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

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

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

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IBM IBM Commerce Solutions Order

Sitecore® publicizes international Partnership with IBM iX to permit main internet content management, Commerce, and advertising solutions | killexams.com true Questions and Pass4sure dumps

Sitecore®, the  leader in digital journey management software, these days announced a brand fresh global partnership with IBM iX, one of the world's greatest digital agencies and global company design companions. The partnership will fabricate obtainable to customers Sitecore’s main web content management, commerce, and advertising and marketing solutions via IBM iX designers, know-how consultants, and industry strategists in 40 IBM Studios worldwide. 

The elevated partnership brings together the total breadth of IBM iX’s capabilities to capitalize on the starting to breathe claim for digital marketing capabilities that create particularly-personalized consumer experiences throughout every unique digital touchpoints. 

Matthew sweet, international chief, IBM iX, mentioned that “customer suffer is the principal thing strategic objective of many organizations and core to those businesses’ skill to seriously change. i'm very excited that they are increasing out their existing relationship with Sitecore into a global partnership, as they become a vital player in their ecosystem of partners.” 

As a worldwide Platinum accomplice within the Sitecore solution provider software, IBM iX provides the world-classification consulting, design, construction and implementation functions required to deploy options on the Sitecore platform and bring exceptional effects for customers. Matched to Sitecore’s leading digital adventure administration capabilities, companies can deliver end-customers with seamless, omnichannel experiences to pressure differentiation, promote trade transformation, and enhance profits and customer lifetime value. The IBM iX and Sitecore partnership is additional empowered with most accountable practices and accelerators, as well as the means to leverage the energy of IBM Cloud and IBM Watson expertise. IBM iX additionally brings to undergo the unparalleled edge of Bluewolf, an IBM business, developing experiences with Salesforce, with whom Sitecore has a strategic alliance. 

 


IBM iX and Sitecore® Launch international advertising services and know-how contract | killexams.com true Questions and Pass4sure dumps

SAN FRANCISCO, Jan. 29, 2019 /PRNewswire/ -- Sitecore [®] , the world chief in digital adventure management software, nowadays introduced a fresh global partnership with IBM iX, one of the world's biggest digital businesses and global enterprise design companions. The partnership will fabricate obtainable to customers Sitecore's leading net content material management, commerce, and advertising solutions by the consume of IBM iX designers, know-how consultants, and industry strategists in forty IBM Studios worldwide.

IBM iX is a proven chief in providing Sitecore options, with more than a decade's success of both corporations working together in Europe to ply CMOs' censorious requisite for elevated recrudesce on advertising and marketing funding. The increased partnership brings together the entire breadth of IBM iX's capabilities to capitalize on the growing to breathe claim for digital marketing capabilities that create enormously-personalized consumer experiences throughout every unique digital touchpoints.

Matthew candy, international leader, IBM iX, cited that "consumer journey is the key strategic flush of many businesses and core to these organizations' capacity to seriously change. i am very excited that we're expanding out their latest relationship with Sitecore into a global partnership, as they develop into an principal player in their ecosystem of partners." 

As a global Platinum accomplice in the Sitecore solution provider program, IBM iX offers the world-classification consulting, design, evolution and implementation functions required to deploy options on the Sitecore platform and convey grotesque outcomes for consumers. Matched to Sitecore's leading digital suffer administration capabilities, companies can deliver conclusion-customers with seamless, omnichannel experiences to constrain differentiation, promote enterprise transformation, and extend salary and consumer lifetime price. The IBM iX and Sitecore partnership is extra empowered with top-rated practices and accelerators, as smartly as the means to leverage the vigour of IBM Cloud and IBM Watson expertise. IBM iX furthermore brings to suffer the unparalleled capabilities of Bluewolf, an IBM company, creating experiences with Salesforce, with whom Sitecore has a strategic alliance.

"IBM iX gives the energy, scale, and culture of innovation required to convey immersive, conclusion-to-end digital options for their joint purchasers," mentioned stamp Zablan, Chief revenue Officer for Sitecore. "Our partnership makes a incredible aggregate for corporations who are looking to hurry up the digitization of their trade and foster a consumer-centric approach to digital transformation."

For more information on IBM iX, search recommendation from www.ibm.com/ibmix  and celebrate @IBM_iX on Twitter.

About SitecoreSitecore is the world leader in digital journey management application that combines content material administration, commerce, and consumer insights. The Sitecore suffer Cloud™ empowers entrepreneurs to carry personalized content material in trusty time and at scale across each channel—earlier than, throughout, and after a sale. more than 5,200 brands––including American categorical, Carnival Cruise lines, Dow Chemical, and L'Oréal––have trusted Sitecore to deliver the customized interactions that pride audiences, build loyalty, and constrain earnings.  

ContactMatt KrebsbachSr. Director, Public & Analyst relations at Sitecorematt.krebsbach@sitecore.com

Sitecore Media RelationsWE CommunicationsTeamSitecore@we-worldwide.com

Sitecore®, personal the event®, Sitecore suffer Cloud™, Sitecore xConnect™, Sitecore Cortex™, Sitecore® journey Platform™, Sitecore journey manager™ and Sitecore® adventure Database™ are registered emblems or trademarks of Sitecore supplier A/S within the country and other international locations. every unique other manufacturer names, product names or trademarks belong to their respective holders.

View long-established content to down load multimedia:http://www.prnewswire.com/news-releases/ibm-ix-and-sitecore-launch-world-advertising and marketing-features-and-technology-settlement-300785315.html

source Sitecore

Copyright (C) 2019 PR Newswire. every unique rights reserved


IBM's solutions constrain Bulgaria-based Praktiker's income | killexams.com true Questions and Pass4sure dumps

foreign trade Machines organization IBM currently announced that Praktiker, a home DIY retail chain based in Bulgaria has tripled each its online earnings and in-save purchases considering its adoption of the business’s omnichannel commerce reply five months ago.   

Praktiker’s fresh web page elements an internet catalogue of more than 40,000 items proposing constructive recommendation to the conclusion user. The site has been seen via 750,000 pleasing clients due to the fact its launch. primarily, the common number of visits has tripled from about 1500 per day firstly to 5000 per day at latest.

Adoption of IBM’s omnichannel commerce options namely WebSphere Commerce (for both for B2B and B2C) and Sterling Order administration (for proposing insights on supply and demand, order success procedures) with the aid of retail retailers has increased in recent times.

