in these areas.

    Figure 8: Region-level summary statistics across each and every species. Betweenness centrality is a measure of the number of paths that pass through a inevitable region; a lofty score suggests potentially primary multi-generation connectivity pathways. In-degree and out-degree refer to the amount of a node’s incoming and outgoing connections. Betweenness centrality, in-degree, and out-degree acquire each and every been normalized to values between 0 to 1 per species. Local retention is measured as the proportion of larvae that settled back to their spawn site out of each and every larvae spawned at that site. Source-sink index is a measure of net export or import; negative values (blue) betoken a net larval sink, while positive values (red) betoken a net larval source. White indicates that a site is neither a strong source nor sink. Gray values for Cellana spp. denote a requisite of suitable habitat sites in that particular region.

    Several species-wide hotspots of local retention emerged, particularly East Kalaupapa Peninsula/Leinaopapio Point, the northeast point of Moloka‘i, and the middle of the south shore. Some species too showed some degree of local retention west of Kalaupapa Peninsula. While local retention was observed in the long-dispersing Caranx spp. and Panulirus spp., this amount was essentially negligible. In terms of source–sink dynamics, Ki‘oko‘o, Pu‘ukaoku Point, and West Kalaupapa Peninsula, each and every on the north shore, were the only sites that consistently acted as a net source, exporting more larvae than they import (Fig. 8). Kaunakakai Harbor, Lono Harbor, and Mokio Point acted as net sinks across each and every species. Puko‘o, Pauwalu Harbor, and Cape Halawa were either weak net sources or neither sources nor sinks, which corresponds to the lofty levels of local retention observed at these sites. Pala‘au and Mo‘omomi acted as either weak sinks or sources for short dispersers and as sources for long dispersers.

    Only four networks showed regional cut-nodes, or nodes that, if removed, wreck a network into multiple weakly-connected components (Fig. S5). Cellana spp. showed two cut-nodes: Mokio Point in northwest Moloka‘i and La‘au Point in southwest Moloka‘i, which if removed isolated Small Bay and Lono Harbor, respectively. C. perspicillatus, and S. rubroviolaceus showed a similar pattern in regards to Mokio Point; removal of this node isolated Small Bay in this species as well. In C. ignoblis, loss of Pauwalu Harbor isolated Lono Harbor, and loss of Pala‘au isolated Ilio Point on the northern coast. Finally, in Panulirus spp., loss of Leinaopapio Point isolated Papuhaku Beach, since Leinapapio Point was the only larval source from Moloka‘i for Papuhaku Beach in this species.

    Figure 9: Connectivity matrix for larvae spawned on Kalaupapa Peninsula. Includes larvae settled on Molokaí (regions below horizontal black line) and those settled on other islands (regions above horizontal black line), spawned from either the east (E) or west (W) coast of Kalaupapa. Heatmap colors picture rearward proportion, calculated by the number of larvae settling at site s from site o divided by each and every settled larvae at site s. White squares betoken no dispersal along this path. The role of Kalaupapa Peninsula in inter- and intra-island connectivity

    Our model suggests that Kalaupapa National Historical Park may play a role in inter-island connectivity, especially in terms of long-distance dispersal. Out of each and every regions on Moloka‘i, East Kalaupapa Peninsula was the single largest exporter of larvae to Hawai‘i Island, accounting for 19% of each and every larvae transported from Moloka‘i to this island; West Kalaupapa Peninsula accounted for another 10%. The park too contributed 22% of each and every larvae exported from Moloka‘i to O‘ahu, and successfully exported a smaller percentage of larvae to Maui, Lana‘i, and Kaho‘olawe (Fig. 9). Kalaupapa was not marked as a cut-node for any species, signification that complete population breaks are not predicted in the case of habitat or population loss in this area. Nevertheless, in their model Kalaupapa exported larvae to multiple regions along the north shore in each and every species, as well as regions along the east, south, and/or west shores in most species networks (Figs. 9 and 10). The park may play a particularly primary role for long-dispersing species; settlement from Kalaupapa made up 18%–29% of each and every successful settlement in Caranx spp. and Panulirus spp., despite making up only 12% of spawning sites included in the model. In C. strigosus, S. rubroviolaceus, and C. strigosus, Kalaupapa showed a particularly lofty out-degree, or number of outgoing connections to other regions, and West Kalaupapa was too one of the few regions on Moloka‘i that acted as a net larval source across each and every species (Fig. 8). Their study has too demonstrated that different regions of a marine protected region can potentially perform different roles, even in a small MPA such as Kalaupapa. Across species, the east coast of Kalaupapa showed a significantly higher betweenness centrality than the west (p = 0.028), while the west coast of Kalauapapa showed a significantly higher source–sink index than the east (p = 2.63e−9).

