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000-N04 exam Dumps Source : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

Test Code : 000-N04
Test appellation : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1
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: 30 actual Questions

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IBM IBM Commerce Solutions Order

IBM (IBM) Down 10.three% when you reckon that remaining salary document: Can It Rebound? | killexams.com actual Questions and Pass4sure dumps

A month has passed by when you reckon that the ultimate income record for IBM (IBM). Shares fill lost about 10.3% in that time frame, underperforming the S&P 500.

Will the coincident terrible vogue proceed leading up to its next income liberate, or is IBM due for a breakout? earlier than they dive into how traders and analysts fill reacted as of late, let's pick a quick loom on the most fresh income file to live able to regain a stronger tackle on the critical catalysts.

IBM’s Q2 results benefit from cost reducing, reduce share count

IBM mentioned third-quarter 2018 non-GAAP earnings of $3.forty two per share, which beat the Zacks Consensus rate by couple of cents. revenue per share (EPS) expanded 4.9% from the yr-ago quarter.

The year-over-12 months boom in EPS can likewise live attributed to stalwart pre-tax margin operating leverage (28 cents contribution) and aggressive share buybacks (19 cents contribution). This become partially offset by course of reduce revenues (seven cents impoverished influence) and better tax cost (17 cents impoverished fill an result on).

Revenues of $18.seventy six billion lagged the Zacks Consensus rate of $19.10 billion and declined 2.1% on a year-over-12 months foundation. At regular forex (cc), revenues remained flat.

IBM brought up that signings plunged 21% to $8 billion. services backlog declined 3% from the 12 months-ago quarter to $113 billion.

Geographic salary details

Revenues from Americas inched up 1%, driven with the aid of continued boom in Canada and Latin the united states and modest multiply within the united states.

Europe, middle-East and Africa reduced 2% from the 12 months-in the past quarter, driven by decline in Germany and France, partially offset by multiply in Spain and the UK.

Asia-Pacific revenues declined 1% on a year-over-yr groundwork with modest multiply in Japan.

Strategic Imperatives multiply Continues

Strategic Imperatives (cloud, analytics, mobility and security) grew 7% at cc from the year-in the past quarter to $9.three billion. protection revenues surged 34%. On a trailing 12-month groundwork, Strategic Imperatives revenues fill been $39.5 billion, up 13% (eleven% at cc).

Cloud revenues surged 13% from the yr-ago quarter to $4.6 billion. The annual accelerate expense for cloud as-a-service revenues elevated 24% at cc on a year-over-year basis to $11.4 billion.

Cloud revenues of $19 billion on a trailing 12-month basis increased 20% (18% at cc) and now debts for 24% of IBM’s complete revenues.

Cognitive Revenues Decline

Cognitive options’ revenues-external lowered 5.7% year over 12 months (down 5% at cc) to $4.15 billion. Segmental revenues relating Strategic Imperatives and Cloud declined 4% and 2%, respectively. Cloud as-a-provider salary annual accelerate rate was $2 billion.

solutions utility contains choices in strategic verticals relish health, area-certain capabilities relish analytics and security, and IBM’s emerging applied sciences of AI and blockchain. The segment likewise includes choices that address horizontal domains relish collaboration, commerce and skill. solutions utility revenues lowered 3% 12 months over year in the quarter.

IBM cited that in commerce zone the infusion of AI into choices relish consumer flavor analytics helped SaaS signings to grow double digit in the quarter. The coincident launch of Notes Domino version 10, which is optimized for cellular, and helps JavaScript and node.js will multiply increase collaboration in 2019.

Transaction Processing software includes application that runs mission-vital workloads, leveraging IBM’s hardware platforms. Revenues fell 8% on a 12 months-over-year groundwork.

IBM witnessed growth in trade verticals relish health, key areas of analytics and security within the quarter. Watson health witnessed huge-based multiply in Payer, issuer, Imaging and actuality Sciences domains.

all the course through the quarter, the Sugar.IQ software, developed by means of Medtronic in partnership with IBM, hit the market. The software is designed to simplify and enhance day by day diabetes administration.