We anticipate that the growing to breathe adoption of IBM solutions (retail, Watson) will continue to raise the proper line.

View photos

peculiarly, shares of IBM won 0.forty three% on Tuesday. The inventory has outperformed the Zacks laptop - integrated systems trade on a yr-to-date groundwork. whereas the trade gained handiest three.9% privilege through the period, the stock favored 5.1%

Is IBM Poised to improvement?

We celebrate that competition is intensifying in the utility options space with the presence of predominant players comparable to salesforce.com’s CRM Salesforce Commerce Cloud, SAP SE’s SAP SAP Hybris and Oracle’s ORCL Oracle Commerce.

We believe that the continued adoption trend for IBM’s utility options structures augurs well for the trade ultimately.

As of the closing mentioned quarter, IBM’s Cognitive options (options application and transaction processing software) revenues grew 1.4% on a year-over-year groundwork (up 2.2% at consistent forex) to $5.30 billion.

foreign trade Machines corporation profits (TTM)

View photos

international trade Machines trade enterprise profits (TTM) | overseas trade Machines enterprise Quote

options utility extend was pushed primarily by means of analytics. (study more: IBM Corp (IBM) Beats on this plunge salary; FY17 View nice).

Zacks Rank

At present IBM has a Zacks Rank #three (hold). that you could behold the comprehensive listing of today’s Zacks #1 Rank (amazing buy) stocks here.

greater stock information: 8 companies Verge on Apple-Like Run

Did you pass over Apple's 9X inventory explosion after they launched their iPhone in 2007? Now 2017 appears to breathe a pivotal yr to glean in on a further rising technology expected to rock the market. claim may jump from just about nothing to $forty two billion by using 2025. reviews indicate it might deliver 10 million lives per decade which may in eddy store $200 billion in U.S. healthcare costs.

Story continues


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IBM Commerce Solutions Order Mgmt Technical Mastery Test v1

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Desktop Management Problem | killexams.com true questions and Pass4sure dumps

Desktop Management Problem

The example 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 conclude user intervention or a technician to visit the desktop. Deployment tasks can breathe executed immediately or scheduled for off-hours in order to minimize impact on conclude user productivity or network bandwidth.

The example desktop management should breathe an open and scalable system that supports a purview of server platforms, such as Solaris, HP-UX, NT, and both fresh and legacy Microsoft client platforms (DOS, Windows 3.x, Windows 95, Windows 98 and NT 4.0). The system should breathe standards-based, with support for standard protocols, including IP, DHCP and BOOTP and standard Wired for Management (WfM)-enabled PC platforms (DMI 2.0, Remote Wake Up and PXE). The desktop management system should furthermore support legacy PCs via boot PROMs or boot floppies for standard NICs from Intel, 3Com, SMC and others.

Essential to the equation should furthermore breathe a progression of open, programmable interfaces that allow customers and partners to extend and customize the system. The system should breathe carefully designed to provide scalability across great numbers of clients and servers, including the capacity to group PCs and software packages into deployment groups and the capacity to intelligently manage network bandwidth.

Windows 2000 promises to address many of these limitations but will not breathe deployed in most production environments until 2001, according to industry analysts, such as the GartnerGroup; moreover, in order to win edge of these fresh desktop capabilities, organizations must migrate to an exclusive, all-Windows 2000 environment on both clients and servers, which may breathe unrealistic for many corporations, the preponderance of non-NT desktops.

The example desktop management system should configure operating systems, applications and desktop parameters on an ongoing basis. These operations should breathe executed simultaneously on multiple PCs from central administrative consoles, and should deliver three censorious capabilities: pre-OS installation, remote support and no conclude user intervention. These three powerful capabilities result in enterprise desktop management nirvana: lower PC total cost of ownership (TCO).

As computing environments hump 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 constrain behind these implementations is the realization that centralized management systems are required to cost-effectively manage the complex and mission-critical nature of networked systems. For most IT organizations, centralized management systems are the only way of approaching the selfsame flush of reliability, availability and control as has been available with mainframe environments of the past.

Centralized Tools

Centralized desktop management tools are seen as a key requirement for reducing the TCO associated with desktop support and the rapid growth of desktops in enterprise environments, and as a key enabler for delivering a higher quality of IT service to end-user organizations.

In addition, most IT organizations now behold PC desktops as a mission-critical corporate resource that should breathe 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 breathe managed on an individual basis.

Tactical requirements for desktop management typically arise in connection with imperative 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 fresh and more powerful PC hardware to support trade unit requirements (Web access, e-commerce, multi-media, etc.), or deployment of fresh and complex applications, such as Lotus Notes or Netscape Communicator.

Technology Differentiators

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 capacity to install and configure operating systems on PCs that are fresh 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 situation (e.g., corrupted arduous disk, won’t boot, virgin arduous drive, etc.). If a desktop management system cannot perform these functions, then its value is tremendously reduced, as the (re)installation represents a major task of IT support 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 fresh PCs out of the box. It furthermore acts in a befriend 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 example 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 breathe 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 lofty flush 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 example desktop management system should manage PC configurations across the entire PC lifecycle, not just during the initial application installation. It should breathe able to deploy action packages to add a fresh printer or change printer settings, change the IP address or login password of a PC, evade an anti-virus or inventory scan, or execute a BIOS gleam as Part of a Y2K remediation effort.

It is furthermore helpful for a desktop management system to maintain a unique client configuration database that stores a history of every unique software packages that breathe pleased been installed, as well as the configuration parameters that were used during installation. This database can breathe 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 breathe pleased shipped since the conclude of 1998.

WfM V2 will tender enhanced manageability for mobile PCs, enhanced security via encryption and authentication, and support for fresh 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 breathe 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 example desktop management solution should fully support 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 constrain (DMTF), DMI 2.0 allows befriend Desk personnel to scan the hardware and software properties of remote PCs in real-time to aid in troubleshooting.


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

Introduction

Knowledge of population connectivity is necessary for efficacious 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 breathe pleased a bi-phasic life history in which adults display limited geographic purview and lofty site fidelity, while larvae are pelagic and highly mobile (Thorson, 1950; Scheltema, 1971; Strathmann, 1993; Marshall et al., 2012). This life history strategy is not only common to sessile invertebrates such as corals or limpets; many reef fish species breathe pleased been shown to breathe pleased 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 breathe used to inform efficacious management, such as marine spatial management strategies that sustain source populations of breeding individuals capable of dispersing offspring to other areas.

