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ST. LOUIS--(business WIRE)--Perficient, Inc. (NASDAQ: PRFT) (“Perficient”), a leading digital transformation consulting company serving international 2000® and different gigantic commercial enterprise customers every through North the us, announced it has been named IBM’s 2019 Watson Commerce enterprise associate of the year. The IBM Excellence Award, announced birthright through IBM’s PartnerWorld at suppose 2019, recognizes Perficient’s ongoing increase and relationships with key shoppers, and conviction management around the IBM Watson client assignation Commerce platform as an quintessential Part for digital transformation.
“Our approach to commerce is concentrated on crafting a experience, connecting with purchasers, and supplying a seamless customer event across channels and every the course through the commercial enterprise, imperatives in nowadays’s buyer-pushed world,” mentioned Steve Gatto, countrywide sales director, Commerce solutions, Perficient Digital. “collectively, with their valued clientele, we’re remodeling agencies in a mode that no longer best drives growth but strengthens their overall company, and they continually evolve their choices to retain customers at the excellent of their online game. We’re honored to be identified with the aid of IBM, and we’re anticipating sharing their innovative options during IBM mediate 2019.”
Perficient Digital Takes Commerce solutions beyond Transactions to radically change the customer Lifecycle for a global varied brand
With branded manufacturers and distributors under pressure from the melodramatic shift to online purchasing, a worldwide diverse company sought to digitally radically change its commerce business. In partnership with Perficient Digital, the two enterprises delivered optimized client income, updated product information (PIM), and streamlined the ordering technique through construction of a B2B portal. With the implementation of IBM’s Sterling Order administration device (OMS), and Perficient’s knowledge, the assorted brand is future-proofing its trade to align with trade trends and market opportunities.
furthermore, the business’s OMS will provide them more desirable flexibility in managing complicated order administration scenarios, stronger reliability so as processing and fulfilment, and a expense reduction in implementing across its enterprise. it's going to extra permit the organization to bring carrier enhancements to its purchasers, optimize its pricing, promoting and measure supply chain, boost earnings because of superior stock visibility, and slice back prices through better efficiencies so as visibility.
Perficient Digital Enhances the on-line consumer undergo for a number one fabric Retailer
In a market that has historically trusted brick-and-mortar experiences, a number one cloth and craft retailer turned into challenged with extending the consumer adventure on-line. Perficient partnered with the trade to Put in compel an IBM Watson Commerce retort that offered up to date visibility of its stock and greater tracking of its product amount, place, and availability. using IBM Order management, Perficient further more advantageous the solution via cloud migration that presents a lone view of give and demand, orchestrates order success processes throughout buy on-line Pickup In save (BOPIS) and Ship-from-shop (SFS), and empowers trade representatives to enhanced serve purchasers both in name centers and in-save engagements.
“Perficient has been deploying IBM Commerce solutions for nearly 20 years, proposing conclusion-to-end digital commerce solutions that embody multiple channels, and convey seamless and efficient experiences throughout their entire business,” observed Sameer Peera, average supervisor, Perficient’s commerce practice. “With the contemporary information that HCL took over building of IBM WebSphere Portal, IBM internet content management and internet event manufacturing facility, their consumers proceed to engage us for aid with their digital commerce ideas. We’re completely jubilant to be their go-to accomplice as they navigate the changing market landscape and deliver for his or her customers.”
Perficient erudition in motion at IBM believe 2019
apart from its award-profitable commerce solution abilities, Perficient specialists are accessible throughout the IBM feel 2019 conference in sales space #320 to talk about its undergo and capabilities across the IBM portfolio , chiefly cloud, cognitive, facts, analytics, DevOps, IoT, content management, BPM, connectivity, commerce, mobile, and customer engagement.
whereas IBM has introduced its plans to sell its commerce portfolio, the intelligence of its acquisition of crimson Hat additionally signaled the criticality cloud construction and dawn play in a hit conclusion-to-conclusion digital transformations. As an IBM global Elite accomplice, one in every of most efficient seven companions with that fame globally, and a purple Hat Premier partner, Perficient is smartly positioned to drudgery with each companies via this transition. And, their consultants could be on hand every the course through IBM suppose to talk about the course to navigate the cloud market, participate key client success reports, and provide strategic erudition on the alternatives forward for valued clientele.