IBM cited that analytics carried out smartly in the quarter, driven via statistics science offerings and IBM Cloud private for statistics offering.

all through the quarter, the commerce announced warp detection services and launched current Watson functions on the IBM Cloud private platform.

safety boom changed into pushed by course of choices in orchestration, facts safety and endpoint management.

In blockchain, IBM food fill assurance network for meals security went are animated within the quarter. Reatiler Carrefour joined IBM’s blockchain community. The company likewise collectively introduced TradeLens with Maersk that addresses inefficiencies in the global deliver chain. IBM currently supports 75 energetic blockchain networks.

world enterprise functions Revenues raise

Revenues from world commerce services-exterior segment fill been $four.13 billion, up 0.9% from the year-in the past quarter (up three% at cc). Segmental revenues pertaining to Strategic Imperatives grew 9%. Cloud apply surged 18%. Cloud as-a-provider income annual accelerate rate turned into $1.9 billion.

utility management revenues declined 1% from the yr-in the past quarter. besides the fact that children, global manner functions revenues climbed 2%. in addition, Consulting revenues improved 7% 12 months over year, pushed with the aid of robust efficiency from IBM’s digital business.

expertise capabilities & Cloud structures: Revenues Dip

Revenues from technology services & Cloud structures-external diminished 2% from the 12 months-in the past quarter (flat at cc) to $8.29 billion. Segmental revenues manner on Strategic Imperatives superior 16%, pushed through hybrid cloud features. Cloud surged 22% from the yr-ago quarter. Cloud as-a-provider salary annual accelerate rate was $7.5 billion.

Integration utility accelerated 1% from the 12 months-ago quarter. right through the quarter, ninety five organizations worldwide chosen IBM Cloud private providing. Infrastructure services revenues likewise elevated 1% on a yr-over-12 months groundwork.

however, Technical abet capabilities revenues diminished 3% from the yr-in the past quarter.

power & z14 coerce methods Revenues

techniques revenues expanded 0.9% on a year-over-12 months foundation (up 2% at cc) to $1.74 billion. Segmental revenues manner on Strategic Imperatives surged 5%, while Cloud revenues declined 8%.

IBM Z revenues improved 6% yr over year on greater than 20% MIPS growth, pushed by huge-primarily based adoption of the z14 mainframe.

vigor revenues expanded 17% from the yr-ago quarter. during the quarter, IBM launched its subsequent generation POWER9 processors for midrange and high-end systems which are designed for managing advanced analytics, cloud environments and records-intensive workloads in AI, HANA, and UNIX markets.

IBM likewise delivered current choices optimizing both hardware and application for AI. management believes that products relish PowerAI vision and PowerAI enterprise will assist coerce current consumer adoption.

although, storage hardware revenues declined 6% as a result of susceptible performance in the midrange and tall conclusion, in fraction offset via stalwart growth in replete glint Arrays. IBM cited that pricing pressure in the immensely competitive storage market is hurting revenues. The commerce introduced its current FlashSystems with next technology NVMe expertise replete through the quarter.

operating methods utility revenues declined four%, while programs Hardware superior 4% from the year-in the past quarter.

eventually, world Financing (contains financing and used paraphernalia revenue) revenues decreased 9.1% at cc to $388 million.

working details

Non-GAAP Gross margin remained unchanged from the yr-ago quarter at forty seven.4%. This become IBM’s most efficient Gross margin performance in years and changed into essentially driven by 160 basis points (bps) expansion in functions margin. however, unfavourable combine in z14 mainframe and application fully offset this expansion.

working charge declined 4% 12 months over year, as a result of consciousness of acquisition synergies and improving operational efficiencies. IBM continues to withhold money into speedy turning out to live fields relish hybrid cloud, simulated intelligence (AI), protection and blockchain.

Pre-tax margin from continuing operations extended 50 bps on a year-over-yr basis to 19.2%.