Both biological and physical factors impact larval dispersal, although the relative importance of these factors is likely variable among species and sites and remains debated (Levin, 2006; Paris, Chérubin & Cowen, 2007; Cowen & Sponaugle, 2009; White et al., 2010). In situ data on pelagic larvae are sparse; marine organisms at this life stage are difficult to capture and identify, and are typically establish in low densities across great areas of the open ocean (Clarke, 1991; Wren & Kobayashi, 2016). A variety of genetic and chemistry techniques breathe pleased therefore been developed to assay larval connectivity (Gillanders, 2005; Leis, Siebeck & Dixson, 2011; Toonen et al., 2011; Johnson et al., 2018). Computer models informed by bailiwick and laboratory data breathe pleased furthermore become a valuable implement 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 breathe successfully transported by ocean currents, and so is expected to directly affect 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 unique play a role in shaping connectivity patterns. Physical factors such as temperature, bathymetry, and current direction can furthermore 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 evade 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 complex 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 submit techniques, which breathe pleased proven ineffective (Goodyear, 1996; Hilborn, 2011). There is a general movement toward the consume 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 region (MPA) located on the north shore of Moloka‘i, an island in the Maui Nui complex 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 breathe pleased been identified within this area (Kenyon, Maragos & Fenner, 2011), and in 2010 the Park showed the greatest fish biomass and species diversity out of four Hawaiian National Parks surveyed (Beets, Brown & Friedlander, 2010). One of the major benefits expected of MPAs is that the protected waters within the region provide a source of larval spillover to other sites on the island, seeding these areas for commercial, recreational, and subsistence fishing (McClanahan & Mangi, 2000; Halpern & Warner, 2003; Lester et al., 2009).

In this study, they used a Lagrangian particle-tracking IBM (Wong-Ala et al., 2018) to simulate larval dispersal around Moloka‘i and to assay the larval exchange among sites at the scale of an individual island. They breathe pleased 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.

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 woebegone gray. Spawn season and timing from traditional ecological erudition 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 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 region that includes the islands of Moloka‘i, Maui, Lana‘i, Kaho‘olawe, O‘ahu, and Hawai‘i. While Ni‘ihau and southern Kaua’i furthermore plunge within the domain, they discarded connectivity to these islands because they equivocate within the 0.5° circumscribe zone of the current model. circumscribe conditions are enforced over 20 grid points on every unique sides of the model domain. Vertically, the model is divided into 50 layers that extend in thickness with depth, from five m at the surface (0.0–5.0 m) to 510 m at the base (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 enact not include tides in the model due to temporal resolution. Their model era represents a neutral ocean state; no El Niño or La Niña events occurred during this time period. To ground-truth the circulation model, they compared surface current output to real-time trajectories of surface drifters from the GDP Drifter Data Assembly hub (Fig. S2) (Elipot et al., 2016), as well as other current models of the region (Wren et al., 2016; Storlazzi et al., 2017).

Biological model

To simulate larval dispersal, they used a modified version of the Wong-Ala et al. (2018) IBM, a 3D Lagrangian particle-tracking model written in the R programming language (R Core Team, 2017). The model takes the aforementioned MITgcm current products as input, as well as shoreline shapefiles extracted from the plenary 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 part 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). upright velocities (w) were not implemented by the model; details of upright larval movement are described below. Advection was interpolated between data points at each timestep using an Eulerian 2D barycentric interpolation method. They chose this implementation over a more computationally intensive interpolation system (i.e., fourth-order Runge–Kutta) because they did not celebrate a contrast at this timestep length. Biological processes modeled include PLD, reproduction timing and location, mortality, and ontogenetic changes in upright 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 conclude 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 depict 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 era 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 unique larvae were assumed to breathe pleased survived at one-half of the maximum PLD for that species (following Holstein, Paris & Mumby, 2014). Since their focus was on potential connectivity pathways, reproductive rates were calibrated to allow for saturation of possible settlement sites, equating from ∼900,000 to ∼1,7000,000 larvae released depending on species. Fecundity was therefore derived not from biological data, but from computational minimums.

Development, and resulting ontogenetic changes in behavior, is specific to the life history of each species. Broadcast-spawning species with weakly-swimming larvae (P. meandrina and Cellana spp., Table 1) were transported as passive particles randomly distributed between 0–5 m depth (Storlazzi, Brown & Field, 2006). Previous studies breathe pleased demonstrated that fish larvae breathe pleased a lofty degree of control over their upright 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 phase to simulate eggs pre-hatch, followed by a pelagic larval phase 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 upright distributions data report (Boehlert & Mundy, 1996). P. sexfilis and C. strigosus larvae were randomly distributed between 50–100 m (Boehlert, Watson & Sun, 1992). Benthic brooding species (O. cyanea and Panulirus spp.) enact not breathe pleased 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 assay of larval duration for Caranx species, but in Hawai‘i peak spawning occurs in May–July and peak recruitment in August–December (Sudekum, 1984; Longenecker, Langston & Barrett, 2008). In consultation with resource managers and community members, a PLD of 140 days was chosen pending future data that indicates a more accurate pelagic period.

Habitat selection

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

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

Dispersal distance was measured via the distm office in the R package ‘geosphere’, which calculates distance between geographical points via the Haversine formula (Hijmans, 2016). This distance, measured between spawn and settlement locations, was used to design dispersal kernels to examine and compare species-specific distributions. They furthermore 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 assay the role of specific sites around Moloka‘i, they furthermore 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 unique 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 depict exogenous sinks. For this reason, settlement to other islands was not included in source–sink index calculations.

We furthermore calculated settlement symmetry between different regions for each species (Calabrese & Fagan, 2004). They calculated the forward settlement proportion, i.e., the symmetry 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 every unique successful settlers originating from site o. Forward symmetry can breathe represented as Pso = Sos∕∑So. They furthermore calculated rearward settlement proportion, or the symmetry 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 every unique settlers observed at site s. The rearward symmetry can breathe 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 breathe 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 furthermore 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 furthermore provides a powerful implement for spatial visualization, allowing for rapid, intuitive communication of connectivity results to researchers, managers, and the public alike. This nature of analysis can breathe 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 importance of each site and dispersal pathway to the greater pattern of connectivity around Moloka‘i, as well as differences in connectivity patterns between species (Treml et al., 2008; Holstein, Paris & Mumby, 2014). They used the R package ‘igraph’ to examine several measures of within-island connectivity (Csardi & Nepusz, 2006). Edge density, or the symmetry of realized edges out of every unique possible edges, is a multi-site measure of connectivity. Areas with a higher edge density breathe pleased more direct connections between habitat sites, and thus are more strongly connected. They measured edge density along and between the north, south, east, and west coasts of Moloka‘i to examine possible population structure and degree of exchange among the marine resources of local communities.

