Synthesizing disparate data for a comprehensive view of floating kelp distribution in Washington State, USA

Synthesizing disparate data for a comprehensive view of floating kelp distribution in Washington State, USA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Synthesizing disparate data for a comprehensive view of floating kelp distribution in Washington State, USA Gray E. McKenna, Danielle C. Claar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7482762/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Understanding the current distribution of floating kelp forests is critical for tracking changes to these ecologically, economically, and culturally important ecosystems. It also informs management efforts in response to threats from urbanization, climate change, and other environmental stressors. However, current floating kelp spatial datasets represent a diverse patchwork of surveys from varied time periods and with varied spatial resolution, making comparison and change analysis difficult. Here, we present a recent effort to integrate multiple datasets to comprehensively map kelp canopies in Washington State, USA. By synthesizing remote sensing data, in-situ boat-based surveys, and historical records to 1-km coastal line segments, we created a novel spatial dataset that describes the statewide distribution of floating kelp. Analysis of the most recent survey data shows that the proportion of floating kelp-containing shorelines is highest in the Western Strait of Juan de Fuca, followed by the North Coast, Eastern Strait of Juan de Fuca, and San Juan Islands. While a long time series of data exists in most of these areas, the San Juans has relatively few years of data. Proportional kelp presence is much lower throughout the rest of the State, including Puget Sound, and temporal data availability for these areas is similarly sparse. The dataset produced through this effort has enabled new approaches for statewide analyses and status reporting for floating kelp forests, and the synthesis methodology developed is deployable to other regions and species. Floating kelp Nereocystis luetkeana Macrocystis pyrifera mapping spatial analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Kelp forests are highly productive temperate marine ecosystems dominated by large brown macroalgae in the order Laminariales (Wernberg et al. 2019). They occupy an estimated 22% of the world’s coastline (Jayathilake and Costello 2020). Kelps cycle nutrients, sequester carbon, and provide habitat for many marine species of cultural, economic, and ecological significance (Naar 2020; Eger 2023; McHenry 2025). Kelp forests are under threat in many regions of the world from a range of stressors including warming water temperatures and increasingly frequent marine heatwaves, altered trophic dynamics, pollution, and other anthropogenic impacts (Estes et al. 2004; Krumhansl et al. 2016; Smale 2020; McPherson et al. 2021; Wernberg et al. 2025). In the face of these threats, long-term mapping and monitoring of kelp forests provides critical information to resource managers to inform conservation and recovery efforts (Hamilton et al. 2022). Kelp forests are dynamic with high interannual and regional variability, underscoring the need for long-term large-area data records (Edwards and Estes 2006; Reed et al. 2015; Bell et al. 2020). Floating kelp is commonly mapped from remote sensing platforms including satellites (Bell et al. 2020; Hamilton et al. 2020; Gendall 2023), manned aircraft (Van Wagen 2015; Pfister et al. 2018), and unoccupied aircraft (Cavanaugh et al. 2021; Saccomanno et al. 2023). In-situ boat-based methods, such as surveys from kayaks or small vessels, are also used to map floating kelp, especially in areas where floating kelp is difficult to detect from remote sensing platforms due to complex geomorphology, tide and current conditions, and challenging weather (Bishop 2014; Thompson 2021; Ledbetter and Berry 2024). In some areas, historical data sources including navigational charts, resource surveys, and oral histories have been retrospectively analyzed to extend long-term monitoring records decades and centuries into the past (Costa et al. 2020; Berry et al. 2021; Selgrath et al. 2024). These different mapping and monitoring methods are suited for different contexts, depending on the research or management questions, spatial scale, and local environmental conditions, and often a combination of methods may be implemented (Cavanaugh et al. 2021; Diggon et al. 2022; Reshitnyk et al. 2023). However, each method generates different types of data, for example, bed area with varying definitions of bed depending on the protocol, canopy area with varying spatial resolution depending on collection platform, or linear extent along varied definitions of the shoreline. These data are not readily comparable for the geospatial analysis needed for large area management, such as across an entire state. In Washington State, describing kelp distribution and trends has been identified as a key effort for advancing successful management (Calloway et al. 2020). Bull kelp ( Nereocystis luetkeana ) and giant kelp ( Macrocystis pyrifera ) are the two floating canopy-forming species of kelp in the northeast Pacific (Mumford 2007; Druehl and Clarkson 2016). Over the last century, floating kelp has been relatively stable in some areas of this region including the North Coast and the Strait of Juan de Fuca (Pfister et al. 2018) but has majorly declined in others including South Puget Sound, the innermost and southernmost sub-basin of the Salish Sea (Berry et al. 2021). Responding to documented declines, a law was passed in Washington State in 2022 directing the Department of Natural Resources to create a statewide Native Kelp Forest and Eelgrass Meadow Health and Conservation Plan to conserve and restore 10,000 acres of kelp forest and eelgrass meadow habitat by 2040 (RCW 79.135.440). Thus, there is a strong management need for cohesive statewide floating kelp data. While there are many floating kelp mapping and monitoring programs in Washington State, no single survey has comprehensively mapped floating kelp presence across the entire state since the Washington State ShoreZone Inventory was conducted between 1995 and 2000 (Berry et al. 2001). Floating kelp distribution in some areas of Washington State has changed significantly over the last 25+ years (Berry et al. 2021). Although much more recent floating kelp data exists from active monitoring programs and research efforts, the variety of methods and data formats constitutes a spatial and temporal patchwork of information, which makes these datasets difficult to use directly with one another. This presents a challenge for understanding the current status of floating kelp and planning conservation and restoration. The challenge of integrating disparate spatial data is not unique to kelp forests in Washington. Vast amounts of scientific data are collected at small scales and may not be readily accessible to the public but have the potential to provide important ecological insights (Heidorn 2008; Todman et al. 2023). Simultaneously, the availability and utility of big data for ecological research and conservation, particularly from remote sensing platforms, has dramatically increased in recent decades (Michener and Jones 2012; Hampton et al. 2013; Grémillet et al. 2022; Nathan et al. 2022). The rapid advancement of computing power has led to the development of novel methods for integrating both big and small spatial and non-spatial data into coherent synthetic datasets and databases (Gotway and Young 2002; Soranno et al. 2015; Pacific et al. 2017; Mahdavi et al 2024). A unique aspect of mapping floating kelp in the northeast Pacific, as opposed to mapping the distribution of terrestrial or mobile species, is that the potential distribution of floating kelp is restricted to a relatively narrow depth band along the coastline (Mumford 2007). This lends itself to the use of line segments to represent nearshore habitat. Linear vector units have been used to characterize shorelines throughout the northeast Pacific beginning in the 1980s through the ShoreZone protocol, which describes biological and geological characteristics of the shore for discrete linear geomorphological units (Harper et al. 1986; Berry et al. 2001; Harper and Morris 2004; Harper et al. 2013; Cook et al. 2017). Linear units have also been used to synthesize current and historical data for spatiotemporal analysis of floating kelp distribution in South Puget Sound, Washington (Berry et al. 2021), in British Columbia (Starko et al. 2024) and in Alaska (Hollarsmith et al. 2024). Here, we present a new implementation of the linear extent framework for integration of disparate floating kelp spatial data. By generating representational polygons for each line segment, we automated the synthesis of 12+ data sources to describe the spatial distribution of floating kelp across Washington State, providing a comprehensive dataset for research, management, and conservation applications. This method has potential utility for integrating spatial data in other regions and for other submerged or intertidal vegetation species. Materials and Methods Study System The state of Washington has an extensive and complex coastline. The western Olympic coast touches the northeast Pacific Ocean, and Pacific tides flow east through the Strait of Juan de Fuca and north into the Strait of Georgia, as well as south into Puget Sound. These areas inland of the Pacific Ocean are also referred to as the southern Salish Sea. Tidal currents are a major driver of circulation in the Salish Sea, and freshwater runoff and riverine inflows help create stratified conveyer-belt circulation with surface outflow of brackish waters and deeper inflow of saline waters (Khangaonkar et al. 2011). The complex geomorphology of both the Pacific coast and the Salish Sea contributes to heterogenous current, temperature, salinity, turbidity, and nutrient conditions throughout the State’s waters (Kozloff 1973; Khangaonkar et al. 2011). Anthropogenic impacts are also heterogenous across this complex coastline. The region has been populated by Native people for more than 12,000 years (Jepsen and Norberg 2017). Many sections of Puget Sound’s shoreline have been highly developed and urbanized since European colonization, which has had significant impacts on marine ecosystems in this region (Simenstad et al. 2011). The Floating Kelp Vital Sign Indicator, a component of the Puget Sound Action Agenda (Puget Sound Partnership 2022) divides Washington waters into 11 marine sub-basins based on geomorphology. These include, from the Pacific inland: the North Coast (NCO) and South Coast (SCO) along the Pacific side of the Olympic Peninsula, the Eastern and Western Strait of Juan de Fuca (EST and WST, respectively), the San Juan Islands (SJI), North Puget Sound (NPS) for waters north of the Strait of Juan de Fuca and east of the San Juan Islands, Admiralty Inlet (ADM) at the entrance to Puget Sound, Hood Canal (HDC), Saratoga-Whidbey Basin (SWB) inland of Whidbey Island, as well as Central Puget Sound (CPS) and finally the innermost basin, South Puget Sound (SPS) (Fig. 1, Berry et al. 2023). In this study, we report results of floating kelp linear extent and data availability for the entire State as well as by these sub-basins. Overview of Input Datasets Data sources (Table S1) summarized for this study included kelp bed and canopy area polygons or linear along-shore observations of floating kelp presence. The surveys generating these datasets were primarily conducted once or on an annual basis during summer around peak floating kelp biomass. Polygon data sources included classified fixed-wing aerial imagery from the Washington State Department of Natural Resources (WA DNR) (Van Wagenen 2015, Nearshore Habitat Program 2022) and Samish Indian Nation Department of Natural Resources (Samish Indian Nation GIS unpublished ), classified UAS imagery from WA DNR in collaboration with the Suquamish Tribe (McClure et al. unpublished ), and kayak survey data from WA DNR (Ledbetter and Berry 2025) and the Northwest Straits Commission and Marine Resources Committees (MRC) (Bishop et al. 2014, updated 2023). Linear data