Cognitive solutions and global enterprise functions section pre-tax margins increased one hundred ninety bps and 320 bps, respectively, on a 12 months-over-year basis. despite the fact, technology features & Cloud structures segment pre-tax margin shriveled one hundred bps.

techniques pre-tax profits become $209 million down 38% year over 12 months. world Financing section pre-tax income jumped 26.7% to $308 million.

stability Sheet & money rush details

IBM ended third-quarter 2018 with $14.70 billion in total cash and marketable securities in comparison with $11.ninety three billion at the conclusion of second-quarter 2018. total debt (together with world financing) was $46.9 billion, up $1.four million from the faded quarter.

IBM pronounced cash rush from operations (except world Financing receivables) of $3.1 billion and generated free cash circulation of $2.2 billion in the quarter.

in the suggested quarter, the enterprise returned $2.1 billion to shareholders through dividends and share repurchases. on the conclusion of the quarter, the company had $1.four billion remaining under existing buyback authorization.

tips

IBM reiterated EPS forecast for 2018. Non-GAAP EPS is expected to live as a minimum $13.80.

IBM quiet anticipates 2018 free cash circulate of $12 billion.

Story Continues

How fill Estimates Been relocating since Then?

during the past month, buyers fill witnessed a downward fashion in sparkling estimates.

VGM ratings

at the present, IBM has an ordinary multiply score of C, although it is lagging a runt bit on the Momentum ranking front with a D. however, the stock became allocated a grade of A on the value aspect, putting it within the suitable quintile for this funding method.

overall, the inventory has an compund VGM rating of B. if you don't seem to live concentrated on one method, this score is the one recall to live attracted to.

Outlook

Estimates fill been generally trending downward for the inventory, and the magnitude of those revisions indicates a downward shift. specifically, IBM has a Zacks Rank #three (hold). They are expecting an in-line recur from the inventory within the following couple of months.

need the newest techniques from Zacks investment analysis? these days, which you could down load 7 most desirable stocks for the subsequent 30 Days. click on to regain this free record foreign commerce Machines supplier (IBM) : Free inventory evaluation document To read this article on Zacks.com click on right here. Zacks funding research


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Metro shoes taps IBM Watson For Digital Commerce | killexams.com actual Questions and Pass4sure dumps

ibm shoes

Metro shoes Ltd, one in replete India’s leading multi-brand footwear chains, is launching a current Digital Commerce platform powered with the aid of Watson customer engagement hosted on IBM Cloud. this would comprise IBM Watson Order administration and Commerce for seamless digital engagement. Working with IBM company companion CEBS worldwide, IBM solutions will now not most effectual abet power advanced customer experiences and current levels of convenience however convey efficiencies to the give chain.

With a countrywide footprint of 350 actual showrooms, an expanding brand portfolio and changing client preferences, Metro shoes Ltd became dealing with challenges in managing orders coming from several online structures.  previous dealt with by course of unreliable software, leading to exigency of visibility of real-time statistics of income, inventory residence and returns. in addition to its stock management challenges, Metro shoes Ltd crucial to improve on-line presence for a few of their regular inner manufacturers which fill been getting low visibility impacting usual earnings.

“expertise is redefining client engagement and will live the essential thing differentiator for retail manufacturers of the long run. We’re excited to collaborate with IBM and CEBS to embark on their digital transformation journey,” noted Alisha Malik, vp, Digital, Metro shoes. “With IBM’s capabilities within the omni-channel commerce and retail area, we're assured that these alterations will now not handiest champion accelerate the execution of their approach, but likewise supply us an edge over competitors. At Metro footwear, they strongly confidence that the current solution will boost the ordinary person journey, thereby increasing revisits, site visitors and loyalty.”

With IBM, Metro footwear Ltd can profit current stages of consumer insight, which can live used to personalize the online journey for every traveller as they navigate throughout the site. Delivered through a single platform, Metro footwear will live in a position to exhibit replete of its brands and suggest specific objects in response to insights shared by customers. This personalised flavor will comprise current and handy fulfillmentoptions corresponding to purchase on-line, select up in keep, reserve in retain and simple returns. on account of these current capabilities, Metro shoes will live able to elevate every vacationer’s event on the web page by enabling commerce practitioners with cognitive tools which champion them bring omni-channel experiences that fill interaction shoppers and pressure revenue.