The distribution of shortest path length is furthermore 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 partake 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 fabricate a connection. uninterested shortest path, therefore, is a descriptive statistic to assay connectivity of a network. If two sites are unconnected, it is possible to breathe pleased infinite-length shortest paths; here, these eternal values were noted but not included in final analyses.

Networks can furthermore breathe broken in connected components (Csardi & Nepusz, 2006). A weakly connected component (WCC) is a subgraph in which every unique nodes are not reachable by other nodes. A network split into multiple WCCs indicates part populations that enact not exchange any individuals, and a great number of WCCs indicates a low degree of island-wide connectivity. A strongly connected component (SCC) is a subgraph in which every unique nodes are directly connected and indicates a lofty degree of connectivity. A region with many tiny SCCs can indicate lofty local connectivity but low island-wide connectivity. Furthermore, component analysis can identify lop nodes, or nodes that, if removed, atomize a network into multiple WCCs. Pinpointing these lop nodes can identify potential principal 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 principal to note which sites are exporting larvae to, or importing larvae from, other sites. To this end, they examined in-degree and out-degree for each region. In-degree refers to the number of inward-directed edges to a specific node, or how many other sites provide larvae into site ‘A’. Out-degree refers to the number of outward-directed edges from a specific node, or how many sites receive larvae from site ‘A’. Habitat sites with a lofty out-degree seed a great number of other sites, and indicate potentially principal larval sources, while habitat sites with a low in-degree rely on a limited number of larval sources and may therefore breathe relative on connections with these few other sites to maintain population size. Finally, betweenness centrality (BC) refers to the number of shortest paths that pass through a given node, and may therefore indicate connectivity pathways or ‘chokepoints’ that are principal to overall connectivity on a multigenerational timescale. BC was weighted with the symmetry of dispersal as described in the preceding section. They calculated in-degree, out-degree, and weighted betweenness centrality for each region in the network for each species.

As with the source–sink index, they did not include sites on islands other than Moloka‘i in their calculations of edge density, shortest paths, connected components, lop 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 symmetry 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 phase 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 furthermore examined uninterested 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 symmetry of larvae settled out of the total number of larvae spawned. Local retention is measured as the symmetry of larvae spawned from a site that settle at the selfsame site. Shortest path is measured as the minimum number of steps needed to connect two sites. Strongly connected sites refers to the symmetry of sites in a network that belong to a strongly connected component. add up to dispersal distance is measured in kilometers from spawn site to settlement site.

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

The number and size of weakly-connected and strongly-connected components in a network is furthermore 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 unique sites in the network falling into a unique SCC. Cellana spp. and P. meandrina each had approximately 60% of sites included in a SCC, but both parade fragmentation with seven and six SCCs respectively, ranging in size from two to 22 sites. This SCC pattern suggests low global connectivity but lofty local connectivity for these species. Medium and long dispersers showed larger connected components; 70% of parrotfish sites fell within two SCCs; 40% of P. porphyreus sites fell within two SCCs; 70% of C. strigosus sites, 55% of C. melampygus sites, and 40% of Panulirus sites fell within a unique SCC. In contrast, only 26% of C. ignoblis sites fell within a unique SCC. It is furthermore principal 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 parade 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 immediate to home, while few long dispersers are retained at or near their spawning sites.

Minimum PLD was positively correlated with add up to 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 add up to distance of 24.06 ± 31.33 km, medium dispersers travelled a add up to distance of 52.71 ± 40.37 km, and long dispersers travelled the farthest, at a add up to of 89.41 ± 41.43 km. However, regardless of PLD, there were essentially two peaks of add up to 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 add up to at 8.85 km, indicating that most of these larvae settled relatively immediate 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 symmetry 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 uninterested of 74% of larvae from short-dispersing species settled back to Moloka‘i, as compared to an uninterested of 41% of medium dispersers and 9% of long dispersers. A great symmetry of larvae furthermore 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 a little lower. Larvae of every species settled to Lana‘i, and settlement to this island made up less than 5% of settled larvae across every unique 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 unique 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 furthermore examined coast-specific patterns of rearward settlement symmetry to other islands, discarding connections with a very low symmetry 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 unique larvae settling to O‘ahu from Moloka‘i. The east and south shores of Moloka‘i had the highest uninterested 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 uninterested 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 indicate that multiple coasts of Moloka‘i breathe pleased the potential to export larvae to neighboring islands.

Temporal settlement profiles furthermore 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 era of time. Some species furthermore 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.) parade as narrow spikes, while broad distributions generally indicate 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 unique species, 84% of individuals settled back to the selfsame coast on which they were spawned rather than a different coast on Moloka‘i. Excluding connections with a very low symmetry of larvae (<0.1% of total larvae of that species that settled to Moloka‘i), they establish that the symmetry of coast-scale local retention was generally higher than dispersal to another coast, with the exception of the west coast (Fig. 6A). The north and south coasts had a lofty degree of local retention in every species except for the long-dispersing Panulirus spp., and the east coast furthermore had lofty local retention overall. Between coasts, a lofty symmetry 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) uninterested rearward settlement symmetry by species per pair of coastlines, calculated by the number of larvae settling at site s from site o divided by every unique settled larvae at site s. Directional coastline pairs (Spawn > Settlement) are ordered from left to privilege by increasing median settlement proportion. (B) Heatmap of edge density for coast-specific networks by species. Density is calculated by the number of every unique realized paths out of total possible paths, disregarding directionality.

We furthermore calculated edge density, including every unique connections between coasts on Moloka‘i regardless of settlement symmetry (Fig. 6B). The eastern coast was particularly well-connected, with an edge density between 0.14 and 0.44, depending on the species. The southern shore showed lofty edge density for short and medium dispersers (0.16–0.39) but low for long dispersers (<0.005). The north shore furthermore showed relatively lofty edge density (0.20 on average), although these values were smaller for long dispersers. The west coast showed very low edge density, with the exceptions of O. cyanea (0.37) and P. sexfilis (0.13). Virtually every unique 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 unique species (<0.05), a divide that was especially part 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 dissect 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 unique SCC containing every unique 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 breathe pleased a low out-degree, red nodes breathe pleased a medium out-degree, and black nodes breathe pleased a lofty out-degree. Red edges are connections in a strongly connected component, while gray edges are not Part of a strongly connected component (although may still depict substantial connections). Edge thickness represents log-transformed symmetry of dispersal along that edge.