“technology is altering so unexpectedly, and firms exigency to preserve pace or mug disruption,” spoke of Hari Madamalla, vice chairman, emerging options, Perficient. “With erudition and adventure in every features of the commerce journey, to leading cloud, internet hosting, managed capabilities and assist options, companies gyrate to Perficient as a go-to associate for his or her digital transformations.”
join a yoke of Perficient discipline depend consultants and their customers as they current every through six IBM suppose classes, together with:
As a Platinum IBM company partner, Perficient holds more than 30 awards throughout its 20-year partnership historical past. The company is an award-winning, certified utility cost Plus retort provider and one of the vital few companions to receive dozens of IBM professional stage software competency achievements.
For updates during the event and after, combine with Perficient experts online by course of viewing Perficient and Perficient Digital’s blogs, or comply with us on Twitter @Perficient and @PRFTDigital.
Perficient is the main digital transformation consulting enterprise serving global 2000® and commercial enterprise valued clientele during North the us. With unparalleled counsel technology, administration consulting, and inventive capabilities, Perficient and its Perficient Digital agency deliver imaginative and prescient, execution, and value with astonishing digital experience, enterprise optimization, and trade solutions. Their drudgery allows consumers to enhance productiveness and competitiveness; develop and invent stronger relationships with shoppers, suppliers, and companions; and in the reduction of charges. Perficient's authorities serve clients from a network of offices throughout North the us and offshore areas in India and China. Traded on the Nasdaq international opt for Market, Perficient is a member of the Russell 2000 index and the S&P SmallCap 600 index. Perficient is an award-winning Adobe Premier partner, Platinum level IBM enterprise accomplice, a Microsoft country wide provider company and Gold certified partner, an Oracle Platinum companion, an advanced Pivotal able partner, a Gold Salesforce Consulting partner, and a Sitecore Platinum associate. For more tips, quest counsel from www.perficient.com.
protected Harbor observation
probably the most statements contained during this information free up that aren't simply ragged statements focus on future expectations or state different forward-looking suggestions related to monetary results and company outlook for 2018. these statements are field to general and unknown dangers, uncertainties, and different components that could trigger the precise consequences to vary materially from these pondered by the statements. The forward-looking assistance is according to administration’s existing intent, belief, expectations, estimates, and projections concerning their company and their trade. recall to be mindful that those statements handiest reflect their predictions. specific routine or results may additionally fluctuate considerably. essential factors that might occasions their precise outcomes to be materially distinctive from the ahead-searching statements embrace (however don't appear to be restricted to) those disclosed beneath the heading “possibility factors” in their annual document on contour 10-k for the year ended December 31, 2017.
Bangalore: IBM nowadays talked about that Metro shoes Ltd, considered one of India’s suitable multi-brand sneakers chains, will launch a brand recent Digital Commerce platform powered by using Watson customer assignation hosted on IBM Cloud.
this might encompass IBM Watson Order management and Commerce for seamless digital engagement. Working with IBM enterprise ally CEBS worldwide, IBM options will no longer simplest uphold power superior customer experiences and recent ranges of console but convey efficiencies to the supply chain.
With a national footprint of 350 actual showrooms, an increasing manufacturer portfolio and changing client preferences, Metro footwear Ltd turned into facing challenges in managing orders coming from dissimilar on-line structures.
past it had unreliable software that brought about want of visibility of real-time information of income, inventory region and returns together with inventory management challenges. Metro footwear Ltd vital to enrich online presence for some of their widely wide-spread interior brands which were getting low visibility impacting ordinary income.
"expertise is redefining client assignation and will be the necessary thing differentiator for retail manufacturers of the long run. We’re excited to collaborate with IBM and CEBS to embark on their digital transformation event,” said Alisha Malik, vp, Digital, Metro shoes.
“With IBM’s competencies in the omni-channel commerce and retail area, they are assured that these adjustments will not handiest aid hurry up the execution of their method, however also give us an locality over competition. At Metro shoes, they strongly believe that the recent solution will increase the general user experience, thereby increasing revisits, traffic and loyalty,” brought Malik.
With IBM, Metro shoes Ltd can benefit recent levels of client perception, which may also be used to customize the online adventure for each of the web site. enterprise will capable of argue off every of its manufacturers and advocate specific items in accordance with insights shared through customers on a lone platform.