    Figure 10: Larval spillover from Kalaupapa National Historical Park. Site-level dispersal to sites around Moloka‘i from sites in the Kalaupapa National Historical Park protected area, by species. (A–D) Short dispersers (3–25 days), (E–G) medium dispersers (30–50 days), and (H–J) long dispersers (140–270 days). Edge color reflects proportion of dispersal along that edge; red indicates higher proportion while yellow indicates lower proportion. Kalaupapa National Historical Park is highlighted in light green. Discussion Effects of biological and physical parameters on connectivity

    We incorporated the distribution of suitable habitat, variable reproduction, variable PLD, and ontogenetic changes in swimming aptitude and empirical vertical distributions of larvae into their model to extend biological realism, and assess how such traits impact predictions of larval dispersal. The Wong-Ala et al. (2018) IBM provides a highly resilient model framework that can easily be modified to incorporate either additional species-specific data or entirely unusual biological traits. In this study, they included specific spawning seasons for each and every species, as well as spawning by moon facet for Cellana spp., P. meandrina, and C. ignoblis because such data was available for these species. It proved difficult to obtain the necessary biological information to parameterize the model, but as more data about life history and larval deportment become available, such information can be easily added for these species and others. Some potential additions to future iterations of the model might include density of reproductive-age adults within each habitat patch, temperature-dependent pelagic larval duration (Houde, 1989), ontogenetic-dependent behavioral changes such as orientation and diel vertical migration (Fiksen et al., 2007; Paris, Chérubin & Cowen, 2007), pre-competency period, and larval habitat preferences as such information becomes available.

    In this study, they acquire demonstrated that patterns of fine-scale connectivity around Moloka‘i are largely species-specific and can vary with life history traits, even in species with identical pelagic larval duration. For example, the parrotfish S. rubroviolaceus and C. perspicillatus point to greater connectivity along the northern coast, while the goatfish P. porphyreus shows higher connectivity along the eastern half of the island. These species acquire similar PLD windows, but vary in dispersal depth and spawning season. Spawning season and timing altered patterns of inter-island dispersal (Fig. 5) as well as overall settlement success, which was slightly higher in species that spawned by moon facet (Fig. 2). While maximum PLD did materialize play a role in the probability of rare long-distance dispersal, minimum PLD appears to be the main driver of tolerable dispersal distance (Fig. 2). Overall, species with a shorter minimum PLD had higher settlement success, shorter be substantive dispersal distance, higher local retention, and higher local connectivity as measured by the amount and size of strongly connected components.

    The interaction of biological and oceanographic factors too influenced connectivity patterns. Because mesoscale current patterns can vary substantially over the course of the year, the timing of spawning for inevitable species may be captious for estimating settlement (Wren et al., 2016; Wong-Ala et al., 2018). Intermittent ocean processes may influence the probability of local retention versus long-distance dispersal; a great proportion of larvae settled to O‘ahu, which is to some extent surprising given that in order to settle from Moloka‘i to O‘ahu, larvae must cross the Kaiwi Channel (approx. 40 km). However, the intermittent presence of mesoscale gyres may act as a stabilizing pathway across the channel, sweeping larvae up either the windward or leeward coast of O‘ahu depending on spawning site. Likewise, in their model long-distance dispersal to Hawai‘i Island was possible at inevitable times of the year due to a gyre to the north of Maui; larvae were transported from Kalaupapa to this gyre, where they were carried to the northeast shore of Hawai‘i (Fig. S6). preparatory analysis too suggests that distribution of larval depth influenced edge directionality and size of connected components (Fig. 7); surface currents are variable and primarily wind-driven, giving positively-buoyant larvae different patterns of dispersal than species that disperse deeper in the water column (Fig. S7).