With IBM’s know-how capabilities and CEBS edge with marketplace integration, Metro footwear as a manufacturer/vendor will likewise live in a position to integrate with greater than 14 e-marketplaces relish Amazon, Flipkart and different leading portals with a centralized procedure and inventory engine to allow Metro to scale up to the needs of a becoming marketplace enterprise. extra, IBM Cloud will abet elevate the potential to configure cumbersome workloads and thereby convey performance required for height usage replete through the searching season.

speaking concerning the collaboration, Nishant Kalra, commerce unit leader – IBM Watson consumer engagement - India/South Asiaadded, “IBM is at the forefront of assisting valued clientele comprise more moderen the course to drudgery and digitally reworking the course they interact with their conclusion purchasers. we're cheerful to live fraction of Metro shoes’ digital transformation adventure by means of delivering superior digital commerce experience, leveraging the stores by merging them with online, and eventually using company advocacy. IBM in association with CEBS will permit deep innovation, sooner-go-to-market and streamline tactics for scalability.”

The IBM platform will create a bridge between its online and offline company which the retailer previously lacked. With the brand current integrated single view, Metro shoes in the future could live in a position to consume insights received from the digital realm to design special offering for purchasers as they stroll into any of their stores. in consequence, they can abide in humor what customers desire, produce certain availability when and the residence they exigency it and even study run selling and upselling across their a considerable number of brands.

For Metro footwear, IBM Watson Order administration and Commerce solutions can pave course for IBM’s cognitive applied sciences to carry insights that abet them provide purchasers with customized ideas and an more advantageous user adventure –from click on to birth.

“With over 15 years of journey in establishing e-business equipment, CEBS has been a depended on options company and accomplice for groups throughout the globe,”talked about Satish Swaroop, President, CEBS international. Their positive and springy software options paired with IBM’s deep expertise potential will supply Metro footwear a real-time, centralized gadget for client administration.”


000-N04 IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

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000-N04 exam Dumps Source : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

Test Code : 000-N04
Test appellation : IBM Commerce Solutions Order Mgmt Technical Mastery Test v1
Vendor appellation : IBM
: 30 actual Questions

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Uniqlo’s current Mobile e-Commerce commerce Model | killexams.com actual questions and Pass4sure dumps

By Si Chen

Article Rating:

February 20, 2015 01:26 PM EST

Reads:

318

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

Did you notice

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

  • Upfront design
  • Inventory
  • Advertising
  • Uniqlo is not just thinking, Gee how execute they regain more Instagram followers to sell the identical faded T-shirts?

    They’re creating a gross current commerce 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 commerce model to turn a T-shirt (commodity) into your own drudgery of knack (priceless.)