sources included ShoreZone (Berry et al. 2001), boat-based linear extent surveys conducted by WA DNR in South Puget Sound in 2017 (Berry et al. 2019) and in Central Puget Sound in 2019 (Berry et al. unpublished ), linear extent analysis of historical aerial photographs (McKenna et al. 2025a), as well as extensive analysis of historical data sources in South Puget Sound (Berry et al. 2021). This South Puget Sound historical analysis (Berry et al. 2021) includes a summary of the results from the 2017 boat-based surveys in South Puget Sound (Berry et al. 2019); to avoid duplication and retain the higher-resolution version of the 2017 survey data, the summarized version of the 2017 data in Berry et al. (2021) was excluded. All geoprocessing was conducted using ArcGIS Pro 3.3 tools (Esri, Redlands, CA) in the spatial reference NAD 1983 (HARN) State Plane Washington South. Constructing Synthesis Data Framework We generated 3,418 approximately equal 1-km linear segments representative of all adjacent marine nearshore habitat in Washington State as the fundamental units of analysis to describe floating kelp presence and absence (Fig. 2). These linear units are referred to as “segments” hereafter. These segments were initially derived from the deep edge of the WA DNR Submerged Vegetation Monitoring Program (SVMP) site polygons (Dowty et al. 2022). The SVMP dataset was chosen as an initial spatial framework for this study because decades of marine vegetation monitoring data for the Salish Sea in Washington State have already been mapped within these site polygons (Christiaen et al. 2022; Dowty et al. 2022), and additional site polygons following the SVMP structure already existed for the Pacific coast and coastal bays. The SVMP polygons span the Ordinary High Water line down to a -6.1 m MLLW isobath (bathymetric contour) derived from gridded bathymetric data (Nysewander et al. 2005), and the deep edge for most polygons extends along 1 km of the -6.1 m MLLW isobath. The -6.1 m MLLW depth represents a common maximum depth of Nereocystis luetkeana in inner basins of Puget Sound, making it a relevant depth contour for floating kelp mapping, although floating kelp occurs deeper in some locations in the State (Berry et al. 2019, Ledbetter and Berry 2025). Modifications were made to the SVMP deep edges to better characterize the spatial distribution of floating kelp along the shoreline. In the SVMP dataset, embayments, tide flats, and river deltas (“flats”) are represented by larger polygons that extend greater than 1 km along the isobath. The majority of these were divided at 1-km intervals to create more equal segments. Some of the resulting segments were slightly larger or smaller than 1 km, given the varying starting lengths of the flats polygons’ deep edges. Additional segments along the -6.1 m MLLW isobath were constructed for Destruction Island off the Pacific coast. Segments were also created around the San Juan Islands along the -12.2 m MLLW isobath to represent areas where the complex bathymetry facilitates offshore floating kelp “islands” deeper than -6.1 m MLLW that do not reasonably tie to nearby segments (Fig. 2c). The -12.2 m MLLW depth was selected to create these additional segments because it neatly encircled the deep edge of many offshore floating kelp “islands” based on visual review of floating kelp data sources. After these edits and modifications, 88% of segments were between 0.99 and 1.01 km. Some small segments that were remnants from the SVMP dataset were retained as-is to preserve compatibility between datasets; a handful of larger segments associated with flats were left as is in areas of the South Coast where floating kelp has never been reported. Each segment used for this analysis represents all nearshore habitat that falls within its proximity, as delineated by the SVMP polygons shallower than -6.1 m MLLW. However, given that kelp occurs deeper than -6.1 m MLLW in some areas, the remaining deep-water areas were also divided into Theissen polygons based on the line segments. Theissen polygons divide surfaces based on input features so that any point within a Thiessen polygon is closer to its parent feature than to any other feature. Theissen polygon generation requires point inputs rather than lines, so points were generated at 100-m intervals on each segment, then Theissen polygons were generated for each point, and finally these polygons were dissolved by a unique key field identifying each polygon and segment. The Theissen polygons merged with the adjacent SVMP polygons were used as the final “container” polygons, referred to as such because they contain all the area associated with a given segment. This allowed all floating kelp spatial data located anywhere in Washington waters to be associated with the appropriate segment (Fig. 2d). These containers were highly variable in area given the complex geomorphology of Washington’s waters. Larger containers were typically associated with gently sloped embayments and tidal flats. Small containers were associated with the remnant small segments from the SVMP dataset and were more common in areas where the bathymetry is highly complex. Containers were also smaller in narrow water passages like Agate Pass, north of Bainbridge Island, where opposite shorelines are relatively close to each other. Calculating Floating Kelp Presence For polygon data sources, the presence of floating kelp for each segment was evaluated using the Summarize Within tool from the ArcPy python module associated with ArcGIS Pro 3.3 (Esri, Redlands CA). For each year of data from each data source, the containers were clipped to the relevant survey boundary to enable distinguishing between areas with true floating kelp absence and areas that were not surveyed. If any floating kelp presence polygons fell within a clipped container from a given data source for a given year, a 1 was returned for presence for the corresponding segment otherwise a 0 was returned to indicate absence (Fig. 3a). For one data source, the Northwest Straits Commission MRC kayak polygons (Table S1), survey boundaries shifted over time and were not available for some years, so containers could not be clipped to survey boundaries and data was treated as presence only, rather than presence and absence. The linear floating kelp datasets were summarized with different methods. To translate the values from the ShoreZone linear data, which follows an Ordinary High Water contour, to the appropriate line segments, the ShoreZone floating kelp linear features were buffered by 10 m. This accounted for the few instances where ShoreZone linear features fell shallower than the containers. Any overlaps between the buffered lines were removed along centerlines, then the area for each category of kelp presence (“absent”, “patchy”, “continuous”) was calculated within each container; if any “patchy” or “continuous” kelp fell within the container, a 1 was returned for presence for the corresponding segment, otherwise a 0 was returned to indicate absence. The video capture date for each ShoreZone line, ranging between 1995 and 2000, was used to associate the survey year with each floating kelp observation. The methodology used to generate the original Washington ShoreZone dataset is described in Berry et al. (2001) The extensive historical analysis in South Puget Sound (Berry et al. 2021), the 2017 and 2019 surveys (Berry et al. 2019), and the 1984 imagery analysis project (McKenna et al. 2025a) all mapped floating kelp along the same segments used in this study and so those datasets were compiled directly with the segment-level floating kelp presence summaries derived above. Calculating Coverage Category The coverage category metric was developed to provide more granularity to segment-level floating kelp presence, which makes no distinction between a single individual kelp and a large kelp bed. To calculate coverage category of floating kelp along each segment, polygons were created by splitting the segments at 0.25-km intervals. These subdivided segments were buffered by 1 km and overlaps between the resulting buffer polygons were removed along centerlines, resulting in coarsely subdivided containers. Summarize Within was again used to evaluate kelp presence in each subdivision from each data source for each survey year. If floating kelp was present, a 1 was assigned to the subdivision, otherwise a 0 was assigned for absence. To account for variability in the number of subdivisions and resulting from parent segments longer or shorter than 1-km, each floating kelp presence value was weighted by its associated subdivision length as a percentage of the total parent line segment length and summed per line segment to calculate a weighted proportional presence P for each line segment (Equation 1, Fig. 3b). Where: P = the weighted proportional kelp presence for line segment i k j = the presence of kelp (0 or 1) for the j rh subdivision of line segment i l j = the length of j th subdivision of line segment i L i = the length of the line segment i The resulting value for P was then assigned to a coverage category class (Equation 2). (2) Coverage category was not calculated for data from the South Puget Sound historical analysis (Berry et al. 2021) as results were already summarized to the line segments. Synthesizing Results All segment-level floating kelp presence and coverage category results were compiled into a single spatial dataset with all available floating kelp records from each data source for each of the 3,418 segments. These results were evaluated at the statewide and sub-basin scale. To describe the most recent distribution of floating kelp throughout the State, records were filtered to the most recent year of data for each segment to create a new spatial layer. The most recent year varied between segments as there has been no recent synoptic floating kelp survey. When multiple records were available for the most recent year, as occurs at some sites with co-located monitoring, the source that mapped more kelp, based on presence and coverage category, was selected for inclusion in the most recent view. The percentage of segments with floating kelp present in the most recent year was calculated at the statewide level and the proportion of most recent segment-level coverage category was calculated for each sub-basin. To describe the spatial distribution of floating kelp across all data records, the percentage of segments where floating kelp was present at least once was calculated for each sub-basin. To evaluate temporal span of floating kelp data records, the average number of unique years of survey data for segments with floating kelp present at least once was calculated for each sub-basin as well as the number of records available from each year across the entire time span. Hood Canal (HDC) and the South Coast (SCO) had no records of floating kelp presence from any of the data sources included here and so were excluded from all sub-basin level analyses. The final dataset was made available to the public via an interactive data viewing platform (Fig. S2). This platform enables access to the synthesized linear extent data and serves as a visual compendium of the source floating kelp spatial datasets so that users can identify and retrieve source data that may be appropriate for their research or management questions. The datasets are also distributed with a user guide (McKenna et al. 2025b). Results Over 34,000 segment-level observations of floating kelp from 12 data sources were synthesized to 1-km line segments, including Berry et al. 2021 which itself synthesizes 48 data sources. According to the most recent floating kelp survey data, 28% of segments across the State had floating kelp present (Fig. 4, Fig. S4). For segments with floating kelp present in the most recent year, 95% were surveyed between 2022-2024. The oldest records that constitute the most recent survey for some segments are absence records from 1995-2000 ShoreZone surveys. These older absence records are primarily in areas where floating kelp does not occur and may never have occurred and so have not been resurveyed recently, such as Hood Canal (HDC) and the South Coast (SCO). Floating kelp distribution in Washington State varied greatly between the marine