This customized undergo will embrace recent and simple fulfillment alternate options corresponding to purchase on-line, select up in save, reserve in save and simple returns. as a result of these recent capabilities, Metro shoes should be able to raise every visitor’s adventure on the web site by course of enabling commerce practitioners with cognitive tools which aid them bring omni-channel experiences that Have interaction valued clientele and power earnings.
With IBM’s know-how capabilities and CEBS erudition with market integration, Metro footwear as a company/seller will also be in a position to integrate with more than 14 e-marketplaces enjoy Amazon, Flipkart and other main portals with a centralized procedure and inventory engine to permit Metro to scale as much as the wants of a becoming marketplace business. further, IBM Cloud will uphold elevate the skill to configure cumbersome workloads and thereby convey performance required for height usage every the course through the searching season.
Nishant Kalra, company unit chief – IBM Watson consumer assignation - India/South Asia delivered, “IBM is at the forefront of helping consumers embody more recent how you can drudgery and digitally remodeling the course they interact with their conclusion valued clientele. we're jubilant to be Part of Metro shoes’ digital transformation adventure via delivering sophisticated digital commerce adventure, leveraging the stores via merging them with online, and at terminal driving company advocacy. IBM in affiliation with CEBS will permit profound innovation, quicker-go-to-market and streamline processes for scalability.”
The IBM platform will create a bridge between its online and offline company which the retailer up to now lacked. With the recent built-in lone view, Metro footwear sooner or later will be capable of utilize insights won from the digital realm to design special providing for clients as they stroll into any of their outlets. in consequence, they can deem what purchasers want, ensure availability when and where they want it and even examine cross promoting and upselling throughout their a number of brands.
For Metro footwear, IBM Watson Order management and Commerce solutions can pave approach for IBM’s cognitive technologies to deliver insights that attend them provide purchasers with personalised recommendations and an more desirable consumer journey –from click on to beginning.
“With over 15 years of event in developing e-business tools, CEBS has been a relied on options issuer and associate for businesses across the globe,” famed Satish Swaroop, President, CEBS global. Their advantageous and flexible application options paired with IBM’s profound technology talents will provide Metro shoes a real-time, centralized gadget for customer administration.”
aspect Roberts, WA and Vancouver, BC - February 14, 2019 (Investorideas.com Newswire) Investorideas.com (www.investorideas.com), a world investor information source overlaying artificial Intelligence (AI) brings you today's edition of The AI Eye - gazing inventory news, deal tracker and advancements in artificial intelligence.
The #AI Eye: IBM ( $IBM) to invest $50 Million in analysis With sanatorium and medical middle and Qualcomm ($QCOM)
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modern day Column - The AI Eye - staring at stock information, deal tracker and developments in artificial intelligence
world AI Chipset Market to strategy $13.4 Billion USD by using 2023
intellect Commerce has posted recent research by means of In For boom, indicating that the world AI chipset market will mode $13.four billion USD by means of 2023, with the united states producing over $three.5 billion. An excerpt from the document summary reads:
The AI chipset marketplace is poised to seriously change the entire embedded tackle ecosystem with a large number of AI capabilities comparable to profound laptop getting to know, photograph detection, and a lot of others. this may also be transformational for current censorious enterprise functions corresponding to id management, authentication, and cybersecurity.
Multi-processor AI chipsets be trained from the atmosphere, users, and machines to uncover hidden pattern amongst facts, forecast actionable perception, and effect movements in keeping with specific situations. AI chipsets will gyrate into an essential component of each AI utility/systems in addition to crucial assist of any records-intensive operation as they enormously enrich processing for a number of functions in addition to enhance universal computing performance.
IBM to invent investments $50 Million in research With hospital and medical hub and Qualcomm, Accenture and Kellogg team to develop VR Merchandising solution
stocks discussed: (NYSE:IBM) (NasdaqGS:QCOM) (NYSE:ACN) (NYSE:ok)
IBM, (NYSE:IBM) through Watson fitness, plans to invent investments $50 million in analysis collaborations with Brigham and girls's medical institution, a instructing health hub of Harvard scientific college, and Vanderbilt school clinical middle - to enhance the science of artificial intelligence (AI) and its software to main public fitness considerations. drudgery with the two associations will be cognizant of constructing options to fitness issues gold measure faultless for AI. Kyu Rhee, M.D., M.P.P., vp and chief fitness officer at IBM Watson fitness, commented:
"building on the MIT-IBM Watson Lab announced terminal yr, this collaboration will embrace contributions from IBM Watson fitness's lengthy-standing commitment to scientific analysis and their credence that working along with the world's main associations is the fastest course to strengthen, strengthen, and abide in intelligence purposeful solutions that pellucid up probably the most world's biggest fitness challenges."