    If runt Instagram could build a billion-dollar commerce 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 actual 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 critical 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 fill a bi-phasic life history in which adults pomp limited geographic orbit and tall 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 fill been shown to fill a home orbit of <1 km as adults (Meyer et al., 2000; Meyer, Papastamatiou & Clark, 2010). Depending on species, the mobile planktonic stage can eventual 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). knowledge of larval dispersal patterns can live 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 significance 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 institute in low densities across large areas of the open ocean (Clarke, 1991; Wren & Kobayashi, 2016). A variety of genetic and chemistry techniques fill therefore been developed to rate larval connectivity (Gillanders, 2005; Leis, Siebeck & Dixson, 2011; Toonen et al., 2011; Johnson et al., 2018). Computer models informed by sphere and laboratory data fill likewise become a valuable utensil 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 live successfully transported by ocean currents, and so is expected to directly strike 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 replete play a role in shaping connectivity patterns. Physical factors such as temperature, bathymetry, and current direction can likewise 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 foretell 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 accelerate east to west and are driven by the prevalent 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 involved 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 squabble for implementation of a more holistic approach than traditional single-species maximum sustainable yield techniques, which fill proven ineffective (Goodyear, 1996; Hilborn, 2011). There is a common movement toward the consume of ecosystem-based management, which requires knowledge 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 zone (MPA) located on the north shore of Moloka‘i, an island in the Maui Nui involved 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 fill 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 zone 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 rate the larval exchange among sites at the scale of an individual island. They fill 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, report 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 dusky gray. Spawn season and timing from traditional ecological knowledge 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 report 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 zone that includes the islands of Moloka‘i, Maui, Lana‘i, Kaho‘olawe, O‘ahu, and Hawai‘i. While Ni‘ihau and southern Kaua’i likewise plunge within the domain, they discarded connectivity to these islands because they lie within the 0.5° frontier zone of the current model. frontier conditions are enforced over 20 grid points on replete sides of the model domain. Vertically, the model is divided into 50 layers that multiply in thickness with depth, from five m at the surface (0.0–5.0 m) to 510 m at the ground (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 common 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 execute not comprise tides in the model due to temporal resolution. Their model period 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 center (Fig. S2) (Elipot et al., 2016), as well as other current models of the zone (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 replete 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). plumb velocities (w) were not implemented by the model; details of plumb 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 manner (i.e., fourth-order Runge–Kutta) because they did not keep a inequity at this timestep length. Biological processes modeled comprise PLD, reproduction timing and location, mortality, and ontogenetic changes in plumb 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 End 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 period 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 replete larvae were assumed to fill 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 viable 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 fill demonstrated that fish larvae fill a tall degree of control over their plumb 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 plumb 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.) execute not fill 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 rate 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 autochthonous 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 zone not covered by sand or mud, for a total of 87 sites. Number of adults was assumed equal at replete 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 identical benthic habitat classification and prevalent wave direction, and/or were fraction of the identical 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 likewise 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 rate the role of specific sites around Moloka‘i, they likewise 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 inequity between number of successfully exported and imported larvae by the sum of replete 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 likewise calculated settlement balance between different regions for each species (Calabrese & Fagan, 2004). They calculated the forward settlement proportion, i.e., the balance of settlers from a specific settlement site (s) originating from an observed origin site (o), by scaling the number of successful settlers from site o settling at site s to replete successful settlers originating from site o. Forward balance can live represented as Pso = Sos∕∑So. They likewise calculated rearward settlement proportion, or the balance of settlers from a specific origin site (o) observed at settlement site (s), by scaling the number of settlers observed at site s originating from site o to replete settlers observed at site s. The rearward balance can live 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 live 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 likewise 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 likewise provides a powerful utensil for spatial visualization, allowing for rapid, intuitive communication of connectivity results to researchers, managers, and the public alike. This sort of analysis can live used to model pairwise relationships between spatial data points by breaking down individual-based output into a string of nodes (habitat sites) and edges (directed connections between habitat sites). They then used these nodes and edges to examine the relative significance 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 balance of realized edges out of replete viable edges, is a multi-site measure of connectivity. Areas with a higher edge density fill 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 viable population structure and degree of exchange among the marine resources of local communities.

    The distribution of shortest path length is likewise 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 produce a connection. medium shortest path, therefore, is a descriptive statistic to rate connectivity of a network. If two sites are unconnected, it is viable to fill infinite-length shortest paths; here, these illimitable values were celebrated but not included in final analyses.

    Networks can likewise live broken in connected components (Csardi & Nepusz, 2006). A weakly connected component (WCC) is a subgraph in which replete nodes are not reachable by other nodes. A network split into multiple WCCs indicates separate populations that execute not exchange any individuals, and a large number of WCCs indicates a low degree of island-wide connectivity. A strongly connected component (SCC) is a subgraph in which replete nodes are directly connected and indicates a tall degree of connectivity. A region with many diminutive SCCs can betoken tall local connectivity but low island-wide connectivity. Furthermore, component analysis can identify slash nodes, or nodes that, if removed, demolish a network into multiple WCCs. Pinpointing these slash nodes can identify potential essential 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 essential 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 tall out-degree seed a large number of other sites, and betoken potentially essential larval sources, while habitat sites with a low in-degree rely on a limited number of larval sources and may therefore live contingent 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 essential to overall connectivity on a multigenerational timescale. BC was weighted with the balance 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 comprise sites on islands other than Moloka‘i in their calculations of edge density, shortest paths, connected components, slash 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 balance 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 likewise examined medium 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 balance of larvae settled out of the total number of larvae spawned. Local retention is measured as the balance of larvae spawned from a site that settle at the identical site. Shortest path is measured as the minimum number of steps needed to connect two sites. Strongly connected sites refers to the balance of sites in a network that belong to a strongly connected component. spell 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, sense that on average, it would pick fewer generations for these species to demographically bridge any given pair of sites. Using maximum shortest path, it could pick these species three generations at most to connect sites. Cellana spp. and P. meandrina, by comparison, could pick as many as five generations. Other medium- and long-dispersing species showed relatively equivalent shortest-path distributions, with trevally species showing the highest spell path length and therefore the lowest island-scale connectivity.