sub-basins (Fig. 1) in the most recent data. The Western Strait (WST) had the highest floating kelp presence (96%) and proportional floating kelp coverage within segments in the most recent data, where over 75% of segments had a coverage category of 4, indicating generally continuous floating kelp coverage within that subbasin (Fig. 5a). The Eastern Strait (EST), North Coast (NCO), and San Juan Islands (SJI) all had similar floating kelp presence in the most recent data (52-58%), but EST had proportionally higher within-segment coverage than NCO or SJI. North Puget Sound (NPS), Admiralty Inlet (ADM), Saratoga-Whidbey Basin (SWH), Central Puget Sound (CPS), and South Puget Sound (SPS) all had floating kelp present in less than 25% of segments in the most recent data and floating kelp coverage category was spread more evenly between 1 and 4. Floating kelp presence followed a similar pattern of variation between sub-basins when examining all presence records across time. In WST, floating kelp was observed in every segment (100%) at least once in the data records. In EST, NCO, and SJI, 75% or more of segments had floating kelp present at least once. There is markedly less floating kelp presence reported across all records for the NPS, ADM, SWH, CPS, and CPS, where 33% or fewer segments had floating kelp present at least once in the available data records. Data availability across all records also varied between sub-basins and followed similar patterns to floating kelp presence. WST had an average of 35 years of data available (± 0.0 SD, n = 100) for segments with floating kelp present once or more, and EST had an average of 26 years of data available (± 12.7 SD, n = 180) (Fig. 5b). However, SJI had an average of only 7 years of data available (± 2.6 SD, n = 552). For three of the four subbasins with high proportional floating kelp presence, NCO, WST, and EST, annual floating kelp survey data exists for most or all segments since 1989 (Fig. S3). For the San Juans Islands, the historical record was more sparse, with the earliest integrated records around 2000 and several sub-basin-wide surveys conducted between 2004 and 2023. Data availability was much lower for the remaining sub-basins, which also had less kelp presence overall. NPS had an average of 4 years of data available (± 4.9 SD, n = 93) for segments where floating kelp was present once or more, ADM had an average of 4 years of data available (± 5.7 SD, n = 67), SWH had an average of only 3 years of data available (± 2.3 SD, n = 58), CPS had an average of 3 (± 2.1 SD, n = 98), and SPS had an average of 9 years of data available (± 2.5 SD, n = 119) (Fig. 5b). NPS, ADM, SWH, and CPS have all had site-targeted monitoring programs in the last 5 to 15 years, but generally lack synoptic or analyzed historical data prior to these programs other than ShoreZone (Fig. S3). SPS has current site-targeted monitoring programs and the oldest available data records incorporated here (1873) given the historical analysis by Berry et al. (2021). Discussion Comprehensive mapping of species distribution is foundational for habitat inventory, conservation, restoration, and management (Schweiger et al. 2002; Brooks et al. 2004). The broad spatial and temporal perspective it affords is vital for understanding dynamics of kelp forests ecosystems (Reed et al. 2015), particularly in regions like Washington State where floating kelp persists in some areas but is declining in others (Pfister et al. 2018; Berry et al. 2021; Berry et al. 2023). In Washington State, no statewide floating kelp survey has been conducted since the late 1990s (Berry et al. 2001). However, a multitude of different monitoring programs provide relatively recent floating kelp distribution data for nearly all areas of the State where floating kelp is or has been present. Up-scaling and synthesizing these disparate datasets to a common linear spatial unit allows for comprehensive mapping, providing a cohesive dataset that is useful for research and provides essential information for natural resource management of nearshore marine systems. The methods and approach developed here also have potential applications for mapping floating kelp in other geographies as well as other species and habitats where data availability is patchy and/or comprised of multiple formats. The key components of this approach are (1) selecting synthesis units, in this case 1-km line segments, that are reasonable for the species and spatial scales of the study system in question, (2) defining the area that each synthesis unit represents, and (3) identifying and applying a common analysis workflow that functions across all datasets, in this case an ArcGIS Pro-based Summarize Within function on annualized data. Many technical considerations and decision-making factored into this synthesis; we describe them in the Supplementary Information to facilitate applying a similar approach elsewhere. By applying this methodology, we created a comprehensive floating kelp spatial dataset for Washington State, a jurisdiction with active floating kelp monitoring (Berry et al. 2023), restoration (McKenna et al. 2022), and conservation (Native Kelp Forest and Eelgrass Meadow Health and Conservation Plan 2022). The Western Strait of Juan de Fuca stood out as having the highest proportion of floating kelp-containing segments amongst the sub-basins, both historically and in the most recent available data, and had the highest coverage category values in the most recent available data, suggesting relatively continuous kelp presence along the shorelines of the sub-basin. Kelp forests in the Western Strait have been largely stable over the past century (Pfister et al. 2018) and have remained stable over recent years despite interannual variability (Berry et al. 2023, Tolimieri et al. 2023; Claar et al. in review ). The Eastern Strait of Juan de Fuca, North Coast, and San Juan Islands also had high proportions of floating kelp-containing segments, although more variable floating kelp coverage category values across segments, indicating patchier floating kelp presence in these sub-basins. Kelp forests along the Eastern Strait of Juan de Fuca and the North Coast have also been largely stable over the past century (Pfister et al. 2018), although recent declines have been detected in some areas of the Eastern Strait (Berry et al. 2023, Rubin et al. 2023). While there is a large spatial and temporal data record for the North Coast and Strait of Juan de Fuca, the San Juan Islands stand out as a region with high floating kelp presence but relatively few years of survey data available. The rest of Washington’s marine sub-basins have far lower proportions recent and historical kelp presence, and many sub-basins have very few years of data. These results point to the need for new historical analyses and continued monitoring to generate more floating kelp data in some regions of the state, including the San Juan Islands. In the San Juan Islands, the Samish Indian Nation has documented floating kelp losses by comparing Traditional Ecological Knowledge (TEK) with modern aerial imagery (Samish Indian Nation, unpublished ). Traditional Ecology Knowledge data is not currently incorporated into the linear extent dataset, but the framework developed here has the capacity to integrate this type of observational data in future iterations. The currently limited data record makes reporting changes and trends challenging in this sub-basin, but integration of TEK, historical records, and continued annual monitoring data may help paint a fuller picture of how floating kelp has changed over time. New analysis of historical data would also be valuable in North Puget Sound, Admiralty Inlet, Saratoga-Whidbey Basin, and Central Puget Sound, areas with some floating kelp presence but only a handful of years of survey data. Once historical data are digitized into a modern GIS format, they can be readily integrated into the floating kelp linear extent dataset using the framework described in this manuscript. For example, we were able to include historical records of floating kelp presence along the Seattle waterfront in 1984 analyzed in McKenna et al. (2025a), and records spanning approximately 145 years by integrating spatial data published in Berry et al. (2021). Continued future monitoring whilst extending the data record backwards with new historical analyses will enhance understanding of floating kelp distribution change over time. This is critical to improving the ability to understand the drivers of floating kelp loss and resilience across the entire State. While the linear extent method has yielded insights into floating kelp distribution, there are inherent limitations to this approach and the resulting dataset. This synthesis uses 1-km line segments as the common unit for analysis and generalizes floating kelp observations to an annual level. This requires upscaling and generalizing many of the source datasets both spatially and temporally, resulting in unavoidable loss of information resolution in the process of creating compatibility. In addition, not all line segments are 1 km in length exactly due to the complexity of Washington State’s coastline, and the area of nearshore habitat that each segment represents is similarly unequal. Finally, this synthesis method necessarily treats the inputs as true and accurate observations of floating kelp presence without addressing any underlying uncertainties in the source data. The use of this synthesized dataset in further analyses should therefore take these considerations into account. For example, filtering or weighting segments by length may facilitate more balanced units of floating kelp distribution for use in further analyses. When modeling floating kelp presence with other environmental variables, care should be taken to scale other datasets appropriately to match a resolution that incorporates the disparate datasets most effectively and comprehensively (Gotway and Young 2002). If a research or management question targets a more localized scale, using this dataset to identify and access the underlying higher-resolution floating kelp observations may be the most appropriate path. Therefore, care was taken in the construction of the dataset and web viewer so that users can readily find the source data driving the kelp presence, year, and coverage category for each line segment. Despite these limitations, the resulting dataset has wide-ranging applications beyond describing floating kelp distribution. The most recent floating kelp distribution is currently being integrated into a Puget Sound-wide analysis of floating kelp and eelgrass stressors (C. Magel, pers. comm. ). It will also be integrated into the determination of sub-basin status in the Floating Kelp Indicator (Berry et al. 2023), a component of the Puget Sound Partnership’s Vital Signs program (Puget Sound Partnership 2022). Because it uses mapping units derived from another marine vegetation monitoring program, it is primed to compare the distribution of floating kelp with other marine vegetation like eelgrass and understory kelp. Linear extent data has been used to evaluate the relationship between floating kelp loss and resilience with environmental variables in South Puget Sound (Berry et al. 2021), British Columbia (Starko et al. 2024), and Alaska (Hollarsmith et al. 2024). This dataset could similarly support a statewide analysis of change in the distribution of floating kelp and potential linkages with environmental drivers. It may also have applications for predictive distribution modeling under future climate scenarios, enabling managers to pre-emptively respond to areas at high risk to climate-related stressors (Reiss et al. 2015; Martinez et al. 2018; Khangaonkar et al. 2021). By providing a new statewide look at floating kelp distribution, the Washington State Floating Kelp Linear Extent dataset will enable better statewide management, conservation, and restoration of kelp forests. It helps identify spatial and temporal data gaps and can be integrated into analyses of current and future kelp stressors and other models. The approach outlined here can be applied in other areas or for other species where spatial data is available but in disparate formats. Declarations None. Funding This research was funded by the Washington State Legislature. Competing interests None declared. Availability of data and material Data is available as a file geodatabase download through the Nearshore Habitat Program webpage [https://dnr.wa.gov/aquatics/aquatic-science/nearshore-habitat-program] and as hosted feature layers through ArcGIS Online [https://experience.arcgis.com/experience/e03ea8b2a6574e4094230aff5e862626/] Code availability Code is available at https://github.com/WA-Nearshore/wa-kelp-linear-extent Author’s contributions DC and GM designed the study questions. GM developed the methodology, authored all scripts, conducted all geoprocessing and analysis, and wrote the initial manuscript draft. DC provided substantial revisions to the manuscript. Acknowledgements The Samish Indian Nation and the Northwest Straits Commission provided key datasets for this project. Helen Berry laid the conceptual foundation for the project. Tyler Cowdrey provided substantial comments on the initial project design and manuscript draft, and Bart Christiaen, Pete Dowty, Lisa Ferrier, Jeff Gaeckle, and Julia Ledbetter provided comments on the manuscript draft. References Bell TW, Allen JG, Cavanaugh KC, Siegel DA (2020) Three decades of variability in California’s giant kelp forests from the Landsat satellites. Remote Sens Environ 238:110811. Berry HD, Harper JR, Mumford TF, Bookheim BE, Sewell AT, Tamayo, LJ (2001) The Washington State ShoreZone Inventory User's Manual. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1-23 Berry HD, Calloway M, Ledbetter J (2019) Bull Kelp Monitoring in South Puget Sound in 2017 and 2018. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1-60 Berry HD, Mumford TF, Christiaen B, Dowty P, Calloway M, Ferrier L, Grossman EE, VanArendonk NR (2021) Long-term changes in kelp forests in an inner basin of the Salish Sea. PLOS ONE 16:e0229703. Berry HD, Raymond WW, Claar DC, Dowty P, Spaulding E, Christiansen B, Ferrier L, Ledbetter J, Naar N, Woodard T, Palmer-McGee C, Cowdrey T, Oster D, Shull S, Mumford T, Dethier M (2023) Floating Kelp Monitoring in Washington State: Statewide Summary Report. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1-22. Bishop E (2014) A kayak-based survey protocol for Bull Kelp in Puget Sound – updated 2023. Northwest Straits Commission, Mount Vernon, Washington. pp 1-10 Brooks RP, Wardrop DH, Bishop JA (2004) Assessing Wetland Condition on a Watershed Basis in the Mid-Atlantic Region Using Synoptic Land-Cover Maps. Environ Monit Assess 94:9–22. 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Ledbetter J, Berry HD (2025) Long-term kayak monitoring of floating kelp in Puget Sound: Results through field year 2024. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1 - 129 Mahdavi S, Amani M, Parsian S, MacDonald C, Teasdale M, So J, Zhang F, Gullage M (2024) A Combination of Remote Sensing Datasets for Coastal Marine Habitat Mapping Using Random Forest Algorithm in Pistolet Bay, Canada. Remote Sens 16:2654 Martínez B, Radford B, Thomsen MS, Connell SD, Carreño F, Bradshaw CJA., Fordham DA, Russell BD, Gurgel CFD, Wernberg T (2018) Distribution models predict large contractions of habitat‐forming seaweeds in response to ocean warming. Divers Distrib 24:1350–1366. McHenry J, Okamoto DK, Filbee-Dexter K, Krumhansl KA, MacGregor KA, Hessing-Lewis M, Timmer B, Archambault P, Attridge CM, Cottier D, Costa M (2025) A blueprint for national assessments of the blue carbon capacity of kelp forests applied to Canada’s coastline. Npj Ocean Sustain 4:30. McPherson ML, Finger DJ, Houskeeper HF, Bell TW, Carr MH, Rogers-Bennett L, Kudela RM. (2021) Large-scale shift in the structure of a kelp forest ecosystem co-occurs with an epizootic and marine heatwave. Commun Biol 4:298. McKenna G, B Allen, H Hayford, D Tonnes (2022) Kelp forest restoration in Puget Sound: Outplant techniques and lessons learned. Salish Sea Ecosystem Conference, online. McKenna G, Berry H, Claar D, Cowdrey T (2025a) Mapping floating kelp presence along Seattle shorelines in 1984 using historical aerial imagery. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1-36 McKenna G, Cowdrey T, Claar D (2025b) Washington Floating Kelp Linear Extent Data User Guide. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1-18 Michener WK, Jones MB (2012) Ecoinformatics: supporting ecology as a data-intensive science. Trends Ecol Evol 27:85–93. Mumford TJ (2007) Kelp and Eelgrass in Puget Sound. Washington Department of Fish and Wildlife, Olympia, Washington. pp 1-34. Naar N (2020) Appendix B: The Cultural Importance of Kelp for Pacific Northwest Tribes. Puget Sound Kelp Conservation and Recovery Plan. Seattle, Washington. pp B1 – B12 Nathan R, Monk CT, Arlinghaus R, Adam T, Alós J, Assaf M, Baktoft H, Beardsworth CE, Bertram MG, Bijleveld AI, Brodin T (2022) Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375: eabg1780. Nearshore Habitat Program (2022) WA DNR COSTR/AQRES Aerial Imagery (v2021.0) [Data set]. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. Pacifici K, Reich BJ, Miller DA, Gardner B, Stauffer G, Singh S, McKerrow A, Collazo JA. (2017). Integrating multiple data sources in species distribution modeling: a framework for data fusion. Ecology 98:840–850 Pfister CA, Berry HD, Mumford T (2018) The dynamics of Kelp Forests in the Northeast Pacific Ocean and the relationship with environmental drivers. J Ecol 106: 1520–1533. Reed DC, Rassweiler AR, Miller RJ, Page HM, Holbrook SJ (2015) The value of a broad temporal and spatial perspective in understanding dynamics of kelp forest ecosystems. Mar Freshwater Res 67:14–24. Reiss H, Birchenough S, Borja A, Buhl-Mortensen L, Craeymeersch J, Dannheim J, Darr A, Galparsoro I, Gogina M, Neumann H, Populus J (2015) Benthos distribution modelling and its relevance for marine ecosystem management. ICES J Mar Sci 72:297–315. Reshitnyk L, Saccomanno V, Bell T, Cavanaugh KC (2023) Mapping Canopy-Forming Kelps in the Northeast Pacific: A guidebook for Decision-Makers and Practitioners. Hakai Institute. Campbell River, British Columbia. pp 1-45 Rubin SP, Foley MM, Miller IM, Stevens AW, Warrick JA, Berry HD, Elder NE, Beirne MM, Gelfenbaum G (2023) Nearshore subtidal community response during and after sediment disturbance associated with dam removal. Front Ecol Evol 11:1233895 Saccomanno VR, Bell T, Pawlak C, Stanley CK, Cavanaugh KC, Hohman R, Klausmeyer KR, Cavanaugh K, Nickels A, Hewerdine W, Garza C (2023) Using unoccupied aerial vehicles to map and monitor changes in emergent kelp canopy after an ecological regime shift. Remote Sens Ecol Conserv 9:62-75. Selgrath JC, Carlton JT, Pearse J, Thomas T, Micheli F (2024) Setting deeper baselines: kelp forest dynamics in California over multiple centuries. Reg Environ Change 24:104. Schweiger EW, Leibowitz SG, Hyman JB, Foster WE, Downing MC (2002) Synoptic assessment of wetland function: a planning tool for protection of wetland species biodiversity. Biodivers Conserv 11:379–406. Simenstad C, Ramirez M, Burke J, Logsdon M, Shipman H, Tanner C, Toft J, Craig B, Davis C, Fung J, Bloch P, Fresh K, Campbell S, Myers D, Iverson E, Bailey A, Schlenger P, Kiblinger C, Myre P, Gertsel WI, MacLennan A (2011) Historical Change of Puget Sound Shorelines: Puget Sound Nearshore Ecosystem Project Change Analysis. Washington Department of Fish and Wildlife, Olympia, Washington, and U.S. Army Corps of Engineers, Seattle, Washington. Puget Sound Nearshore Report No. 2011-01 pp 1-289 Smale DA (2020) Impacts of ocean warming on kelp forest ecosystems. New Phytol 225:1447–1454. Soranno PA, Bissell EG, Cheruvelil KS, Christel ST, Collins SM, Fergus CE, Filstrup CT, Goring SJ, Isaak DJ, Johnston S, Knoll LB, Liu J, Oliver SK, Olmanson L, Soranno D, Thompson SE, Van Sickle J, Winslow LA (2015) Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science and data reuse. GigaScience 4:28. Starko S, Timmer B, Reshitnyk L, Csordas M, McHenry J, Schroeder S, Hessing-Lewis M, Costa M, Zielinksi A, Zielinksi R, Cook S, Underhill R, Boyer L, Fretwell C, Yakimishyn J, Heath W, Gruman C, Hingmire D, Baum J, Neufeld C (2024) Local and regional variation in kelp loss and stability across coastal British Columbia. Mar Ecol-Prog Ser, 733:1–26. Thompson, Markus. 2021. MaPP Kelp Monitoring Protocol. Marine Plan Partnership. Quadra Island, British Columbia. pp 1-11 Todman LC, Bush A, Hood ASC (2023) ‘Small Data’ for big insights in ecology. Trends Ecol Evol 38:615–622. Tolimieri N, Shelton A, Samhouri J, Harvey C, Feist B, Williams G, Andrews K, Frick K, Lonhart S, Sullaway G, Liu O, Berry H, Waddell J (2023) Changes in kelp forest communities off Washington, USA, during and after the 2014-2016 marine heatwave and sea star wasting syndrome. Mar Ecol-Prog Ser 703:47–66. Van Wagenen, A. 2015. Washington Coastal Kelp Resources: Port Townsend to the Columbia River, Summer 2014. Ecosan Resource Data. Watsonville, California. Wernberg T, Krumhansl KA, Filbee-Dexter K, Pedersen MF (2019) Status and Trends for the World’s Kelp Forests. In: World Seas: An Environmental Evaluation. Academic Press, Cambridge, pp. 57–78. Wernberg T, Thomsen MS, Burrows MT, Filbee-Dexter K., Hobday AJ, Holbrook NJ, Montie S, Moore PJ, Oliver ECJ, Sen Gupta A, Smale DA, Smith K (2025). Marine heatwaves as hot spots of climate change and impacts on biodiversity and ecosystem services. Nat Rev Biodivers 1:461–479. Additional Declarations No competing interests reported. Supplementary Files mckennaclaarkelplinearextentsupp.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Sep, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 12 Sep, 2025 First submitted to journal 28 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7482762","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514547520,"identity":"2f463316-1546-47e7-9cd9-22c77763b216","order_by":0,"name":"Gray E. McKenna","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBADAzD5gY1ULYwzSNbCzEOMFvnow88kGHPsjPmlew9+tim7k88gkfyA8UvFYZxaDM+lmUkwbks2k5xzLlk659wzywaJNANmmTN4tPQwgLQcsDG4kWMgndt22IBBOsGAWbItDY8W9m9gLfY3cox/W4K1pH/Aq0Wehwdsi5mBRI6ZNCNYS44B48c2G5xaDHh4ii0StyUbS9zIMbPsOXfYgE3+TcFhhjO4tcj3sG+88XGbnWH/jBzjGz/KDhvw8xzf+PBHhQRuWw4wsEgkIIuAouYwD04NQFsaGJg/YIgy/sCjZRSMglEwCkYcAABQgE0wV5bVUQAAAABJRU5ErkJggg==","orcid":"","institution":"Washington State Department of Natural Resources","correspondingAuthor":true,"prefix":"","firstName":"Gray","middleName":"E.","lastName":"McKenna","suffix":""},{"id":514547521,"identity":"98614d71-840d-4ccf-90e7-cf89d03a642f","order_by":1,"name":"Danielle C. Claar","email":"","orcid":"","institution":"Washington State Department of Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Danielle","middleName":"C.","lastName":"Claar","suffix":""}],"badges":[],"createdAt":"2025-08-28 18:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7482762/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7482762/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93759881,"identity":"437e475d-6e1f-4b51-b414-8eddd4292a5f","added_by":"auto","created_at":"2025-10-17 09:26:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2461626,"visible":true,"origin":"","legend":"\u003cp\u003eMap of study area and sub-basins as delineated by the Floating Kelp Indicator Vital Sign. Sub-basins are outlined in dark grey and labeled with three-character codes; ADM: Admiralty Inlet; CPS: Central Puget Sound; EST: Eastern Strait of Juan de Fuca; HDC: Hood Canal; NCO: North Coast; NPS: North Puget Sound; SJI: San Juan Islands; SPS: South Puget Sound; WST: Western Strait of Juan de Fuca. Basemap data: Airbus, USGS, NGA, NASA, CGIAR, NCEAS, NLS, OS, NMA, Ge and the GIS User Community\u003c/p\u003e","description":"","filename":"Fig1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7482762/v1/53159e3f5daccc535d2ac0be.jpg"},{"id":93758876,"identity":"948b4271-227f-4f75-b0b3-6d893898f267","added_by":"auto","created_at":"2025-10-17 09:18:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2027276,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of the segments and associated containers used for data synthesis in this project. Top left map indicates the extent of examples of how different geomorphologies are represented in this system; \u003cstrong\u003ea \u003c/strong\u003elarge flats and islands, \u003cstrong\u003eb\u003c/strong\u003e