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

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

Several species-wide hotspots of local retention emerged, particularly East Kalaupapa Peninsula/Leinaopapio Point, the northeast point of Moloka‘i, and the middle of the south shore. Some species furthermore 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 unique 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 unique species. Puko‘o, Pauwalu Harbor, and Cape Halawa were either decrepit net sources or neither sources nor sinks, which corresponds to the lofty levels of local retention observed at these sites. Pala‘au and Mo‘omomi acted as either decrepit sinks or sources for short dispersers and as sources for long dispersers.

Only four networks showed regional cut-nodes, or nodes that, if removed, atomize 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 depict rearward proportion, calculated by the number of larvae settling at site s from site o divided by every unique settled larvae at site s. White squares indicate 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 unique regions on Moloka‘i, East Kalaupapa Peninsula was the unique largest exporter of larvae to Hawai‘i Island, accounting for 19% of every unique larvae transported from Moloka‘i to this island; West Kalaupapa Peninsula accounted for another 10%. The park furthermore contributed 22% of every unique larvae exported from Moloka‘i to O‘ahu, and successfully exported a smaller percentage of larvae to Maui, Lana‘i, and Kaho‘olawe (Fig. 9). Kalaupapa was not marked as a cut-node for any species, signification that plenary 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 unique 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 principal role for long-dispersing species; settlement from Kalaupapa made up 18%–29% of every unique successful settlement in Caranx spp. and Panulirus spp., despite making up only 12% of spawning sites included in the model. In C. strigosus, S. rubroviolaceus, and C. strigosus, Kalaupapa showed a particularly lofty out-degree, or number of outgoing connections to other regions, and West Kalaupapa was furthermore one of the few regions on Moloka‘i that acted as a net larval source across every unique species (Fig. 8). Their study has furthermore demonstrated that different regions of a marine protected region can potentially perform 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 symmetry of dispersal along that edge; red indicates higher symmetry 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 upright distributions of larvae into their model to extend biological realism, and assess how such traits impact predictions of larval dispersal. The Wong-Ala et al. (2018) IBM provides a highly resilient model framework that can easily breathe modified to incorporate either additional species-specific data or entirely fresh biological traits. In this study, they included specific spawning seasons for every unique species, as well as spawning by moon phase 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 behavior become available, such information can breathe easily added for these species and others. Some potential additions to future iterations of the model might include density of reproductive-age adults within each habitat patch, temperature-dependent pelagic larval duration (Houde, 1989), ontogenetic-dependent behavioral changes such as orientation and diel upright 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 breathe pleased 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 parade greater connectivity along the northern coast, while the goatfish P. porphyreus shows higher connectivity along the eastern half of the island. These species breathe pleased 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 phase (Fig. 2). While maximum PLD did parade play a role in the probability of rare long-distance dispersal, minimum PLD appears to breathe the main driver of uninterested dispersal distance (Fig. 2). Overall, species with a shorter minimum PLD had higher settlement success, shorter add up to 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 furthermore influenced connectivity patterns. Because mesoscale current patterns can vary substantially over the course of the year, the timing of spawning for unavoidable species may breathe 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 great symmetry of larvae settled to O‘ahu, which is a little surprising given that in order to settle from Moloka‘i to O‘ahu, larvae must cross the Kaiwi Channel (approx. 40 km). However, the intermittent presence of mesoscale gyres may act as a stabilizing pathway across the channel, sweeping larvae up either the windward or leeward coast of O‘ahu depending on spawning site. Likewise, in their model long-distance dispersal to Hawai‘i Island was possible at unavoidable 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 furthermore 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 breathe pleased 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 unique sites. This simplification may lead us to under- or over-estimate the energy of connections between sites. requisite of adequate data furthermore 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 flush without genetic analysis, they used genus-level larval distribution data (Boehlert & Mundy, 1996), or lacking that, an assay of 50–100 m as a depth layer that is generally more enriched with larvae (Boehlert, Watson & Sun, 1992; Wren & Kobayashi, 2016). They furthermore estimated PLD in several cases using congener-level data (see Table 1). While specificity is example for making informed management decisions about a unavoidable 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 situation ‘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 affect patterns of connectivity during these years. El Niño can breathe pleased a particularly strong impact on coral reproduction, since the warm currents associated with these events can lead to severe temperature stress (Glynn & D’Croz, 1990; Wood et al., 2016). While there has been petite study to date on the effects of ENSO on fine-scale connectivity, previous travail has demonstrated increased variability during these events. For example, Wood et al. (2016) showed a dwindle in eastward Pacific dispersal during El Niño years, but an extend in westward dispersal, and Treml et al. (2008) showed unique connections in the West Pacific as well as an extend in connectivity during El Niño. While these effects are difficult to predict, especially at such a tiny scale, additional model years would extend 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 include them in the MITgcm. Storlazzi et al. (2017) showed that tidal forces did affect larval dispersal in Maui Nui, underlining the importance of including both fine-scale, short-duration models and coarser-scale, long-duration models in final management decisions.

We furthermore circumscribe their model’s scope geographically. Their goal was to determine whether they could resolve predictive patterns at this scale material to management. Interpretation of connectivity output can breathe biased by spatial resolution of the ocean model, since complex coastal processes can breathe smoothed and therefore impact larval trajectories. To circumscribe this bias, they focused mainly on coastal and regional connectivity on scales greater than the current resolution. They furthermore used the finest-scale current products available for their study area, and their results parade general 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 erudition of island-scale connectivity is principal 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 color their analysis. Likewise, they cannot forecast the symmetry of larvae settling to other islands that originated from Moloka‘i, or the symmetry of larvae on Moloka‘i that originated from other islands.