    The number and size of weakly-connected and strongly-connected components in a network is likewise 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 replete 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 betoken fragmentation with seven and six SCCs respectively, ranging in size from two to 22 sites. This SCC pattern suggests low global connectivity but tall 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 likewise essential 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 betoken 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 spell 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 spell distance of 24.06 ± 31.33 km, medium dispersers travelled a spell distance of 52.71 ± 40.37 km, and long dispersers travelled the farthest, at a spell of 89.41 ± 41.43 km. However, regardless of PLD, there were essentially two peaks of spell 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 spell 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 balance 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 medium of 74% of larvae from short-dispersing species settled back to Moloka‘i, as compared to an medium of 41% of medium dispersers and 9% of long dispersers. A large balance of larvae likewise 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 kind of lower. Larvae of every species settled to Lana‘i, and settlement to this island made up less than 5% of settled larvae across replete 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 replete 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 likewise examined coast-specific patterns of rearward settlement balance to other islands, discarding connections with a very low balance 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 replete larvae settling to O‘ahu from Moloka‘i. The east and south shores of Moloka‘i had the highest medium 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 medium 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 fill the potential to export larvae to neighboring islands.

    Temporal settlement profiles likewise 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 period of time. Some species likewise 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.) loom 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 replete species, 84% of individuals settled back to the identical coast on which they were spawned rather than a different coast on Moloka‘i. Excluding connections with a very low balance of larvae (<0.1% of total larvae of that species that settled to Moloka‘i), they institute that the balance 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 tall degree of local retention in every species except for the long-dispersing Panulirus spp., and the east coast likewise had tall local retention overall. Between coasts, a tall balance 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) medium rearward settlement balance by species per pair of coastlines, calculated by the number of larvae settling at site s from site o divided by replete settled larvae at site s. Directional coastline pairs (Spawn > Settlement) are ordered from left to right by increasing median settlement proportion. (B) Heatmap of edge density for coast-specific networks by species. Density is calculated by the number of replete realized paths out of total viable paths, disregarding directionality.

    We likewise calculated edge density, including replete connections between coasts on Moloka‘i regardless of settlement balance (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 tall edge density for short and medium dispersers (0.16–0.39) but low for long dispersers (<0.005). The north shore likewise showed relatively tall 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 replete 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 replete species (<0.05), a divide that was especially several 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 analyze relationships between these regions. Arranging model output into node-edge networks clarified pathways and regions of note, and revealed several patterns which did not ensue 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 replete 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 fill a low out-degree, red nodes fill a medium out-degree, and black nodes fill a tall out-degree. Red edges are connections in a strongly connected component, while gray edges are not fraction of a strongly connected component (although may quiet picture substantial connections). Edge thickness represents log-transformed balance 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 tall 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 tall BC scores. P. meandrina and C. strigosus showed several regions along the south shore with tall 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 replete species. Betweenness centrality is a measure of the number of paths that pass through a certain region; a tall score suggests potentially essential 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 fill replete been normalized to values between 0 to 1 per species. Local retention is measured as the balance of larvae that settled back to their spawn site out of replete 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 stalwart source nor sink. Gray values for Cellana spp. denote a exigency 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 likewise 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, replete 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 replete species. Puko‘o, Pauwalu Harbor, and Cape Halawa were either debilitated net sources or neither sources nor sinks, which corresponds to the tall levels of local retention observed at these sites. Pala‘au and Mo‘omomi acted as either debilitated sinks or sources for short dispersers and as sources for long dispersers.