steeply-sloped channels, \u003cstrong\u003ec \u003c/strong\u003ecomplex bathymetry with some offshore kelp “islands”\u003cstrong\u003e d \u003c/strong\u003ethe synthesis geometry labeled where the area shallow of the line segment is dervied from the Submerged Vegetation Monitoring Program (SVMP) dataset while the deeper portion of the polygon was generated using a Theissen approach\u003c/p\u003e","description":"","filename":"Fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7482762/v1/60c16cfadafc77c80a9355b1.jpg"},{"id":93758612,"identity":"66e68cc6-3463-4d2b-ad2c-48662d016497","added_by":"auto","created_at":"2025-10-17 09:10:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1817612,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Example workflow for calculating floating kelp linear extent from polygon kelp data. From left to right: Example kelp bed (black) and survey boundary (dashed) over containers (light grey). Containers are clipped to survey boundary. Kelp presence is calculated as 1 if present and 0 if absent. Results are assigned to corresponding line segments. \u003cstrong\u003eb \u003c/strong\u003eAnalysis method for calculating coverage category from polygon kelp data. From left to right: Lines are split at 0.25-km intervals and buffered to create subdivided containers (dashed lines). Floating kelp data is assessed against subdivided containers. Values are converted to presence/absence. Weighted score per segment is combined to calculate coverage category for each line segment\u003c/p\u003e","description":"","filename":"Fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7482762/v1/73a6efa7e702a89a810ac69d.jpg"},{"id":93758615,"identity":"b8ca95a2-0b80-40a2-b05f-cf0cc512c8a5","added_by":"auto","created_at":"2025-10-17 09:10:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1861239,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eFloating kelp presence based on the most recent data available for each line segment symbolized as present (black) or absent (grey). \u003cstrong\u003eb \u003c/strong\u003eMost recent floating kelp survey year for each line segment symbolized by color (older = cooler colors, more recent = warmer colors). Line segments last surveyed by ShoreZone, between 1995-2000, shown in grey.\u003c/p\u003e","description":"","filename":"Fig4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7482762/v1/05b89fadf3f173ac842645c4.jpg"},{"id":93758878,"identity":"f4d476dd-8d5c-46d0-b601-1190c0987b15","added_by":"auto","created_at":"2025-10-17 09:18:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":886767,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eProportion of coverage category for segments within each sub-basin in most recent survey data for each segment. Kelp coverage category shown in a gradient from light grey (0) to black (4). \u003cstrong\u003eb \u003c/strong\u003eRelationship between mean number of unique years of data per line segment with floating kelp present once or more (+/- SD) and the percent of segments with floating kelp present once or more for each sub-basin. Sub-basins with more proportional kelp presence are towards the right; sub-basins with more years of survey data, on average, are higher. The South Coast and Hood Canal are not displayed as no floating kelp was reported in those sub-basins.\u003c/p\u003e","description":"","filename":"Fig5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7482762/v1/15fa41267e5a31331311263b.jpg"},{"id":93760007,"identity":"05b8fa56-b5f7-404e-b7c5-19a7d8b0d9fa","added_by":"auto","created_at":"2025-10-17 09:34:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9519336,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7482762/v1/c9a860d2-896e-4634-a20a-b332caba6186.pdf"},{"id":93758618,"identity":"94f3cf16-4337-41f9-87d8-cb6c2425a5ee","added_by":"auto","created_at":"2025-10-17 09:10:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":506792,"visible":true,"origin":"","legend":"","description":"","filename":"mckennaclaarkelplinearextentsupp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7482762/v1/9600c566b3049d7abcaa34d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Synthesizing disparate data for a comprehensive view of floating kelp distribution in Washington State, USA","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKelp forests are highly productive temperate marine ecosystems dominated by large brown macroalgae in the order \u003cem\u003eLaminariales\u0026nbsp;\u003c/em\u003e(Wernberg et al. 2019). They occupy an estimated 22% of the world\u0026rsquo;s coastline (Jayathilake and Costello 2020). Kelps cycle nutrients, sequester carbon, and provide habitat for many marine species of cultural, economic, and ecological significance (Naar 2020; Eger 2023; McHenry 2025). Kelp forests are under threat in many regions of the world from a range of stressors including warming water temperatures and increasingly frequent marine heatwaves, altered trophic dynamics, pollution, and other anthropogenic impacts (Estes et al. 2004; Krumhansl et al. 2016; Smale 2020; McPherson et al. 2021; Wernberg et al. 2025).\u003c/p\u003e\n\u003cp\u003eIn the face of these threats, long-term mapping and monitoring of kelp forests provides critical information to resource managers to inform conservation and recovery efforts (Hamilton et al. 2022). Kelp forests are dynamic with high interannual and regional variability, underscoring the need for long-term large-area data records (Edwards and Estes 2006; Reed et al. 2015; Bell et al. 2020). Floating kelp is commonly mapped from remote sensing platforms including satellites (Bell et al. 2020; Hamilton et al. 2020; Gendall 2023), manned aircraft (Van Wagen 2015; Pfister et al. 2018), and unoccupied aircraft (Cavanaugh et al. 2021; Saccomanno et al. 2023). In-situ boat-based methods, such as surveys from kayaks or small vessels, are also used to map floating kelp, especially in areas where floating kelp is difficult to detect from remote sensing platforms due to complex geomorphology, tide and current conditions, and challenging weather (Bishop 2014; Thompson 2021; Ledbetter and Berry 2024). In some areas, historical data sources including navigational charts, resource surveys, and oral histories have been retrospectively analyzed to extend long-term monitoring records decades and centuries into the past (Costa et al. 2020; Berry et al. 2021; Selgrath et al. 2024). These different mapping and monitoring methods are suited for different contexts, depending on the research or management questions, spatial scale, and local environmental conditions, and often a combination of methods may be implemented (Cavanaugh et al. 2021; Diggon et al. 2022; Reshitnyk et al. 2023). However, each method generates different types of data, for example, bed area with varying definitions of bed depending on the protocol, canopy area with varying spatial resolution depending on collection platform, or linear extent along varied definitions of the shoreline. These data are not readily comparable for the geospatial analysis needed for large area management, such as across an entire state.\u003c/p\u003e\n\u003cp\u003eIn Washington State, describing kelp distribution and trends has been identified as a key effort for advancing successful management (Calloway et al. 2020). Bull kelp (\u003cem\u003eNereocystis luetkeana\u003c/em\u003e) and giant kelp (\u003cem\u003eMacrocystis pyrifera\u003c/em\u003e) are the two floating canopy-forming species of kelp in the northeast Pacific (Mumford 2007; Druehl and Clarkson 2016). Over the last century, floating kelp has been relatively stable in some areas of this region including the North Coast and the Strait of Juan de Fuca (Pfister et al. 2018) but has majorly declined in others including South Puget Sound, the innermost and southernmost sub-basin of the Salish Sea (Berry et al. 2021). Responding to documented declines, a law was passed in Washington State in 2022 directing the Department of Natural Resources to create a statewide Native Kelp Forest and Eelgrass Meadow Health and Conservation Plan to conserve and restore 10,000 acres of kelp forest and eelgrass meadow habitat by 2040 (RCW 79.135.440). Thus, there is a strong management need for cohesive statewide floating kelp data.\u003c/p\u003e\n\u003cp\u003eWhile there are many floating kelp mapping and monitoring programs in Washington State, no single survey has comprehensively mapped floating kelp presence across the entire state since the Washington State ShoreZone Inventory was conducted between 1995 and 2000 (Berry et al. 2001). Floating kelp distribution in some areas of Washington State has changed significantly over the last 25+ years (Berry et al. 2021). Although much more recent floating kelp data exists from active monitoring programs and research efforts, the variety of methods and data formats constitutes a spatial and temporal patchwork of information, which makes these datasets difficult to use directly with one another. This presents a challenge for understanding the current status of floating kelp and planning conservation and restoration.\u003c/p\u003e\n\u003cp\u003eThe challenge of integrating disparate spatial data is not unique to kelp forests in Washington. Vast amounts of scientific data are collected at small scales and may not be readily accessible to the public but have the potential to provide important ecological insights (Heidorn 2008; Todman et al. 2023). Simultaneously, the availability and utility of big data for ecological research and conservation, particularly from remote sensing platforms, has dramatically increased in recent decades (Michener and Jones 2012; Hampton et al. 2013; Gr\u0026eacute;millet et al. 2022; Nathan et al. 2022). The rapid advancement of computing power has led to the development of novel methods for integrating both big and small spatial and non-spatial data into coherent synthetic datasets and databases (Gotway and Young 2002; Soranno et al. 2015; Pacific et al. 2017; Mahdavi et al 2024).\u003c/p\u003e\n\u003cp\u003eA unique aspect of mapping floating kelp in the northeast Pacific, as opposed to mapping the distribution of terrestrial or mobile species, is that the potential distribution of floating kelp is restricted to a relatively narrow depth band along the coastline (Mumford 2007). This lends itself to the use of line segments to represent nearshore habitat. Linear vector units have been used to characterize shorelines throughout the northeast Pacific beginning in the 1980s through the ShoreZone protocol, which describes biological and geological characteristics of the shore for discrete linear geomorphological units (Harper et al. 1986; Berry et al. 2001; Harper and Morris 2004; Harper et al. 2013; Cook et al. 2017). Linear units have also been used to synthesize current and historical data for spatiotemporal analysis of floating kelp distribution in South Puget Sound, Washington (Berry et al. 2021), in British Columbia (Starko et al. 2024) and in Alaska (Hollarsmith et al. 2024). Here, we present a new implementation of the linear extent framework for integration of disparate floating kelp spatial data. By generating representational polygons for each line segment, we automated the synthesis of 12+ data sources to describe the spatial distribution of floating kelp across Washington State, providing a comprehensive dataset for research, management, and conservation applications. This method has potential utility for integrating spatial data in other regions and for other submerged or intertidal vegetation species.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003eStudy System\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe state of Washington has an extensive and complex coastline. The western Olympic coast touches the northeast Pacific Ocean, and Pacific tides flow east through the Strait of Juan de Fuca and north into the Strait of Georgia, as well as south into Puget Sound. These areas inland of the Pacific Ocean are also referred to as the southern Salish Sea. Tidal currents are a major driver of circulation in the Salish Sea, and freshwater runoff and riverine inflows help create stratified conveyer-belt circulation with surface outflow of brackish waters and deeper inflow of saline waters (Khangaonkar et al. 2011). The complex geomorphology of both the Pacific coast and the Salish Sea contributes to heterogenous current, temperature, salinity, turbidity, and nutrient conditions throughout the State\u0026rsquo;s waters (Kozloff 1973; Khangaonkar et al. 2011). Anthropogenic impacts are also heterogenous across this complex coastline. The region has been populated by Native people for more than 12,000 years (Jepsen and Norberg 2017). Many sections of Puget Sound\u0026rsquo;s shoreline have been highly developed and urbanized since European colonization, which has had significant impacts on marine ecosystems in this region (Simenstad et al. 2011).