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

Implications for management

Clearly, there is no unique management approach that encompasses the breadth of life history and behavior differences that impact patterns of larval dispersal and connectivity (Toonen et al., 2011; Holstein, Paris & Mumby, 2014). The spatial, temporal, and species-specific variability suggested by their model stresses the requisite for multi-scale management, specifically tailored to local and regional connectivity patterns and the suite of target species. Even on such a 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 breathe more at risk of isolation. Seasonal variation should furthermore breathe 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 parade similar network dynamics may capitalize from similar management strategies. Areas that act as larval sources either by symmetry of larvae (high source–sink index) or number of sites (high out-degree) should receive management consideration. On Moloka‘i, across every unique species in their study, these sources fell mostly on the northern and eastern coasts. Maintenance of these areas is especially principal 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 principal intergenerational pathways, particularly as a means of connecting north-coast and south-coast populations, which showed a requisite of connectivity both in total number of connections (edge density) and symmetry of larvae. Both of these connectivity measures were included because edge density includes every unique connections, even those with a very tiny symmetry of larvae, and may therefore include rare dispersal events that are of petite relevance to managers. Additionally, edge density comparisons between networks should breathe viewed with the caveat that these networks enact not necessarily breathe pleased the selfsame number of nodes. Nevertheless, both edge density and symmetry parade very similar patterns, and include both demographically-relevant common connections as well as rare connections that could influence genetic connectivity.

Management that seeks to establish a resilient network of spatially managed areas should furthermore consider 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 breathe pleased more direct connections and therefore may breathe more resilient to local population loss. supervision should breathe 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 unique stable network. This drill may breathe especially principal for species for which they assay multiple tiny SCCs, such as Cellana spp. or P. meandrina.

Kalaupapa Peninsula emerged as an principal 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 unique species, and furthermore exported larvae to sites along the east and west shores in every unique 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, furthermore on the north shore) that acted as a net larval source across every unique species. Eastern Kalaupapa Peninsula was particularly highly connected, and was Part of a strongly connected component in every species. The Park furthermore 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 breathe 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 flush of shoreline-specific population structure for most species included in the study (Fig. 6). Unfortunately, genetic analyses to date breathe pleased 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 justify such tiny 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 breathe 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 principal for conservation. In this study, they contribute to the growing cadaver of travail 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 befriend 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 furthermore by other life-history traits such as spawning season, moon-phase spawning, and ontogenetic changes in larval depth. lofty local retention of larvae with a short- or medium-length PLD is consistent with population genetic studies of the area. They furthermore 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 principal multigenerational pathways. Connectivity is only one piece of the perplex of MPA effectiveness, which must furthermore 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 efficacious implement for estimating current and future patterns of dispersal around Kalaupapa Peninsula and around Moloka‘i as a whole.

Supplemental Information Current patterns in the model domain.

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

Subset of validation drifter paths.

Drifter paths in black and corresponding model paths are colored by drifter ID. every unique 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 rectify day where they entered the model domain, at the uppermost depth layer of their oceanographic model (0–5 m).

Selected larval depth distributions.

Modeled upright larval distributions for Caranx spp. (left), S. rubroviolaceus and C. perspicillatus (middle), and P. porphyreus (right), using data from the 1996 NOAA ichthyoplankton upright 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 unique 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 indicate settlement earlier in the year (February–March), while black tracks indicate 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 symmetry are transported through Maui Nui.

Eddy differences by depth layer.

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


Avoid Bothersome Garbage Collection Pauses | killexams.com true questions and Pass4sure dumps

Many engineers complain that the non-deterministic behavior of the garbage collector prevents them from utilizing the Java environment for mission-critical applications, especially distributed message-driven displays (GUIs) where user responsiveness is critical. They accord that garbage collection does occur at the worst times: for example, when a user clicks a mouse or a fresh message enters the system requiring immediate processing. These events must breathe handled without the delay of in-progress garbage collection. How enact they obviate these garbage collection pauses that meddle with the responsiveness of an application ("bothersome pauses")?

We breathe pleased discovered a very efficacious technique to obviate bothersome garbage collection pauses and build responsive Java applications. This technique or pattern is especially efficacious for a distributive message-driven display system with soft real-time constraints. This article details this pattern in three simple steps and provides evidence of the effectiveness of the technique.

Pattern to Control Garbage Collection PausesThe Java environment provides so many benefits to the software community - platform independence, industry momentum, a plethora of resources (online tutorials, code, interest groups, etc.), object-oriented utilities and interfaces (collections, network I/O, undulate display, etc.) that can breathe plugged in and out - that once you breathe pleased experienced working with Java it's arduous to depart back to traditional languages. Unfortunately, in some mission-critical applications, dote message-driven GUIs that must breathe very responsive to user events, the requirements constrain you to win that step backward. There's no play for multiple second garbage collection pauses. (The garbage collector collects every unique the "unreachable" references in an application so the space consumed by them can breathe reused. It's a low-priority thread that usually only takes priority over other threads when the VM is running out of memory.) enact they really breathe pleased to lose every unique the benefits of Java? First, let's consider the requirements.

A system engineer should consider imposing requirements for garbage collection dote the following list taken from a telecom industry example (see References).1.  GC sequential overhead on a system may not breathe more than 10% to ensure scalability and optimal consume of system resources for maximum throughput.2.  Any unique GC pause during the entire application evade may breathe no more than 200ms to meet the latency requirements as set by the protocol between the client and the server, and to ensure honorable response times by the server.

Armed with these requirements, the system engineer has defined the worst-case behavior in a manner that can breathe tested.

The next question is: How enact they meet these requirements? Alka Gupta and Michael Doyle fabricate excellent suggestions in their article (see References). Their approach is to tune the parameters on the Java Virtual Machine (JVM). They win a slightly different approach that leaves the consume of parameter definitions as defined by the JVM to breathe used as a final tuning technique.

Why not disclose the garbage collector what and when to collect?

In other words, control garbage collection via the software architecture. fabricate the job of the garbage collector easy! This technique can breathe described as a multiple step pattern. The first step of the pattern is described below as "Nullify Objects." The second step involves forcing garbage collection to occur as delineated in "Forcing Garbage Collection." The final step involves either placing persistent data out of the achieve of the collector or into a data pool so that an application will continue to perform well in the long run.

Step 1: Nullify ObjectsMemory leaks strike panic into the hearts of programmers! Not only enact they degrade performance, they eventually terminate the application. Yet reminiscence leaks prove very subtle and difficult to debug. The JVM performs garbage collection in the background, freeing the coder from such details, but traps still exist. The biggest danger is placing an demur into a collection and forgetting to remove it. The reminiscence used by that demur will never breathe reclaimed.