QUALCOMM incorporated (NasdaqGS:QCOM) subsidiary Qualcomm technologies, Accenture (NYSE:ACN) and Kellogg enterprise (NYSE:okay) are collaborating to better and pilot a digital verity merchandise answer. The VR merchandising solution makes utilize of a Qualcomm VR reference design headset, powered with the aid of Qualcomm Snapdragon 845 mobile VR Platform and is developed by course of the Accenture extended reality (XR) practice. checking out turned into finished in collaboration with Kellogg across the launch of their Pop muffins Bites product. Raffaella digital camera, world head, Innovation & Market approach, Accenture extended fact, defined the tech:
"Our VR merchandising solution has the advantage to seriously change product placement by means of inspecting purchaser purchasing habits in a holistic approach. by means of combining the verve of VR with eye-monitoring and analytics capabilities, it enables massive recent insights to be captured whereas consumers store through monitoring the state and the course they evaluate every items across a gross shelf or aisle. finally, this permits product placement choices to be made that may positively Have an impact on total brand sales, versus simplest lone product revenue."
Sam Mowers, Investorideas.com
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The exemplar desktop management system should provide a "push" technology that allows administrators to deploy software to multiple PCs simultaneously from a centralized administrative console, without requiring cease user intervention or a technician to visit the desktop. Deployment tasks can be executed immediately or scheduled for off-hours in order to minimize impact on cease user productivity or network bandwidth.
The exemplar desktop management should be an open and scalable system that supports a purview of server platforms, such as Solaris, HP-UX, NT, and both recent and legacy Microsoft client platforms (DOS, Windows 3.x, Windows 95, Windows 98 and NT 4.0). The system should be standards-based, with uphold for measure protocols, including IP, DHCP and BOOTP and measure Wired for Management (WfM)-enabled PC platforms (DMI 2.0, Remote Wake Up and PXE). The desktop management system should also uphold legacy PCs via boot PROMs or boot floppies for measure NICs from Intel, 3Com, SMC and others.
Essential to the equation should also be a progression of open, programmable interfaces that allow customers and partners to extend and customize the system. The system should be carefully designed to provide scalability across large numbers of clients and servers, including the faculty to group PCs and software packages into deployment groups and the faculty to intelligently manage network bandwidth.
Windows 2000 promises to address many of these limitations but will not be deployed in most production environments until 2001, according to industry analysts, such as the GartnerGroup; moreover, in order to select advantage of these recent desktop capabilities, organizations must migrate to an exclusive, all-Windows 2000 environment on both clients and servers, which may be unrealistic for many corporations, the preponderance of non-NT desktops.
The exemplar desktop management system should configure operating systems, applications and desktop parameters on an ongoing basis. These operations should be executed simultaneously on multiple PCs from central administrative consoles, and should deliver three censorious capabilities: pre-OS installation, remote uphold and no cease user intervention. These three powerful capabilities result in enterprise desktop management nirvana: lower PC total cost of ownership (TCO).
As computing environments scramble toward increasingly distributed and heterogeneous environments, many IT organizations are now implementing centralized management systems for managing network resources such as routers and printers, application and database servers (e.g., SAP, Oracle, Lotus Domino), and desktop PCs.
The driving compel behind these implementations is the realization that centralized management systems are required to cost-effectively manage the complicated and mission-critical nature of networked systems. For most IT organizations, centralized management systems are the only course of approaching the very level of reliability, availability and control as has been available with mainframe environments of the past.
Centralized desktop management tools are seen as a key requirement for reducing the TCO associated with desktop uphold and the rapid growth of desktops in enterprise environments, and as a key enabler for delivering a higher trait of IT service to end-user organizations.
In addition, most IT organizations now perceive PC desktops as a mission-critical corporate resource that should be managed as Part of an overall networked environment – embodying the philosophy "the network is the computer" – rather than treated as a progression of isolated standalone resources to be managed on an individual basis.
Tactical requirements for desktop management typically arise in connection with urgent short-term projects such as desktop OS migrations (e.g., from Windows 3.1 or OS/2 to Windows 95/98 or NT), Y2K desktop remediation projects, large-scale deployments of recent and more powerful PC hardware to uphold trade unit requirements (Web access, e-commerce, multi-media, etc.), or deployment of recent and complicated applications, such as Lotus Notes or Netscape Communicator.