    Only four networks showed regional cut-nodes, or nodes that, if removed, demolish 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 diminutive Bay and Lono Harbor, respectively. C. perspicillatus, and S. rubroviolaceus showed a similar pattern in regards to Mokio Point; removal of this node isolated diminutive 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 replete 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 replete regions on Moloka‘i, East Kalaupapa Peninsula was the single largest exporter of larvae to Hawai‘i Island, accounting for 19% of replete larvae transported from Moloka‘i to this island; West Kalaupapa Peninsula accounted for another 10%. The park likewise contributed 22% of replete 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, sense that replete 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 replete 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 essential role for long-dispersing species; settlement from Kalaupapa made up 18%–29% of replete 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 tall out-degree, or number of outgoing connections to other regions, and West Kalaupapa was likewise one of the few regions on Moloka‘i that acted as a net larval source across replete species (Fig. 8). Their study has likewise demonstrated that different regions of a marine protected zone can potentially effect different roles, even in a diminutive 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 balance of dispersal along that edge; red indicates higher balance 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 capacity and empirical plumb distributions of larvae into their model to multiply biological realism, and assess how such traits impact predictions of larval dispersal. The Wong-Ala et al. (2018) IBM provides a highly springy model framework that can easily live modified to incorporate either additional species-specific data or entirely current biological traits. In this study, they included specific spawning seasons for replete 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 conduct become available, such information can live easily added for these species and others. Some potential additions to future iterations of the model might comprise 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 plumb 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 fill 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 betoken greater connectivity along the northern coast, while the goatfish P. porphyreus shows higher connectivity along the eastern half of the island. These species fill 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 loom play a role in the probability of rare long-distance dispersal, minimum PLD appears to live the main driver of medium dispersal distance (Fig. 2). Overall, species with a shorter minimum PLD had higher settlement success, shorter spell 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 likewise influenced connectivity patterns. Because mesoscale current patterns can vary substantially over the course of the year, the timing of spawning for certain species may live critical 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 large balance of larvae settled to O‘ahu, which is kind of 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 viable at certain 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 likewise 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 fill 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 replete sites. This simplification may lead us to under- or over-estimate the strength of connections between sites. exigency of adequate data likewise 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 smooth without genetic analysis, they used genus-level larval distribution data (Boehlert & Mundy, 1996), or lacking that, an rate of 50–100 m as a depth layer that is generally more enriched with larvae (Boehlert, Watson & Sun, 1992; Wren & Kobayashi, 2016). They likewise estimated PLD in several cases using congener-level data (see Table 1). While specificity is pattern for making informed management decisions about a certain 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 strike patterns of connectivity during these years. El Niño can fill a particularly stalwart impact on coral reproduction, since the warm currents associated with these events can lead to stern temperature stress (Glynn & D’Croz, 1990; Wood et al., 2016). While there has been runt study to date on the effects of ENSO on fine-scale connectivity, previous drudgery has demonstrated increased variability during these events. For example, Wood et al. (2016) showed a reduce in eastward Pacific dispersal during El Niño years, but an multiply in westward dispersal, and Treml et al. (2008) showed unique connections in the West Pacific as well as an multiply in connectivity during El Niño. While these effects are difficult to predict, especially at such a diminutive scale, additional model years would multiply assurance 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 comprise them in the MITgcm. Storlazzi et al. (2017) showed that tidal forces did strike larval dispersal in Maui Nui, underlining the significance of including both fine-scale, short-duration models and coarser-scale, long-duration models in final management decisions.