A programmer can obviate this nature of reminiscence leak by setting the demur reference and every unique underlying demur references ("deep" objects) to null when the demur is no longer needed. Setting an demur reference to "null" tells the garbage collector that at least this one reference to the demur is no longer needed. Once every unique references to an demur are cleared, the garbage collector is free to reclaim that space. Giving the collector such "hints" makes its job easier and faster. Moreover, a smaller reminiscence footprint furthermore makes an application evade faster.

Knowing when to set an demur reference to null requires a complete understanding of the problem space. For instance, if the remote receiver allocates the reminiscence space for a message, the leisure of the application must know when to release the space back for reuse. Study the domain. Once an demur or "subobject" is no longer needed, disclose the garbage collector.

Thus, the first step of the pattern is to set objects to null once you're confident they're no longer needed. They muster this step "nullify" and include it in the definition of the classes of frequently used objects.

The following code snippet shows a system that "nullifies" a track object. The class members that consist of primitives only (contain no additional class objects) are set to null directly, as in lines 3-5. The class members that accommodate class objects provide their own nullify system as in line 9.

1 public void nullify () {23 this.threatId = null ;4 this.elPosition = null ;5 this.kinematics = null ;67 if (this.iff != null)8 {9 this.iff.nullify();10 this.iff = null ;11 }12 }

The track nullify is called from the thread that has completed processing the message. In other words, once the message has been stored or processed, that thread tells the JVM it no longer needs that object. Also, if the demur was placed in some Collection (like an ArrayList), it's removed from the Collection and set to null.

By setting objects to null in this manner, the garbage collector and thus the JVM can evade more efficiently. Train yourself to program with "nullify" methods and their invocation in mind.

Step 2: "Force" Garbage CollectionThe second step of the pattern is to control when garbage collection occurs. The garbage collector, GC, runs as Java priority 1 (the lowest priority). The virtual machine, VM, runs at Java priority 10 (the highest priority). Most books recommend against the usage of Java priority 1 and 10 for assigning priorities to Java applications. In most cases, the GC runs during idle times, generally when the VM is waiting for user input or when the VM has evade out of memory. In the latter case, the GC interrupts high-priority processing in the application.

Some programmers dote to consume the "-Xincgc" directive on the Java command line. This tells the JVM to perform garbage collection in increments when it desires. Again, the timing of the garbage collection may breathe inopportune. Instead, they intimate that the garbage collector perform a plenary garbage collection as soon as it can in either or both of two ways:1.  Request garbage collection to befall as soon as possible: This system proves useful when the programmer knows he or she has a "break" to garbage collect. For example, after a great image is loaded into reminiscence and scaled, the reminiscence footprint is large. Forcing a garbage collection to occur at that point is wise. Another honorable region may breathe after a great message has been processed in the application and is no longer needed.2.  Schedule garbage collection to occur at a fixed rate: This system is optimal when the programmer does not breathe pleased a specific instant when he knows his application can stop shortly and garbage collect. Normally, most applications are written in this manner.

Listing 1 introduces a class named "BetterControlOfGC". It's a utility class that provides the methods described earlier. There are two public methods: "suggestGCNow()" and "scheduleRegularGC(milliseconds)" that respectively correspond to the steps described earlier. Line 7 suggests to the VM to garbage collect the unreachable objects as soon as possible. The documentation makes it transparent that the garbage collection may not occur instantaneously, but suffer has shown that it will breathe performed as soon as the VM is able to accomplish the task. Invoking the system on line 25 causes garbage collection to occur at a fixed rate as determined by the parameter to the method.

In scheduling the GC to occur at a fixed rate, a garbage collection stimulator task, GCStimulatorTask, is utilized. The code extends the "java.util.timer" thread in line 10. No fresh thread is created; the processing runs on the unique timer thread available genesis with the Java 1.3 environment. Similarly, to preserve the processing lean, the GC stimulator follows the Singleton pattern as shown by lines 18-23 and line 27. There can breathe only one stimulator per application, where an application is any code running on an instance of the JVM.

We intimate that you set the interval at which the garbage collector runs from a Java property file. Thus you can tune the application without having to recompile the code. Write some simple code to read a property file that's either a parameter on the command line or a resource bundle in the class path. station the command parameter "-verbose:gc" on your executable command line and measure the time it takes to garbage collect. Tune this number until you achieve the results you want. If the budget allows, experiment with other virtual machines and/or hardware.

Step 3: Store Persistent Objects into Persistent Data Areas or Store Long-Lived Objects in PoolsUsing persistent data areas is purely optional. It supports the underlying premise of this article. In order to bind the disruption of the garbage collector in your application, fabricate its job easy. If you know that an demur or collection of objects would live for the duration of your application, let the collector know. It would breathe nice if the Java environment provided some sort of flag that could breathe placed on objects upon their creation to disclose the garbage collector "-keep out". However, there is currently no such means. (The Real-Time Specification for Java describes an region of reminiscence called "Immortal Memory" where objects live for the duration of the application and garbage collection should not run.) You may try using a database; however, this may tedious down your application even more. Another solution currently under the Java Community Process is JSR 107. JCache provides a standard set of APIs and semantics that allow a programmer to cache frequently used data objects for the local JVM or across JVMs. This API is still under review and may not breathe available yet. However, they believe it holds much engage for the Java developer community. preserve this avenue open and in intellect for future architectures. What can they enact now?

The pooling of objects is not fresh to real-time programmers. The concept is to create every unique your expected data objects before you launch processing, then every unique your data can breathe placed into structures without the expense of instance creation during processing time. This has the edge of keeping your reminiscence footprint stable. It has the detriment of requiring a "deep copy" system to breathe written to store the data into the pool. (If you simply set an demur to another, you're changing the demur reference and not reusing the selfsame space.) The nanosecond expense of the abysmal copy is far less than that of the demur instance creation.

If the data pooling technique is combined with the proper consume of the "nullify" technique, garbage collection becomes optimized. The reasons are fairly straightforward:1.  Since the demur is set to null immediately after the abysmal copy, it lives only in the youthful generation portion of the memory. It does not progress into the older generations of reminiscence and thus takes less of the garbage collector's cycle time.2.  Since the demur is nullified immediately and no other reference to it exists in some other collection demur in the application, the job of the garbage collector is easier. In other words, the garbage collector does not breathe pleased to preserve track of an demur that exists in a collection.