A successful desktop management system should provide three key technology differentiators versus conventional electronic software distribution systems: pre-OS technology, endemic installation engine and continuous configuration.
The faculty to install and configure operating systems on PCs that are recent or are unable to boot due to corruption or misconfiguration is called pre-OS capability. Pre-OS technology enables the desktop management system to install operating systems on a PC regardless of its state (e.g., corrupted hard disk, won’t boot, virgin hard drive, etc.). If a desktop management system cannot effect these functions, then its value is tremendously reduced, as the (re)installation represents a major task of IT uphold staffs.
Pre-OS technology takes control of the PC even in the absence of a working operating system, and automates the installation and configuration of operating systems on recent PCs out of the box. It also acts in a attend desk setting for PCs that are unable to boot due to misconfiguration or corruption – without requiring a technician to visit the desktop or any end-user interaction.
The exemplar desktop management system should install applications by running the vendor-supplied endemic installation program (setup.exe) on the target client. Its desktop agent should click through the installation wizard using the installation options specified by the administrator before launching the installation task. This allows each installation to be easily customized on a per-user or group-wide basis via a point-and-click administrative interface. No editing of script or batch files is required. In addition, this approach provides a high level of reliability because it leverages the vendor-supplied installation procedure that adapts in real-time to the hardware and software configuration of the target system.
The exemplar desktop management system should manage PC configurations across the entire PC lifecycle, not just during the initial application installation. It should be able to deploy action packages to add a recent printer or change printer settings, change the IP address or login password of a PC, rush an anti-virus or inventory scan, or execute a BIOS glitter as Part of a Y2K remediation effort.
It is also helpful for a desktop management system to maintain a unique client configuration database that stores a history of every software packages that Have been installed, as well as the configuration parameters that were used during installation. This database can be used to rebuild the desktop to its previous configuration at any time, in a completely unattended manner.
Intel WfM Initiative
The Intel WfM initiative is intended to significantly enhance manageability and reduce TCO for desktop PCs. According to Intel, approximately 14 million WfM-enabled PCs Have shipped since the cease of 1998.
WfM V2 will offer enhanced manageability for mobile PCs, enhanced security via encryption and authentication, and uphold for recent hardware/software asset management standards such as CIM (Common Information Model) and WBEM (Web-Based Enterprise Management). WfM V2 is currently in beta with PC manufacturers and is expected to be available in mid-1999.
In addition, 100 percent of the trade PCs offered from vendors, such as Dell, Compaq, IBM and HP are currently shipping with WfM capabilities. The exemplar desktop management solution should fully uphold the WfM V1.1 specification, which consists of three components:
Remote Wake Up (RWU): Allows IT organizations to execute administrative tasks remotely during off-hours to preserve network bandwidth and user productivity.
The PC client is automatically "awakened" under centralized control of the desktop management system, and directed to install and configure operating systems and applications.
DMI 2.0 (Desktop Management Interface): Developed by the Desktop Management task compel (DMTF), DMI 2.0 allows attend Desk personnel to scan the hardware and software properties of remote PCs in real-time to aid in troubleshooting.
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Knowledge of population connectivity is necessary for efficient 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 censorious 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 Have a bi-phasic life history in which adults array limited geographic purview and high 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 Have been shown to Have a home purview of <1 km as adults (Meyer et al., 2000; Meyer, Papastamatiou & Clark, 2010). Depending on species, the mobile planktonic stage can terminal 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). erudition of larval dispersal patterns can be used to inform efficient 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 create in low densities across large areas of the open ocean (Clarke, 1991; Wren & Kobayashi, 2016). A variety of genetic and chemistry techniques Have 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 field and laboratory data Have also become a valuable instrument 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 impress 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 every play a role in shaping connectivity patterns. Physical factors such as temperature, bathymetry, and current direction can also 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 rush 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 complicated and varies seasonally (Friedlander et al., 2005; Vaz et al., 2013).
Hawaiian marine communities mug 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 wrangle for implementation of a more holistic approach than traditional single-species maximum sustainable succumb techniques, which Have proven ineffective (Goodyear, 1996; Hilborn, 2011). There is a general movement toward the utilize of ecosystem-based management, which requires erudition 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 locality (MPA) located on the north shore of Moloka‘i, an island in the Maui Nui complicated 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 Have 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 locality 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 Have 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, relate 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.