\u003c/p\u003e\n\u003cp\u003eThe Floating Kelp Vital Sign Indicator, a component of the Puget Sound Action Agenda (Puget Sound Partnership 2022) divides Washington waters into 11 marine sub-basins based on geomorphology. These include, from the Pacific inland: the North Coast (NCO) and South Coast (SCO) along the Pacific side of the Olympic Peninsula, the Eastern and Western Strait of Juan de Fuca (EST and WST, respectively), the San Juan Islands (SJI), North Puget Sound (NPS) for waters north of the Strait of Juan de Fuca and east of the San Juan Islands, Admiralty Inlet (ADM) at the entrance to Puget Sound, Hood Canal (HDC), Saratoga-Whidbey Basin (SWB) inland of Whidbey Island, as well as Central Puget Sound (CPS) and finally the innermost basin, South Puget Sound (SPS) (Fig. 1, Berry\u0026nbsp;et al. 2023). In this study, we report results of floating kelp linear extent and data availability for the entire State as well as by these sub-basins.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOverview of Input Datasets\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData sources (Table S1) summarized for this study included kelp bed and canopy area polygons or linear along-shore observations of floating kelp presence. The surveys generating these datasets were primarily conducted once or on an annual basis during summer around peak floating kelp biomass. Polygon data sources included classified fixed-wing aerial imagery from the Washington State Department of Natural Resources (WA DNR) (Van Wagenen 2015, Nearshore Habitat Program 2022) and Samish Indian Nation Department of Natural Resources (Samish Indian Nation GIS \u003cem\u003eunpublished\u003c/em\u003e), classified UAS imagery from WA DNR in collaboration with the Suquamish Tribe (McClure et al. \u003cem\u003eunpublished\u003c/em\u003e), and kayak survey data from WA DNR (Ledbetter and Berry 2025) and the Northwest Straits Commission and Marine Resources Committees (MRC) (Bishop et al. 2014, updated 2023). Linear data sources included ShoreZone (Berry et al. 2001), boat-based linear extent surveys conducted by WA DNR in South Puget Sound in 2017 (Berry et al. 2019) and in Central Puget Sound in 2019 (Berry et al. \u003cem\u003eunpublished\u003c/em\u003e), linear extent analysis of historical aerial photographs (McKenna et al. 2025a), as well as extensive analysis of historical data sources in South Puget Sound (Berry et al. 2021). This South Puget Sound historical analysis (Berry et al. 2021) includes a summary of the results from the 2017 boat-based surveys in South Puget Sound (Berry et al. 2019); to avoid duplication and retain the higher-resolution version of the 2017 survey data, the summarized version of the 2017 data in Berry et al. (2021) was excluded. All geoprocessing was conducted using ArcGIS Pro 3.3 tools (Esri, Redlands, CA) in the spatial reference NAD 1983 (HARN) State Plane Washington South.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConstructing Synthesis Data Framework\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe generated 3,418 approximately equal 1-km linear segments representative of all adjacent marine nearshore habitat in Washington State as the fundamental units of analysis to describe floating kelp presence and absence (Fig. 2). These linear units are referred to as \u0026ldquo;segments\u0026rdquo; hereafter. These segments were initially derived from the deep edge of the WA DNR Submerged Vegetation Monitoring Program (SVMP) site polygons (Dowty et al. 2022). The SVMP dataset was chosen as an initial spatial framework for this study because decades of marine vegetation monitoring data for the Salish Sea in Washington State have already been mapped within these site polygons (Christiaen et al. 2022; Dowty et al. 2022), and additional site polygons following the SVMP structure already existed for the Pacific coast and coastal bays. The SVMP polygons span the Ordinary High Water line down to a -6.1 m MLLW isobath (bathymetric contour) derived from gridded bathymetric data (Nysewander et al. 2005), and the deep edge for most polygons extends along 1 km of the -6.1 m MLLW isobath. The -6.1 m MLLW depth represents a common maximum depth of \u003cem\u003eNereocystis luetkeana\u003c/em\u003e in inner basins of Puget Sound, making it a relevant depth contour for floating kelp mapping, although floating kelp occurs deeper in some locations in the State (Berry et al. 2019, Ledbetter and Berry 2025).\u003c/p\u003e\n\u003cp\u003eModifications were made to the SVMP deep edges to better characterize the spatial distribution of floating kelp along the shoreline. In the SVMP dataset, embayments, tide flats, and river deltas (\u0026ldquo;flats\u0026rdquo;) are represented by larger polygons that extend greater than 1 km along the isobath. The majority of these were divided at 1-km intervals to create more equal segments. Some of the resulting segments were slightly larger or smaller than 1 km, given the varying starting lengths of the flats polygons\u0026rsquo; deep edges. Additional segments along the -6.1 m MLLW isobath were constructed for Destruction Island off the Pacific coast. \u0026nbsp;Segments were also created around the San Juan Islands along the -12.2 m MLLW isobath to represent areas where the complex bathymetry facilitates offshore floating kelp \u0026ldquo;islands\u0026rdquo; deeper than -6.1 m MLLW that do not reasonably tie to nearby segments (Fig. 2c). The -12.2 m MLLW depth was selected to create these additional segments because it neatly encircled the deep edge of many offshore floating kelp \u0026ldquo;islands\u0026rdquo; based on visual review of floating kelp data sources. After these edits and modifications, 88% of segments were between 0.99 and 1.01 km. Some small segments that were remnants from the SVMP dataset were retained as-is to preserve compatibility between datasets; a handful of larger segments associated with flats were left as is in areas of the South Coast where floating kelp has never been reported.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach segment used for this analysis represents all nearshore habitat that falls within its proximity, as delineated by the SVMP polygons shallower than -6.1 m MLLW. However, given that kelp occurs deeper than -6.1 m MLLW in some areas, the remaining deep-water areas were also divided into Theissen polygons based on the line segments. Theissen polygons divide surfaces based on input features so that any point within a Thiessen polygon is closer to its parent feature than to any other feature. Theissen polygon generation requires point inputs rather than lines, so points were generated at 100-m intervals on each segment, then Theissen polygons were generated for each point, and finally these polygons were dissolved by a unique key field identifying each polygon and segment. The Theissen polygons merged with the adjacent SVMP polygons were used as the final \u0026ldquo;container\u0026rdquo; polygons, referred to as such because they contain all the area associated with a given segment. This allowed all floating kelp spatial data located anywhere in Washington waters to be associated with the appropriate segment (Fig. 2d). These containers were highly variable in area given the complex geomorphology of Washington\u0026rsquo;s waters. Larger containers were typically associated with gently sloped embayments and tidal flats. Small containers were associated with the remnant small segments from the SVMP dataset and were more common in areas where the bathymetry is highly complex. Containers were also smaller in narrow water passages like Agate Pass, north of Bainbridge Island, where opposite shorelines are relatively close to each other.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCalculating Floating Kelp Presence\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor polygon data sources, the presence of floating kelp for each segment was evaluated using the Summarize Within tool from the ArcPy python module associated with ArcGIS Pro 3.3 (Esri, Redlands CA). For each year of data from each data source, the containers were clipped to the relevant survey boundary to enable distinguishing between areas with true floating kelp absence and areas that were not surveyed. If any floating kelp presence polygons fell within a clipped container from a given data source for a given year, a 1 was returned for presence for the corresponding segment otherwise a 0 was returned to indicate absence (Fig. 3a).\u0026nbsp;For one data source, the Northwest Straits Commission MRC kayak polygons (Table S1), survey boundaries shifted over time and were not available for some years, so containers could not be clipped to survey boundaries and data was treated as presence only, rather than presence and absence.\u003c/p\u003e\n\u003cp\u003eThe linear floating kelp datasets were summarized with different methods. To translate the values from the ShoreZone linear data, which follows an Ordinary High Water contour, to the appropriate line segments, the ShoreZone floating kelp linear features were buffered by 10 m. This accounted for the few instances where ShoreZone linear features fell shallower than the containers. Any overlaps between the buffered lines were removed along centerlines, then the area for each category of kelp presence (\u0026ldquo;absent\u0026rdquo;, \u0026ldquo;patchy\u0026rdquo;, \u0026ldquo;continuous\u0026rdquo;) was calculated within each container; if any \u0026ldquo;patchy\u0026rdquo; or \u0026ldquo;continuous\u0026rdquo; kelp fell within the container, a 1 was returned for presence for the corresponding segment, otherwise a 0 was returned to indicate absence. \u0026nbsp;The video capture date for each ShoreZone line, ranging between 1995 and 2000, was used to associate the survey year with each floating kelp observation. The methodology used to generate the original Washington ShoreZone dataset is described in Berry et al. (2001)\u003c/p\u003e\n\u003cp\u003eThe extensive historical analysis in South Puget Sound (Berry et al. 2021), the 2017 and 2019 surveys (Berry et al. 2019), and the 1984 imagery analysis project (McKenna et al. 2025a) all mapped floating kelp along the same segments used in this study and so those datasets were compiled directly with the segment-level floating kelp presence summaries derived above.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCalculating Coverage Category\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe coverage category metric was developed to provide more granularity to segment-level floating kelp presence, which makes no distinction between a single individual kelp and a large kelp bed. To calculate coverage category of floating kelp along each segment, polygons were created by splitting the segments at 0.25-km intervals. These subdivided segments were buffered by 1 km and overlaps between the resulting buffer polygons were removed along centerlines, resulting in coarsely subdivided containers. Summarize Within was again used to evaluate kelp presence in each subdivision from each data source for each survey year. \u0026nbsp;If floating kelp was present, a 1 was assigned to the subdivision, otherwise a 0 was assigned for absence. To account for variability in the number of subdivisions and resulting from parent segments longer or shorter than 1-km, each floating kelp presence value was weighted by its associated subdivision length as a percentage of the total parent line segment length and summed per line segment to calculate a weighted proportional presence \u003cem\u003eP\u003c/em\u003e for each line segment (Equation 1, Fig. 3b). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"624\" height=\"57\" 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eNXFi6RhkJnOR53eYkZVD9R4/qqK1dc0cGl6C7YJn12/VuzkbV+7Nlfz9l6B/0NH3fqpXCm4vFgK0zkS0WpG02nDzSEAgXwWuNFNxwYiJD29mtZ4Tpg8Jv+MFXsvmB8z/fX4JTr82iWS81WZjNdNkzvkVdTqn9wJ+X53rOBs4HmSzLHQFAFqcn44CwIQgEDWC/BvO6Xd324VYi1Opu3vnRh5PHidaXU1619O5QC9nI862maTinhr80L2R01m+PH81enBbUPZTWktF3zq58HLqa229lFyma26yh8uv9kSFoUWMVLhpLbre2ipVTBycIrV02Q+CpwDAQhAYIEEbnQfpoGIRN8sXkZ3JTPRHb8mI7Eqva3P07GzAeU5qxGgauKMm0IAAhBYGAH+bedhXkCNVtaTo5DzT+UyshOLVEqF31aDUjzp5Q2FXDyJV1dTXMFKVCV49peupPbRc2S2LsyW4sKN71D70Uz0R433QE19+qdWXyvoSTtPnjU2d4X2rVRfuZf6+6Nka+rWXXAqS1wwe4wtrTGTP3Zt51V9HBCAAAQgkO0CY36uGcBJQCWWZfTAXMmk83yRQgunHwn/SeflqncP3KHVnIiKFdR5ouJyCEAAAlkjwFtEB+XBrLBO5p+6n3pK6u3eIXzb4aMIr6y2j3FlXy7eo6aCr1wluKV1ItJI8aXHu7bReIrXpOt0JR+177Q0Mq3P6LVAkHxHrJoVikq9Cm88uP1C/9QzO8jFeakVbdqtnPIvHsLGGp8o8SqtsztMn4ZEdf1J0zVhqdxneIyu9zWHi0SD0YtV2VTkcC4EIACBWQVi+aevFUSjX6diLnak4QIq/0K2hCoMBvpoJEhXPqwJmRc9ZEKAio8TAhCAQI4I9J7ojHU48XFxdx9JLfzHlhb+/5WV5Gzrpv0b7XTZQKL/UG4WRpprGuU+o1vaKiQfb/WNRiupnlcpm3mVUlWbEy2+l2CArsmzN61/qry9d2XtsNB0Kkzfcwg5W9BKDadF2RKm/DoGBwQgAAEI5JgAAtQcm1C8DgQgkJ8CvEU0wvmnBjn/tH2Mt9Ne/sIq6fgQHdo2dA8O5wZKDp9T6A5P9UvNVT23u0xaJ3o4OCVeJezj7cbZFfAFe94juYBvlAskFRpu0Nl1ayVHZwmdun6OHLw1mlcHZ6y27OYgVuQc1UYOYj8NCRlf+eRfgMi1GsU0fjdGj8eD/NQ0guJWEIAABPQmgABVbzOG8UIAAhCYUSBAI7H9vcQFfIkL+M7ZZHKyoJK8Ynd/PaWcMz7znIN8cgDo7CZfSBCXZll7ncn+qb4aEn0T/LbHycLpqHMdZ052yi+l9E0NCepWhHnrttEdTxK9+CotTWH2OZjEf0ek4IVTIQABCEAgsQD+xZLYCGdAAAIQyH6B3n+NBWCVyQWcXu+7In2/lVo5M/TQtm1JvZ9eiyQ1LH5fKmolruLrpO49dnJwIJdifaikfNSeNFUg6WGlmNRG6+9pveigzsHDdCa4ga5Y+TcIsxxDZg/nBRNd3J7cHKodY7ZdF+B2SvJqOA4IQAACEEi/AP83gqnceidk+Ph8gf9GhB7g6nZcu0ibg+tnnOd/oEdKCunBlivczqYHfVC1kcZdIQABCGRWIDgmZzBGScsWMnoskuRuWCytLBrk4FSuJLyHVxln3y6bzhkb5iJMVVyEKakj2ENHYvt7ecXUQAZaJa5uitz1+QaFkQDxduSk7pKfJ5VaafmPjho9pX5sC87PLwBvDQEIaCRgLeZcf+E8jQRuTLZ/0eJRwcAwVwuOUEVx4UQ7G1Tx1cIZ94QABCCQUYGp/FO5gO8yaub9vf54f5kZRvIUryhalGJBnI/ZI1HVbc7H9Ode4aTYymSR0kO0sn0/ua4IoqdLy/e0EtfuIepPcfr5t8fcHYh36tZRDVdY9vBcuOueJdrrI9/JXt6S7Liv2nA89zTC76bH3qgpCuF0CEAAAhDIsMCymiep8oUL9JH/BkV4CVVu/5L8MUIBXnmVq/8mOuQe7dFoBT2+ysLnL6I7byJATWSGn0MAAhDIeoFg7z/H+p/ySK+N3SCf+fKc7VPevfWYGGgnDlJLhTWrDNwTlS+c6Jea9S+bwgDPPGchFweL0cp22r/BQtYs29obf5Vg4BNl8mwlPF7+i0qfUHsdOWkvdfpO0uk9dqno939Ja09Ead/bG8hqUFaBRem0UymQlHpLmxQQcSoEIAABCOSnwLIaerzyBTp34BQd/9Oe0OcF9Sbun/7ZXBjB3hO0T4pQmM7TsbOBhCuvbnd56HnTogKp7HFaZRGJ41Pi+BR9UPPzi8NbQwACuSLgLv+fiODYa4hOJOQNuKy8WjgmtRQTr8T5Z6z8Kr+7/BtLstXnbIEkZYXRY+CtvU3U3beRg1Ptt/bK/VbLSiKSoZ+EQd6bO8h7cz0JPrSp/NMKepJb35C8nZd/WRBri9MkXXP5hFqumlTBxZ0OdNjjwakyr7HANlYgiRsL5eLvGGbUY7Pw7ke8XDlYIhtvCVu6tEhddahc+YcA3gMCEICABgJyHqp769Ohrf37Cl5/yzVrsMmpNKFHH9xa8NGf75Lc3i5+nN/6oPzH0EWqoGdOfEgPfL7IxBHuPQFu8DfbaW/4Lv30hZ/TvwwbTaae2M9RJEmDCcUtIQABCGRKwHthqdDa2nrv48YP0WPjs4+Af2PJLVcMgm2Xg6xHj84ZyGbqPdL5nMmWMrz91dmdubxTNe8gB7VU1UKtVUTjXdu5ZNXUcfCWWaziueUf8XGRurZdvOcRSuVf25MkF2zOhoOLaIWNNT5RYvf44eyW29+IGW9/kw0eGAMEIAAB3QtUb6Ud33qHXC+00xFlFdV93yqq9+At03deeoW+M9fLXnnjvp/Kq6e/ML1aEH7oDXJVfym2etoTOw0Bqu6/HLwABCAAgRQFbozRMMcQpUnkq6Z45wU/PZ532skFFxaipYy1xMYG54mGAzS6JypZBfesPUzngxULwgWhYpedrnDealcW5BDL7WqudzvDyyeCVGf3dY2C0wD5uaiUvGmg1FrIz5iPJK6FAAQgAIHZBJRV1A+Oh/ymNQVPPFpMlz5sDn3P7Ta9mWCrbyJR/nd16K1HHyzovPNjOvLBBrqzyHjPPRGgJhLEzyEAAQjkmMCN7sNclMdGT+RgA9Tgb37GeaccnHLe6Si3lMl03mmhhbc0GThAHeQCEfzdaFaANxgguW5ztuWfTrVt5a3HXPAixBtx/enObw6O8bvLh414hy9hh2+O/QMKrwMBCGSVgNd7wfTb0LVQMQeU3zAdo9eO7wstKn7edMf/5pz5qLO9REP54tDfLyop+PgbP6HjoT20dNGXeGfvvVt/EaBm1SeAwUAAAhDQXkDJP+USPNZlQTLsWiv9YXUfffe2QfeVfO9pKbOf+4dy3mmXV8uqvTPMlbWE1BTyTXXWgz3v0UDURo9ny/7eiRcIjg3H/iRXJP7RMW3avwS46jEvn3J4TlZuy4MF1FS/HpwPAQhAIDUBTkcx0XdeopGti0Ptr3+tILzlVuivit335ZQmuiungoRMr98s8B69Sqv+/GXTlTd+9dmVGS5CgJpIEj+HAAQgkGMC1WuepUqfjxziNW4zc4DbzBzl4HT2gkp6eP1YXm1NBlvKzKJSaKEV3Mm0n4YpEIzSEatBE75Y/mk72TlAm6irpMlzUr1pQGkXwEPLRPEiWzEVsjWXSMqbAlGpzgfOhwAEIJBOgYMXbpm+/N1XiC7tUHVbTgUxvfhdrpx06fCc1yNAVcWLiyAAAQjoV4D/BSF+n4vvfF9+haFDsbYmOj96n7OTjwOjhW8pE++FOsjNzYm4kG/aj3j+abTRQkUTfVPT/hAVN1Sq666Uq+t+k+rtFs223k6u0pZaafmPjmqzSqvi/XEJBCAAAQikRwABanoccRcIQAACEFggAd4yJImtmW0pM9urTm81MxwIUtWR9EaochGos+vWCj5uat62pVoJgNOe46l2HoM9dGRQLl4kb73l35Crvc8c18VbzMhtlTKySqvBO+CWEIAABCAwtwACVHwhEIAABCCgW4FY3mkr9zvNnpYy9rpnifb6aPDwGTpwziLVp6mSb9nNFkkUWoRIhZPar3dwcMp5w5nOsZ3rS+Hc0EE5N1TeemvQauttvIKvjVdpuQcqWqDq9u9dDBwCEIDAbAIIUPFtQAACEICALgXiLWX65b6bzh7yhQRxaZoCtljg6xL6G09TtMrBvWJTKLYUL5SU5kq+Q2aP2NIam6rxrm339EzNhgmMb7211dvpl8cM2vQ+nazgiwJJ2TDnGAMEIAABLQQQoGqhintCAAIQgIDmAve2lKlOb0uZMa4Uy2/AoW/qR6GD6is3UX+/toWSUh+YdlfE80/lrbep9CYtv9kSrvE5xVN393BLGsHIK8LSXKMM9nCLJHmVVssqwdox4c4QgAAEIJCEAALUJJBwCgQgAAEIZJeAknfqGOStvZXUrkFLmWDgEyU6tZVYyMWvnkohKTkPteGJhyVX/3nh8Jkgua6kNw81u2YiPhreeqvkn8q9SQ1Jbb2dLKqUQjXeeJVgZ90q+lSTJqvZqYtRQQACEMgnAQSo+TTbeFcIQAACOSAQaynTKsirdZXt+zkAFERPVwpbcBMYxLYOewS506bao9Dxj1S56Tz1cx7qV9OYh6p2PJpfx1tvlQ6otnqyW5LLP+VA3khVbmqhP9J7r25LOESe9/B6oyBGo41Ut4r7n3K94KwpEJVw9DgBAhCAAASSFUCAmqwUzoMABCAAgawQkFvKdMotZTjvdMB8hfNDuyLpGlisSm6R4OqPKP08Sy1cjtbBd/em+ITJbb6H6UxwA13RqB9qiqPS7HR5661SIElu/fLD+1u/KFt5qVuM/F9IfVuY3hPcSgjbezWbRNwYAhCAQJYIIEDNkonAMCAAAQhAILFAw+L3paJW4tVTPtfHkaOPpJbElyV9RktLeu6Wb9t859p6ywWnwiuXC2LjyaltuTyP4eWbBkWJC1w5u8Pydt2ERZV6T3QqvzTA9t6kP2ecCAEIQECXAghQdTltGDQEIACB/BOIVdaV804z9e424hRU1UfhxlfIuamWOnN8m+/U1tuH78s/dTfwttzlNWK/1EgvTtuWe/DWY8brbcRB6goxme26k8+o2EmbVwlUxNt7vamuaqueSVwIAQhAAAKZFECAmkltPAsCEIAABFQL9O5opv6MBaeqhzl5odc7JK5uikg+32EhV7f5up/iAHRljdgR4S3RNEiblnPkSBSOI0wuSM9QdVdZdbU9kVzP1IntvRVyC5vjGrWwmf+U4w4QgAAEIJAGAQSoaUDELSAAAQhAQHuBC9wHtLVV++dMf8J41/Z59Ru1b/01Ve510aadvXI/VfJkdviaP8377gXjV6paqLUq0aMuEi2dOie+ImrbkbhnqlLt95HlXBypiV7eYEFxpETU+DkEIAABnQsgQNX5BGL4EIAABCCQvQLeg7fEl5qiUq3vVaFtS7VkrneL3OszbUWdsvfNE4xsoupvibWQ808TnNu7gzZx0aqHd22md44LCXNVdWuCgUMAAhCAgCKAABUfAgQgAAEIQEBDAXtHDzn31ubsKqoauljV3wr6QYKeqZOrpxW7aP8GK3JP1WDjGghAAAI6E0CAqrMJw3AhAAEI5LuAu/ymJDp8QiNXf626LYh+f3avSMq5qFvaHpZ8rtewijrx8caq/jrJWniDhPXPhP9Q+yH97+0ZKvkqq6cVtHNkA10VDcb3vF4p379/vD8EIACBXBdAgJrrM4z3gwAEIJBjAkq7EXLSGq4Ke1vgl9O4mmtD2U3p6dph4Ql/H5mtHBCr2KJ78NYq8bSzRapdu1vw922U6t1u0aviPrkyldVrnuXcXB/VGoepqfsAfTpDcBrLU/WIFTtHaPAqB6/vITjNlfnHe0AAAhCYSwABKr4PCEAAAhDQlUC8WNLQ9m2ajruhbLG08zWXYG2NKqt9L/F2VO68qjoeVrb6irW0fJ0lJwsmpTIZ3gtmY1VLKym1lS52Ta+fpNwmvrW38+GdNIKtvanQ4lwIQAACuhdAgKr7KcQLQAACEIBAugXcvGq68lSZ8Ls+iba8+QgVNRvm/Qh5q++e0TbpWlGtECm5LhUXc8GkLN+ePO+XVnmD3ucs5Iq00cjHzZx3ajDyajO29qq0xGUQgAAE9CaAAFVvM4bxQgACEMhTgXjuaSQapcr2URowW0WPRgGed8gsVi0ZokPbhqhhsSQHR/Jm4nkfclXfvtFuad3OXuFrGy1SvSG/t/rOBKps7V3/a/H6xxvIIiA4nfdHhxtAAAIQ0JkAAlSdTRiGCwEIQCBfBXhbqCj1OJUCSSu4PUnzbSK/DjG8B4fEJUuIg19ttyjrkEYZstfLvVW/wj4ab+HWqw/GDQEIQCDXBRCg5voM4/0gAAEI5JBAcGyY3yZxgaSym1yQyEdCJDrXyzupW+ogh2DI64JFOfR54FUgAAEIQCAHBBCg5sAk4hUgAAEI5IuA0p7EVk+F/MJWebVtlhcfMntErsGT8LjIq3QXE56FEyAAAQhAAAIQyJQAAtRMSeM5EIAABCAwLwHOTZTWiQbBtstORccMmuWfzmuQuBgCEIAABCAAgXkJIECdFx8uhgAEIACBjAkEx+gaP6w0ifxTbPHN2KzgQRCAAAQgAIG0CiBATSsnbgYBCEAAAloJBHsO00DURo8n2t/LA0jXFl/uxyntXukR5FTWk71BCjksWr0e7gsBCEAAAhCAAAsgQMVnAAEIQAACuhCI5Z+2k73QMGf+aTpexu0u4+3EtYLQ0kKxOkuD5Kuxko9IsnGLGxe3uEEP03RI4x4QgAAEIACBewUQoOKLgAAEIACBrBeI559So5XzT49y/qk/ouWgvV5uBcPBaetMDxnvIv+4lk/HvSEAAQhAAAL5K4AANX/nHm8OAQhAQBcC8jbbs+ueFjp5e2/bllVktgrk985Wv1cXr4RBQgACEIAABCAwiwACVHwaEIAABCCQtQLl3M+U99kKUZuT2kc7yGwxiBycarp6mrUYGBgEIAABCEAgDwQQoObBJOMVIQABCOhV4AL3M21tjY1+/NA2ws5avc4kxg0BCEAAAhBITgABanJOOAsCEIAABCAAAQhAAAIQgAAENBZAgKoxMG4PAQhAAAIQgAAEIAABCEAAAskJIEBNzglnQQACEIAABCAAAQhAAAIQgIDGAv8PsqoR+mpCi38AAAAASUVORK5CYII=\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere:\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cem\u003eP\u003c/em\u003e = the weighted proportional kelp presence for line segment \u003cem\u003ei\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003ek\u003cem\u003e\u003csub\u003ej\u003c/sub\u003e\u003c/em\u003e = the presence of kelp (0 or 1) for the \u003cem\u003ej\u003c/em\u003e\u003csup\u003erh\u003c/sup\u003e subdivision of line segment \u003cem\u003ei\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003el\u003csub\u003ej\u003c/sub\u003e\u0026nbsp;\u003c/em\u003e= the length of \u003cem\u003ej\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003esubdivision of line segment \u003cem\u003ei\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eL\u003cem\u003e\u003csub\u003ei\u003c/sub\u003e =\u0026nbsp;\u003c/em\u003ethe length of the line segment \u003cem\u003ei\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe resulting value for \u003cem\u003eP\u003c/em\u003e was then assigned to a coverage category class (Equation 2).\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"319\" height=\"94\" 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\" alt=\"image\"\u003e\u0026nbsp;(2)\u003c/p\u003e\n\u003cp\u003eCoverage category was not calculated for data from the South Puget Sound historical analysis (Berry et al. 2021) as results were already summarized to the line segments.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSynthesizing Results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll segment-level floating kelp presence and coverage category results were compiled into a single spatial dataset with all available floating kelp records from each data source for each of the 3,418 segments. These results were evaluated at the statewide and sub-basin scale. To describe the most recent distribution of floating kelp throughout the State, records were filtered to the most recent year of data for each segment to create a new spatial layer. The most recent year varied between segments as there has been no recent synoptic floating kelp survey. When multiple records were available for the most recent year, as occurs at some sites with co-located monitoring, the source that mapped more kelp, based on presence and coverage category, was selected for inclusion in the most recent view. The percentage of segments with floating kelp present in the most recent year was calculated at the statewide level and the proportion of most recent segment-level coverage category was calculated for each sub-basin.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo describe the spatial distribution of floating kelp across all data records, the percentage of segments where floating kelp was present at least once was calculated for each sub-basin. To evaluate temporal span of floating kelp data records, the average number of unique years of survey data for segments with floating kelp present at least once was calculated for each sub-basin as well as the number of records available from each year across the entire time span. Hood Canal (HDC) and the South Coast (SCO) had no records of floating kelp presence from any of the data sources included here and so were excluded from all sub-basin level analyses.\u003c/p\u003e\n\u003cp\u003eThe final dataset was made available to the public via an interactive data viewing platform (Fig. S2). This platform enables access to the synthesized linear extent data and serves as a visual compendium of the source floating kelp spatial datasets so that users can identify and retrieve source data that may be appropriate for their research or management questions. The datasets are also distributed with a user guide (McKenna et al. 2025b).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOver 34,000 segment-level observations of floating kelp from 12 data sources were synthesized to 1-km line segments, including Berry et al. 2021 which itself synthesizes 48 data sources. According to the most recent floating kelp survey data, 28% of segments across the State had floating kelp present (Fig. 4, Fig. S4). For segments with floating kelp present in the most recent year, 95% were surveyed between 2022-2024. The oldest records that constitute the most recent survey for some segments are absence records from 1995-2000 ShoreZone surveys. These older absence records are primarily in areas where floating kelp does not occur and may never have occurred and so have not been resurveyed recently, such as Hood Canal (HDC) and the South Coast (SCO).