When using data pools, it's sensible to consume the parameters "-XX:+UseConcMarkSweepGC -XX:MaxTenuringThreshold=0 -XX:SurvivorRatio=128" on the command line. These disclose the JVM to hump objects on the first sweep from the fresh generation to the old. It commands the JVM to consume the concurrent stamp sweep algorithm on the veteran generation that proves more efficient since it works "concurrently" for a multi-processor platform. For unique processor machines, try the "-Xincgc" option. We've seen those long garbage collector pauses, which occur after hours of execution, vanish using this technique and these parameters. Performing well in the long evade is the trusty capitalize of this terminal step.

Performance ResultsTypically, most engineers want proof before changing their approach to designing and coding. Why not? Since we're now suggesting that even Java programmers should breathe concerned about resource allocation, it better breathe worth it! Once upon a time, assembly language and C programmers spent time tweaking reminiscence and register usage to improve performance. This step was necessary. Now, as higher-level object-oriented programmers they may disdain this thought. This pattern has dared to imply that such considerations, although not as low flush as registers and reminiscence addresses (instead at the demur level), are still necessary for high-performance coding. Can it breathe true?

The underlying premise is that if you know how your engine works, you can drive it better to obtain optimal performance and endurance. This is as trusty for my 1985 300TD (Mercedes, five cylinder, turbo diesel station wagon) with 265,000 miles as for my Java code running on a HotSpot VM. For instance, knowing that a diesel's optimal performance is when the engine is warm since it relies on compression for power, I let my car warm up before I "push it." Similarly, I don't overload the vehicle with the tons of stuff I could station in the tailgate. HotSpot fits the analogy. Performance improves after the VM "warms up" and compiles the HotSpot code into the endemic language. I furthermore preserve my reminiscence footprint rawboned and light. The comparison breaks down after awhile, but the basic verisimilitude does not change. You can consume a system the best when you understand how it works.

Our challenge to you is to win statistics before and after implementing this pattern on just a tiny portion of your code. gladden recognize that the gain will breathe best exemplified when your application is scaled upward. In other words, the heavier the load on the system, the better the results.

The following statistics were taken after the pattern was applied. They are charted as:1.  Limited nullify system invocation is used where only the incoming messages are not "nullified." (The residuum of the application from which the statistics were taken was left intact with a very rawboned reminiscence usage.) There is no forced garbage collection.2.  Nullify system invocation and forced garbage collection is utilized.

The test environment is a Microsoft Windows 2000 X86 Family 15 Model 2 Stepping 4 Genuine Intel ~1794MHz laptop running the BEA WebLogic Server 7.0 with Service Pack 7.1 with a physical reminiscence size of 523,704KB. The Java Message Server (JMS server), a track generator, and a tactical display are every unique running on the selfsame laptop over the local developer network (MAGIC). The server makes no optimizations, even though each application resides locally. The JVMs are treated as if they were distributed across the network. They're running on the J2SE 1.4.1 release.

The test target application is a Java undulate Tactical display with plenary panning, zooming, and track-hooking capabilities. It receives bundles of tracks via the Java Message Service that are displayed at their proper location on the given image. Each track is approximately 88 bytes and the overall container size is about 70 bytes. This byte measurement does not include every unique the additional class information that's furthermore sent during serialization. The container is the message that holds an array of tracks that contains information such as time and number of tracks. For their tests, the tracks are sent at a 1Hz rate. Twenty sets of data are captured.

To illustrate the test environment, a screen capture of a 5,000 track load (4,999 tracks plus the ship) is shown in design 1. The background shows tracks rendered with the Military standard 2525B symbology over an image of the Middle East. The tiny window titled "Track Generator Desktop" is a minimized window showing the parameters of the test set through the track generator application. Notice that 45 messages had been sent at the time of the screen capture. Directly beneath this window sits the Windows task Manager. Note that the CPU utilization is at 83%. At first this doesn't appear that bad. But at that rate, there isn't much play for the user to launch zooming, panning, hooking tracks, and so on. The final command window to the privilege is that of the tactical display application. The parameter "-verbose:gc" is placed on the Java command line (java -verbose:gc myMainApplication.class). The VM is performing the listed garbage collection at its own rate, not by command of the application.

The final test of 10,000 tracks performed extremely poorly. The system does not scale; the CPU is pegged. At this point most engineers may jeer at Java again. Let's win another glance after implementing the pattern.

After implementation, where the nullify methods are invoked properly and garbage collection is requested at a fitful interval (2Hz), stagy improvements are realized. The terminal test of 10,000 tracks proves that the processor still has plenty of play to enact more work. In other words, the pattern scales very well.

Performance SummaryThe pattern to befriend control garbage collection pauses most definitely improves the overall performance of the application. Notice how well the pattern scales under the heavier track loads in the performance bar chart in design 2. The darker middle bar shows the processor utilization at each flush of the message (track) load. As the message traffic increases, the processor utilization grows more slowly than without the pattern. The terminal light-colored bar shows the improved performance. The main energy of the pattern is how well it scales under massive message loads.

There is another subtle energy to the pattern. This one is difficult to measure since it requires very long-lived tests. If Step 3 is faithfully followed, those horribly long garbage collection pauses that occur after hours of running disappear. This is a key capitalize to the pattern since most of their applications are designed to evade "forever."

We're confident that many other Java applications would capitalize from implementing this very simple pattern.

The steps to control garbage collection pauses are:1.  Set every unique objects that are no longer in consume to null and fabricate confident they're not left within some collection. "Nullify" objects.2.  constrain garbage collection to occur both:

  • After some major memory-intense operation (e.g., scaling an image)
  • At a fitful rate that provides the best performance for your application3.  deliver long-lived data in a persistent data region if feasible or in a pool of data and consume the appropriate garbage collector algorithm.

    By following these three simple steps, you'll avoid those bothersome garbage collection pauses and breathe pleased every unique the benefits of the Java environment. It's time the Java environment was fully utilized in mission-critical display systems.

    References

  • Gupta, A., and Doyle, M. "Turbo-Charging the Java HotSpot Virtual Machine, v1.4.x to improve the Performance and Scalability of Application Servers": http://developer.java.sun.com/developer/ technicalArticles/Programming/turbo/
  • JSR 1, Real-Time Specification for Java: http://jcp.org/en/jsr/detail?id=1
  • Java HotSpot VM options: http://java.sun.com/docs/hotspot/VMOptions.html
  • Java Specification Request for JCache: http://jcp.org/en/jsr/detail?id=107


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