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 obscure gray. Spawn season and timing from traditional ecological erudition shared by cultural practitioners on the island. Asterisk indicates that congener-level data was used.
# of larvae spawned
Spawningday of year
Spawning hour of day
Spawning moon phase
Larval depth (m)
1–60 & 121–181
Ko’a/ Cauliflower coral
Moi/ Pacific threadfin
Uhu uliuli/ Spectacled parrotfish
Uhu palukaluka/ Reddlip parrotfish
Kumu/ Whitesaddle Goatfish
Sand, rock, reef10
Kole/ Spotted surgeonfish
Rock, reef, rubble10
‘Ōmilu/ Bluefin trevally
Ulua/ Giant trevally
Sand, rock, reef10
Ula/ Spiny lobster
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 relate 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 locality that includes the islands of Moloka‘i, Maui, Lana‘i, Kaho‘olawe, O‘ahu, and Hawai‘i. While Ni‘ihau and southern Kaua’i also tumble within the domain, they discarded connectivity to these islands because they prevaricate within the 0.5° boundary zone of the current model. boundary conditions are enforced over 20 grid points on every sides of the model domain. Vertically, the model is divided into 50 layers that increase 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 general 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 accomplish not embrace 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 hub (Fig. S2) (Elipot et al., 2016), as well as other current models of the locality (Wren et al., 2016; Storlazzi et al., 2017).
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 sever 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 mode (i.e., fourth-order Runge–Kutta) because they did not commemorate a contrast at this timestep length. Biological processes modeled embrace 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 cease 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 limn 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 every larvae were assumed to Have 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 Have demonstrated that fish larvae Have a high 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 aspect to simulate eggs pre-hatch, followed by a pelagic larval aspect 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.) accomplish not Have 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.
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 locality not covered by sand or mud, for a total of 87 sites. Number of adults was assumed equal at 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 very benthic habitat classification and current wave direction, and/or were Part of the very 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 function 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 calculate dispersal kernels to examine and compare species-specific distributions. They also 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 also 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 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 limn exogenous sinks. For this reason, settlement to other islands was not included in source–sink index calculations.
We also 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 source site (o), by scaling the number of successful settlers from site o settling at site s to every successful settlers originating from site o. Forward balance can be represented as Pso = Sos∕∑So. They also calculated rearward settlement proportion, or the balance of settlers from a specific source site (o) observed at settlement site (s), by scaling the number of settlers observed at site s originating from site o to every settlers observed at site s. The rearward balance can be represented as Pos = Sos∕∑Ss.
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 also 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 also provides a powerful instrument for spatial visualization, allowing for rapid, intuitive communication of connectivity results to researchers, managers, and the public alike. This sort of analysis can be used to model pairwise relationships between spatial data points by breaking down individual-based output into a progression 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 every viable edges, is a multi-site measure of connectivity. Areas with a higher edge density Have 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 also 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 participate 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 invent a connection. average shortest path, therefore, is a descriptive statistic to assess connectivity of a network. If two sites are unconnected, it is viable to Have infinite-length shortest paths; here, these boundless values were famed but not included in final analyses.
Networks can also be broken in connected components (Csardi & Nepusz, 2006). A weakly connected component (WCC) is a subgraph in which every nodes are not reachable by other nodes. A network split into multiple WCCs indicates sever populations that accomplish 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 every nodes are directly connected and indicates a high degree of connectivity. A region with many tiny SCCs can argue high local connectivity but low island-wide connectivity. Furthermore, component analysis can identify slice nodes, or nodes that, if removed, smash a network into multiple WCCs. Pinpointing these slice nodes can identify potential necessary 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 necessary 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 high out-degree seed a large number of other sites, and argue potentially necessary larval sources, while habitat sites with a low in-degree rely on a limited number of larval sources and may therefore be 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 argue connectivity pathways or ‘chokepoints’ that are necessary 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 embrace sites on islands other than Moloka‘i in their calculations of edge density, shortest paths, connected components, slice nodes, in- and out-degree, or betweenness centrality in order to focus on within-island patterns of connectivity.
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 aspect 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 also examined average 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 very 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. involve 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, acceptation 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 involve path length and therefore the lowest island-scale connectivity.