    Model limitations and future perspectives

    Our findings acquire several caveats. Because fine-scale density estimates are not available for their species of interest around Moloka’i, they assumed that fecundity is equivalent at each and every sites. This simplification may lead us to under- or over-estimate the strength of connections between sites. requisite of adequate data too necessitated estimation or extrapolation from congener information for larval traits such as larval dispersal depth and PLD. Since it is difficult if not impossible to identify larvae to the species level without genetic analysis, they used genus-level larval distribution data (Boehlert & Mundy, 1996), or lacking that, an assess of 50–100 m as a depth layer that is generally more enriched with larvae (Boehlert, Watson & Sun, 1992; Wren & Kobayashi, 2016). They too estimated PLD in several cases using congener-level data (see Table 1). While specificity is exemplar for making informed management decisions about a inevitable species, past sensitivity analysis has shown that variation in PLD length does not greatly impact patterns of dispersal in species with a PLD of >40 days (Wren & Kobayashi, 2016).

    Although their MITgcm current model shows annual consistency, it only spans two and a half years chosen as neutral state ‘average’ ocean conditions. It does not span any El Niño or La Niña (ENSO) events, which occasions wide-scale sea-surface temperature anomalies and may therefore influence patterns of connectivity during these years. El Niño can acquire a particularly strong impact on coral reproduction, since the warm currents associated with these events can lead to severe temperature stress (Glynn & D’Croz, 1990; Wood et al., 2016). While there has been tiny study to date on the effects of ENSO on fine-scale connectivity, previous work has demonstrated increased variability during these events. For example, Wood et al. (2016) showed a abate in eastward Pacific dispersal during El Niño years, but an extend in westward dispersal, and Treml et al. (2008) showed unique connections in the West Pacific as well as an extend in connectivity during El Niño. While these effects are difficult to predict, especially at such a small scale, additional model years would extend self-confidence in long-term connectivity estimations. Additionally, with a temporal resolution of 24 h, they could not adequately address the role of tides on dispersal, and therefore did not include them in the MITgcm. Storlazzi et al. (2017) showed that tidal forces did influence larval dispersal in Maui Nui, underlining the importance of including both fine-scale, short-duration models and coarser-scale, long-duration models in final management decisions.

    We too confine their model’s scope geographically. Their goal was to determine whether they could resolve predictive patterns at this scale apposite to management. Interpretation of connectivity output can be biased by spatial resolution of the ocean model, since intricate coastal processes can be smoothed and therefore impact larval trajectories. To confine this bias, they focused mainly on coastal and regional connectivity on scales greater than the current resolution. They too used the finest-scale current products available for their study area, and their results point to generic agreement with similar studies of the region that exhaust a coarser resolution (Wren & Kobayashi, 2016) and a finer resolution (Storlazzi et al., 2017). Also, while learning of island-scale connectivity is primary for local management, it does disregard potential connections from other islands. In their calculations of edge density, betweenness centrality and source-sink index, they included only settlement to Moloka‘i, discarding exogenous sinks that would jaundice their analysis. Likewise, they cannot forecast the proportion of larvae settling to other islands that originated from Moloka‘i, or the proportion of larvae on Moloka‘i that originated from other islands.

    It is too primary to note scale in relation to measures of connectivity; they expect that long-dispersing species such as Caranx spp. and Panulirus spp. will point to much higher measures of connectivity when measured across the whole archipelago as opposed to a single island. The cut-nodes observed in these species may not actually wreck up populations on a great scale due to this inter-island connectivity. Nevertheless, cut-nodes in species with short- and medium-length PLD may indeed ticket primary habitat locations, especially in terms of providing links between two otherwise disconnected coasts. It may be that for inevitable species or inevitable regions, stock replenishment relies on larval import from other islands, underscoring the importance of MPA selection for population maintenance in the archipelago as a whole.