    We likewise restrict 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 live biased by spatial resolution of the ocean model, since involved coastal processes can live smoothed and therefore impact larval trajectories. To restrict this bias, they focused mainly on coastal and regional connectivity on scales greater than the current resolution. They likewise used the finest-scale current products available for their study area, and their results betoken common agreement with similar studies of the region that consume a coarser resolution (Wren & Kobayashi, 2016) and a finer resolution (Storlazzi et al., 2017). Also, while knowledge of island-scale connectivity is essential 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 warp their analysis. Likewise, they cannot foretell the balance of larvae settling to other islands that originated from Moloka‘i, or the balance of larvae on Moloka‘i that originated from other islands.

    It is likewise essential to note scale in relation to measures of connectivity; they hope that long-dispersing species such as Caranx spp. and Panulirus spp. will betoken much higher measures of connectivity when measured across the gross archipelago as opposed to a single island. The cut-nodes observed in these species may not actually demolish up populations on a large scale due to this inter-island connectivity. Nevertheless, cut-nodes in species with short- and medium-length PLD may indeed charge essential habitat locations, especially in terms of providing links between two otherwise disconnected coasts. It may live that for certain species or certain regions, stock replenishment relies on larval import from other islands, underscoring the significance 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 conduct 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 exigency for multi-scale management, specifically tailored to local and regional connectivity patterns and the suite of target species. Even on such a diminutive 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 live more at risk of isolation. Seasonal variation should likewise live 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 betoken similar network dynamics may benefit from similar management strategies. Areas that act as larval sources either by balance of larvae (high source–sink index) or number of sites (high out-degree) should receive management consideration. On Moloka‘i, across replete species in their study, these sources fell mostly on the northern and eastern coasts. Maintenance of these areas is especially essential 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 essential intergenerational pathways, particularly as a means of connecting north-coast and south-coast populations, which showed a exigency of connectivity both in total number of connections (edge density) and balance of larvae. Both of these connectivity measures were included because edge density includes replete connections, even those with a very diminutive balance of larvae, and may therefore comprise rare dispersal events that are of runt relevance to managers. Additionally, edge density comparisons between networks should live viewed with the caveat that these networks execute not necessarily fill the identical number of nodes. Nevertheless, both edge density and balance betoken very similar patterns, and comprise 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 likewise reckon 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 fill more direct connections and therefore may live more resilient to local population loss. keeping should live 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 drill may live especially essential for species for which they rate multiple diminutive SCCs, such as Cellana spp. or P. meandrina.

    Kalaupapa Peninsula emerged as an essential 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 replete species, and likewise exported larvae to sites along the east and west shores in replete 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, likewise on the north shore) that acted as a net larval source across replete species. Eastern Kalaupapa Peninsula was particularly highly connected, and was fraction of a strongly connected component in every species. The Park likewise 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 live 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 foretell some smooth of shoreline-specific population structure for most species included in the study (Fig. 6). Unfortunately, genetic analyses to date fill 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 diminutive scale genetic analyses because there are species, such as the coral P. meandrina, for which the model predicts transparent separation of north-shore and south-shore populations which should live 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 essential for conservation. In this study, they contribute to the growing corpse of drudgery 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 abet 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 likewise by other life-history traits such as spawning season, moon-phase spawning, and ontogenetic changes in larval depth. tall local retention of larvae with a short- or medium-length PLD is consistent with population genetic studies of the area. They likewise 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 essential multigenerational pathways. Connectivity is only one piece of the puzzle of MPA effectiveness, which must likewise 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 utensil 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, tall 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. replete drifter information was extracted from the GDP Drifter Data Assembly center (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 correct day where they entered the model domain, at the uppermost depth layer of their oceanographic model (0–5 m).

    Selected larval depth distributions.

    Modeled plumb larval distributions for Caranx spp. (left), S. rubroviolaceus and C. perspicillatus (middle), and P. porphyreus (right), using data from the 1996 NOAA ichthyoplankton plumb 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 replete 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 balance are transported through Maui Nui.

    Eddy differences by depth layer.

    Differences in eddy pattern and strength in surface layers (A, 2.5 m) vs. deep 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, tall velocity). While large gyres remain consistent at different depths, smaller features vary along this gradient. For example, the currents around Kaho‘olawe, the diminutive gyre off the eastern coast of O‘ahu, and currents to the north of Maui replete vary in direction and/or velocity.



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