The number and size of weakly-connected and strongly-connected components in a network is also 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 every sites in the network falling into a lone SCC. Cellana spp. and P. meandrina each had approximately 60% of sites included in a SCC, but both argue fragmentation with seven and six SCCs respectively, ranging in size from two to 22 sites. This SCC pattern suggests low global connectivity but high 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 lone SCC. In contrast, only 26% of C. ignoblis sites fell within a lone SCC. It is also necessary 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 argue 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 near to home, while few long dispersers are retained at or near their spawning sites.
Minimum PLD was positively correlated with involve 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 involve distance of 24.06 ± 31.33 km, medium dispersers travelled a involve distance of 52.71 ± 40.37 km, and long dispersers travelled the farthest, at a involve of 89.41 ± 41.43 km. However, regardless of PLD, there were essentially two peaks of involve 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 involve at 8.85 km, indicating that most of these larvae settled relatively near 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 average of 74% of larvae from short-dispersing species settled back to Moloka‘i, as compared to an average of 41% of medium dispersers and 9% of long dispersers. A large balance of larvae also 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 rather lower. Larvae of every species settled to Lana‘i, and settlement to this island made up less than 5% of settled larvae across 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 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 also 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 every larvae settling to O‘ahu from Moloka‘i. The east and south shores of Moloka‘i had the highest average 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 average 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 argue that multiple coasts of Moloka‘i Have the potential to export larvae to neighboring islands.
Temporal settlement profiles also 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 also 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.) issue as narrow spikes, while broad distributions generally argue 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 every species, 84% of individuals settled back to the very 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 create 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 high degree of local retention in every species except for the long-dispersing Panulirus spp., and the east coast also had high local retention overall. Between coasts, a high 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) average rearward settlement balance by species per pair of coastlines, calculated by the number of larvae settling at site s from site o divided by 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 every realized paths out of total viable paths, disregarding directionality.
We also calculated edge density, including every 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 high edge density for short and medium dispersers (0.16–0.39) but low for long dispersers (<0.005). The north shore also showed relatively high 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 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 every species (<0.05), a divide that was especially distinct 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 anatomize relationships between these regions. Arranging model output into node-edge networks clarified pathways and regions of note, and revealed several patterns which did not succeed 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 lone SCC containing 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 Have a low out-degree, red nodes Have a medium out-degree, and black nodes Have a high out-degree. Red edges are connections in a strongly connected component, while gray edges are not Part of a strongly connected component (although may silent limn 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 high 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 high BC scores. P. meandrina and C. strigosus showed several regions along the south shore with high 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 every species.
Betweenness centrality is a measure of the number of paths that pass through a certain region; a high score suggests potentially necessary multi-generation connectivity pathways. In-degree and out-degree advert to the amount of a node’s incoming and outgoing connections. Betweenness centrality, in-degree, and out-degree Have every 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 every larvae spawned at that site. Source-sink index is a measure of net export or import; negative values (blue) argue a net larval sink, while positive values (red) argue a net larval source. White indicates that a site is neither a sturdy source nor sink. Gray values for Cellana spp. denote a want 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 also 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, 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 every species. Puko‘o, Pauwalu Harbor, and Cape Halawa were either weak net sources or neither sources nor sinks, which corresponds to the high 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, smash 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 tiny Bay and Lono Harbor, respectively. C. perspicillatus, and S. rubroviolaceus showed a similar pattern in regards to Mokio Point; removal of this node isolated tiny 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 limn rearward proportion, calculated by the number of larvae settling at site s from site o divided by every settled larvae at site s. White squares argue 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 every regions on Moloka‘i, East Kalaupapa Peninsula was the lone largest exporter of larvae to Hawai‘i Island, accounting for 19% of every larvae transported from Moloka‘i to this island; West Kalaupapa Peninsula accounted for another 10%. The park also contributed 22% of 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, acceptation 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 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 necessary role for long-dispersing species; settlement from Kalaupapa made up 18%–29% of 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 high out-degree, or number of outgoing connections to other regions, and West Kalaupapa was also one of the few regions on Moloka‘i that acted as a net larval source across every species (Fig. 8). Their study has also demonstrated that different regions of a marine protected locality can potentially effect different roles, even in a tiny 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.