    Implications for management

    Clearly, there is no single management approach that encompasses the breadth of life history and deportment differences that impact patterns of larval dispersal and connectivity (Toonen et al., 2011; Holstein, Paris & Mumby, 2014). The spatial, temporal, and species-specific variability suggested by their model stresses the requisite for multi-scale management, specifically tailored to local and regional connectivity patterns and the suite of target species. Even on such a small scale, different regions around the island of Moloka‘i can play very different roles in the greater pattern of connectivity (Fig. 8); sites along the west coast, for example, showed fewer ingoing and outgoing connections than sites on the north coast, and therefore may be more at risk of isolation. Seasonal variation should too be taken into account, as mesoscale current patterns (and resulting connectivity patterns) vary over the course of a year. Their model suggests species-specific temporal patterns of settlement (Fig. 5); even in the year-round spawner O. cyanea, local retention to Moloka‘i as well as settlement to O‘ahu was maximized in spring and early summer, while settlement to other islands mostly occurred in late summer and fall.

    Regions that point to similar network dynamics may capitalize from similar management strategies. Areas that act as larval sources either by proportion of larvae (high source–sink index) or number of sites (high out-degree) should receive management consideration. On Moloka‘i, across each and every species in their study, these sources fell mostly on the northern and eastern coasts. Maintenance of these areas is especially primary for downstream areas that depend on upstream populations for a source of larvae, such as those with a low source–sink index, low in-degree, and/or low local retention. Across species, regions with the highest betweenness centrality scores fell mainly in the northeast (Cape Halawa and Pauwalu Harbor). These areas should receive consideration as potentially primary intergenerational pathways, particularly as a means of connecting north-coast and south-coast populations, which showed a requisite of connectivity both in total number of connections (edge density) and proportion of larvae. Both of these connectivity measures were included because edge density includes each and every connections, even those with a very small proportion of larvae, and may therefore include rare dispersal events that are of tiny relevance to managers. Additionally, edge density comparisons between networks should be viewed with the caveat that these networks enact not necessarily acquire the selfsame number of nodes. Nevertheless, both edge density and proportion point to very similar patterns, and include both demographically-relevant common connections as well as rare connections that could influence genetic connectivity.

    Management that seeks to establish a resilient network of spatially managed areas should too regard the preservation of both weakly-connected and strongly-connected components, as removal of key cut-nodes (Fig. S5) breaks up a network. Sites within a SCC acquire more direct connections and therefore may be more resilient to local population loss. care should be taken to preserve breeding populations at larval sources, connectivity pathways, and cut-nodes within a SCC, since without these key sites the network can fragment into multiple independent SCCs instead of a single stable network. This exercise may be especially primary for species for which they assess multiple small SCCs, such as Cellana spp. or P. meandrina.

    Kalaupapa Peninsula emerged as an primary site in Moloka‘i population connectivity, acting as a larval source for other regions around the island. The Park seeded areas along the north shore in each and every species, and too exported larvae to sites along the east and west shores in each and every species except P. meandrina and Cellana spp. Additionally, it was a larval source for sites along the south shore in the fishes C. perspicillatus, S. rubroviolaceus, and C. strigosus as well as Panulirus spp. Western Kalaupapa Peninsula was one of only three regions included in the analysis (the others being Ki‘oko‘o and Pu‘ukaoku Point, too on the north shore) that acted as a net larval source across each and every species. Eastern Kalaupapa Peninsula was particularly highly connected, and was allotment of a strongly connected component in every species. The Park too emerged as a potential point of connection to adjacent islands, particularly to O‘ahu and Hawai‘i. Expanding the spatial scale of their model will further elucidate Kalaupapa’s role in the greater pattern of inter-island connectivity.

    In addition to biophysical modeling, genetic analyses can be used to identify persistent population structure of relevance to managers (Cowen et al., 2000; Casey, Jardim & Martinsohn, 2016). Their finding that exchange among islands is generally low in species with a short- to medium-length PLD agrees with population genetic analyses of marine species in the Hawaiian Islands (Bird et al., 2007; Rivera et al., 2011; Toonen et al., 2011; Concepcion, Baums & Toonen, 2014). On a finer scale, they forecast some level of shoreline-specific population structure for most species included in the study (Fig. 6). Unfortunately, genetic analyses to date acquire been performed over too broad a scale to effectively compare to these fine-scale connectivity predictions around Moloka‘i or even among locations on adjacent islands. These model results warrant such small scale genetic analyses because there are species, such as the coral P. meandrina, for which the model predicts limpid separation of north-shore and south-shore populations which should be simple to test using genetic data. To validate these model predictions with this technique, more fine-scale population genetic analyses are needed.