Effects of biological and physical parameters on connectivity
We incorporated the distribution of suitable habitat, variable reproduction, variable PLD, and ontogenetic changes in swimming faculty and empirical vertical distributions of larvae into their model to increase biological realism, and assess how such traits impact predictions of larval dispersal. The Wong-Ala et al. (2018) IBM provides a highly flexible model framework that can easily be modified to incorporate either additional species-specific data or entirely recent biological traits. In this study, they included specific spawning seasons for every species, as well as spawning by moon aspect 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 embrace 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 Have 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 argue greater connectivity along the northern coast, while the goatfish P. porphyreus shows higher connectivity along the eastern half of the island. These species Have 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 aspect (Fig. 2). While maximum PLD did issue play a role in the probability of rare long-distance dispersal, minimum PLD appears to be the main driver of average dispersal distance (Fig. 2). Overall, species with a shorter minimum PLD had higher settlement success, shorter involve 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 also influenced connectivity patterns. Because mesoscale current patterns can vary substantially over the course of the year, the timing of spawning for certain species may be censorious 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 rather 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). introductory analysis also 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 Have 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 every sites. This simplification may lead us to under- or over-estimate the strength of connections between sites. want of adequate data also 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 also estimated PLD in several cases using congener-level data (see Table 1). While specificity is exemplar 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 impress patterns of connectivity during these years. El Niño can Have a particularly sturdy 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 petite 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 decrease in eastward Pacific dispersal during El Niño years, but an increase in westward dispersal, and Treml et al. (2008) showed unique connections in the West Pacific as well as an increase in connectivity during El Niño. While these effects are difficult to predict, especially at such a tiny scale, additional model years would increase self-possession 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 embrace them in the MITgcm. Storlazzi et al. (2017) showed that tidal forces did impress 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 also 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 be biased by spatial resolution of the ocean model, since complicated coastal processes can be 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 also used the finest-scale current products available for their study area, and their results argue general agreement with similar studies of the region that utilize a coarser resolution (Wren & Kobayashi, 2016) and a finer resolution (Storlazzi et al., 2017). Also, while erudition of island-scale connectivity is necessary 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 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 also necessary to note scale in relation to measures of connectivity; they await that long-dispersing species such as Caranx spp. and Panulirus spp. will argue much higher measures of connectivity when measured across the gross archipelago as opposed to a lone island. The cut-nodes observed in these species may not actually smash 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 tag necessary habitat locations, especially in terms of providing links between two otherwise disconnected coasts. It may be 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 lone 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 exigency for multi-scale management, specifically tailored to local and regional connectivity patterns and the suite of target species. Even on such a tiny 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 also 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 argue 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 every species in their study, these sources fell mostly on the northern and eastern coasts. Maintenance of these areas is especially necessary 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 necessary intergenerational pathways, particularly as a means of connecting north-coast and south-coast populations, which showed a want 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 every connections, even those with a very tiny balance of larvae, and may therefore embrace rare dispersal events that are of petite relevance to managers. Additionally, edge density comparisons between networks should be viewed with the caveat that these networks accomplish not necessarily Have the very number of nodes. Nevertheless, both edge density and balance argue very similar patterns, and embrace 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 also deem 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 Have 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 lone stable network. This practice may be especially necessary for species for which they assess multiple tiny SCCs, such as Cellana spp. or P. meandrina.
Kalaupapa Peninsula emerged as an necessary 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 every species, and also exported larvae to sites along the east and west shores in 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, also on the north shore) that acted as a net larval source across every species. Eastern Kalaupapa Peninsula was particularly highly connected, and was Part of a strongly connected component in every species. The Park also 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 Have 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 tiny scale genetic analyses because there are species, such as the coral P. meandrina, for which the model predicts pellucid 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.
The maintenance of demographically connected populations is necessary for conservation. In this study, they contribute to the growing carcass 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 attend 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 also by other life-history traits such as spawning season, moon-phase spawning, and ontogenetic changes in larval depth. high local retention of larvae with a short- or medium-length PLD is consistent with population genetic studies of the area. They also 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 necessary multigenerational pathways. Connectivity is only one piece of the puzzle of MPA effectiveness, which must also 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 efficient instrument for estimating current and future patterns of dispersal around Kalaupapa Peninsula and around Moloka‘i as a whole.
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, high 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. 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 revise 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 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 argue settlement earlier in the year (February–March), while black tracks argue 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. profound 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, high velocity). While large gyres remain consistent at different depths, smaller features vary along this gradient. For example, the currents around Kaho‘olawe, the tiny gyre off the eastern coast of O‘ahu, and currents to the north of Maui every vary in direction and/or velocity.
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