    Conclusions

    The maintenance of demographically connected populations is primary for conservation. In this study, they contribute to the growing carcass of work in biophysical connectivity modeling, focusing on a region and suite of species that are of relevance to resource managers. Furthermore, they demonstrate the value of quantifying fine-scale relationships between habitat sites via graph-theoretic methods. Multispecies network analysis revealed persistent patterns that can mitigate define region-wide practices, as well as species-specific connectivity that merits more individual consideration. They demonstrate that connectivity is influenced not only by PLD, but too by other life-history traits such as spawning season, moon-phase spawning, and ontogenetic changes in larval depth. lofty local retention of larvae with a short- or medium-length PLD is consistent with population genetic studies of the area. They too identify regions of management importance, including West Kalaupapa Peninsula, which acts as a consistent larval source across species; East Kalaupapa Peninsula, which is a strongly connected region in every species network, and Pauwalu Harbor/Cape Halawa, which may act as primary multigenerational pathways. Connectivity is only one piece of the bewilder of MPA effectiveness, which must too account for reproductive population size, long-term persistence, and post-settlement survival (Burgess et al., 2014). That being said, their study provides a quantitative roadmap of potential demographic connectivity, and thus presents an effectual tool for estimating current and future patterns of dispersal around Kalaupapa Peninsula and around Moloka‘i as a whole.

    Supplemental Information Current patterns in the model domain.

    Current direction and velocity is displayed at a depth of 55 m below sea surface on (A) March 31st, 2011, (B) June 30th, 2011, (C) September 30th, 2011, and (D) December 31st, 2011. Arrowhead direction follows current direction, and u/v velocity is displayed through arrow length and color (purple, low velocity, red, lofty velocity). Domain extends from 198.2°E to 206°E and from 17°N to 22.2°N. The island of Moloka‘i is highlighted in red.

    Subset of validation drifter paths.

    Drifter paths in black and corresponding model paths are colored by drifter ID. each and every drifter information was extracted from the GDP Drifter Data Assembly hub (Elipot et al., 2016). Drifters were included if they fell within the model domain spatially and temporally, and were tested by releasing 1,000 particles on the remedy day where they entered the model domain, at the uppermost depth layer of their oceanographic model (0–5 m).

    Selected larval depth distributions.

    Modeled vertical larval distributions for Caranx spp. (left), S. rubroviolaceus and C. perspicillatus (middle), and P. porphyreus (right), using data from the 1996 NOAA ichthyoplankton vertical distributions data report (Boehlert & Mundy 1996).

    Coast-specific rearward settlement patterns by island

    Proportion of simulated larvae settled to each island from sites on each coast of Moloka‘i, averaged across each and every species that successfully settled to that island.

    Regional cut-nodes for four species networks

    Mokio Point and La‘au Point were cut-nodes for Cellana spp., Mokio Point was a cut-node for C. perspicillatus and S. rubroviolaceus, Pauwalu Harbor and Pala‘au were cut-nodes for C. ignoblis, and Leinaopapio Point was a cut-node for Panulirus spp.

    Selected dispersal pathways for Panulirus spp. larvae

    500 randomly sampled dispersal pathways for lobster larvae (Panulirus spp.) that successfully settled to Hawai‘i Island after being spawned off the coast of Moloka‘i. Red tracks betoken settlement earlier in the year (February–March), while black tracks betoken settlement later in the year (April–May). Most larvae are transported to the northeast coast of Hawai‘i via a gyre to the north of Maui, while a smaller proportion are transported through Maui Nui.

    Eddy differences by depth layer.

    Differences in eddy pattern and strength in surface layers (A, 2.5 m) vs. abysmal layers (B, 55 m) on March 31, 2011. Arrowhead direction follows current direction, and u/v velocity is displayed through arrow length and color (purple, low velocity, red, lofty velocity). While great gyres remain consistent at different depths, smaller features vary along this gradient. For example, the currents around Kaho‘olawe, the small gyre off the eastern coast of O‘ahu, and currents to the north of Maui each and every vary in direction and/or velocity.



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