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFloating kelp distribution in Washington State varied greatly between the marine sub-basins (Fig. 1) in the most recent data. The Western Strait (WST) had the highest floating kelp presence (96%) and proportional floating kelp coverage within segments in the most recent data, where over 75% of segments had a coverage category of 4, indicating generally continuous floating kelp coverage within that subbasin (Fig. 5a). The Eastern Strait (EST), North Coast (NCO), and San Juan Islands (SJI) all had similar floating kelp presence in the most recent data (52-58%), but EST had proportionally higher within-segment coverage than NCO or SJI. North Puget Sound (NPS), Admiralty Inlet (ADM), Saratoga-Whidbey Basin (SWH), Central Puget Sound (CPS), and South Puget Sound (SPS) all had floating kelp present in less than 25% of segments in the most recent data and floating kelp coverage category was spread more evenly between 1 and 4. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFloating kelp presence followed a similar pattern of variation between sub-basins when examining all presence records across time. In WST, floating kelp was observed in every segment (100%) at least once in the data records. In EST, NCO, and SJI, 75% or more of segments had floating kelp present at least once. There is markedly less floating kelp presence reported across all records for the NPS, ADM, SWH, CPS, and CPS, where 33% or fewer segments had floating kelp present at least once in the available data records.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability across all records also varied between sub-basins and followed similar patterns to floating kelp presence. WST had an average of 35 years of data available (\u0026plusmn; 0.0 SD, \u003cem\u003en\u003c/em\u003e = 100) for segments with floating kelp present once or more, and EST had an average of 26 years of data available (\u0026plusmn; 12.7 SD, \u003cem\u003en\u003c/em\u003e = 180) (Fig. 5b). However, SJI had an average of only 7 years of data available (\u0026plusmn; 2.6 SD, \u003cem\u003en\u003c/em\u003e = 552). For three of the four subbasins with high proportional floating kelp presence, NCO, WST, and EST, annual floating kelp survey data exists for most or all segments since 1989 (Fig. S3). For the San Juans Islands, the historical record was more sparse, with the earliest integrated records around 2000 and several sub-basin-wide surveys conducted between 2004 and 2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability was much lower for the remaining sub-basins, which also had less kelp presence overall. NPS had an average of 4 years of data available (\u0026plusmn; 4.9 SD, \u003cem\u003en\u0026nbsp;\u003c/em\u003e= 93) for segments where floating kelp was present once or more, ADM had an average of 4 years of data available (\u0026plusmn; 5.7 SD, \u003cem\u003en\u0026nbsp;\u003c/em\u003e= 67), SWH had an average of only 3 years of data available (\u0026plusmn; 2.3 SD, \u003cem\u003en\u0026nbsp;\u003c/em\u003e= 58), CPS had an average of 3 (\u0026plusmn; 2.1 SD, \u003cem\u003en\u0026nbsp;\u003c/em\u003e= 98), and SPS had an average of 9 years of data available (\u0026plusmn; 2.5 SD, \u003cem\u003en\u0026nbsp;\u003c/em\u003e= 119) (Fig. 5b). NPS, ADM, SWH, and CPS have all had site-targeted monitoring programs in the last 5 to 15 years, but generally lack synoptic or analyzed historical data prior to these programs other than ShoreZone (Fig. S3). SPS has current site-targeted monitoring programs and the oldest available data records incorporated here (1873) given the historical analysis by Berry et al. (2021).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eComprehensive mapping of species distribution is foundational for habitat inventory, conservation, restoration, and management (Schweiger et al. 2002; Brooks et al. 2004). The broad spatial and temporal perspective it affords is vital for understanding dynamics of kelp forests ecosystems (Reed et al. 2015), particularly in regions like Washington State where floating kelp persists in some areas but is declining in others (Pfister et al. 2018; Berry et al. 2021; Berry et al. 2023). In Washington State, no statewide floating kelp survey has been conducted since the late 1990s (Berry et al. 2001). However, a multitude of different monitoring programs provide relatively recent floating kelp distribution data for nearly all areas of the State where floating kelp is or has been present. Up-scaling and synthesizing these disparate datasets to a common linear spatial unit allows for comprehensive mapping, providing a cohesive dataset that is useful for research and provides essential information for natural resource management of nearshore marine systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe methods and approach developed here also have potential applications for mapping floating kelp in other geographies as well as other species and habitats where data availability is patchy and/or comprised of multiple formats. The key components of this approach are (1) selecting synthesis units, in this case 1-km line segments, that are reasonable for the species and spatial scales of the study system in question, (2) defining the area that each synthesis unit represents, and (3) identifying and applying a common analysis workflow that functions across all datasets, in this case an ArcGIS Pro-based Summarize Within function on annualized data. Many technical considerations and decision-making factored into this synthesis; we describe them in the Supplementary Information to facilitate applying a similar approach elsewhere.\u003c/p\u003e\n\u003cp\u003eBy applying this methodology, we created a comprehensive floating kelp spatial dataset for Washington State, a jurisdiction with active floating kelp monitoring (Berry et al. 2023), restoration (McKenna et al. 2022), and conservation (Native Kelp Forest and Eelgrass Meadow Health and Conservation Plan 2022). The Western Strait of Juan de Fuca stood out as having the highest proportion of floating kelp-containing segments amongst the sub-basins, both historically and in the most recent available data, and had the highest coverage category values in the most recent available data, suggesting relatively continuous kelp presence along the shorelines of the sub-basin. Kelp forests in the Western Strait have been largely stable over the past century (Pfister et al. 2018) and have remained stable over recent years despite interannual variability (Berry et al. 2023, Tolimieri et al. 2023; Claar et al. \u003cem\u003ein review\u003c/em\u003e). The Eastern Strait of Juan de Fuca, North Coast, and San Juan Islands also had high proportions of floating kelp-containing segments, although more variable floating kelp coverage category values across segments, indicating patchier floating kelp presence in these sub-basins. Kelp forests along the Eastern Strait of Juan de Fuca and the North Coast have also been largely stable over the past century (Pfister et al. 2018), although recent declines have been detected in some areas of the Eastern Strait (Berry et al. 2023, Rubin et al. 2023). While there is a large spatial and temporal data record for the North Coast and Strait of Juan de Fuca, the San Juan Islands stand out as a region with high floating kelp presence but relatively few years of survey data available. The rest of Washington\u0026rsquo;s marine sub-basins have far lower proportions recent and historical kelp presence, and many sub-basins have very few years of data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese results point to the need for new historical analyses and continued monitoring to generate more floating kelp data in some regions of the state, including the San Juan Islands. In the San Juan Islands, the Samish Indian Nation has documented floating kelp losses by comparing Traditional Ecological Knowledge (TEK) with modern aerial imagery (Samish Indian Nation, \u003cem\u003eunpublished\u003c/em\u003e). Traditional Ecology Knowledge data is not currently incorporated into the linear extent dataset, but the framework developed here has the capacity to integrate this type of observational data in future iterations. The currently limited data record makes reporting changes and trends challenging in this sub-basin, but integration of TEK, historical records, and continued annual monitoring data may help paint a fuller picture of how floating kelp has changed over time.\u003c/p\u003e\n\u003cp\u003eNew analysis of historical data would also be valuable in North Puget Sound, Admiralty Inlet, Saratoga-Whidbey Basin, and Central Puget Sound, areas with some floating kelp presence but only a handful of years of survey data. Once historical data are digitized into a modern GIS format, they can be readily integrated into the floating kelp linear extent dataset using the framework described in this manuscript. For example, we were able to include historical records of floating kelp presence along the Seattle waterfront in 1984 analyzed in McKenna et al. (2025a), and records spanning approximately 145 years by integrating spatial data published in Berry et al. (2021). Continued future monitoring whilst extending the data record backwards with new historical analyses will enhance understanding of floating kelp distribution change over time. This is critical to improving the ability to understand the drivers of floating kelp loss and resilience across the entire State.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile the linear extent method has yielded insights into floating kelp distribution, there are inherent limitations to this approach and the resulting dataset. This synthesis uses 1-km line segments as the common unit for analysis and generalizes floating kelp observations to an annual level. This requires upscaling and generalizing many of the source datasets both spatially and temporally, resulting in unavoidable loss of information resolution in the process of creating compatibility. In addition, not all line segments are 1 km in length exactly due to the complexity of Washington State\u0026rsquo;s coastline, and the area of nearshore habitat that each segment represents is similarly unequal. Finally, this synthesis method necessarily treats the inputs as true and accurate observations of floating kelp presence without addressing any underlying uncertainties in the source data. \u0026nbsp;The use of this synthesized dataset in further analyses should therefore take these considerations into account. For example, filtering or weighting segments by length may facilitate more balanced units of floating kelp distribution for use in further analyses. When modeling floating kelp presence with other environmental variables, care should be taken to scale other datasets appropriately to match a resolution that incorporates the disparate datasets most effectively and comprehensively (Gotway and Young 2002). If a research or management question targets a more localized scale, using this dataset to identify and access the underlying higher-resolution floating kelp observations may be the most appropriate path. Therefore, care was taken in the construction of the dataset and web viewer so that users can readily find the source data driving the kelp presence, year, and coverage category for each line segment.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, the resulting dataset has wide-ranging applications beyond describing floating kelp distribution. The most recent floating kelp distribution is currently being integrated into a Puget Sound-wide analysis of floating kelp and eelgrass stressors (C. Magel, \u003cem\u003epers. comm.\u003c/em\u003e). It will also be integrated into the determination of sub-basin status in the Floating Kelp Indicator (Berry et al. 2023), a component of the Puget Sound Partnership\u0026rsquo;s Vital Signs program (Puget Sound Partnership 2022). Because it uses mapping units derived from another marine vegetation monitoring program, it is primed to compare the distribution of floating kelp with other marine vegetation like eelgrass and understory kelp. Linear extent data has been used to evaluate the relationship between floating kelp loss and resilience with environmental variables in South Puget Sound (Berry et al. 2021), British Columbia (Starko et al. 2024), and Alaska (Hollarsmith et al. 2024). This dataset could similarly support a statewide analysis of change in the distribution of floating kelp and potential linkages with environmental drivers. It may also have applications for predictive distribution modeling under future climate scenarios, enabling managers to pre-emptively respond to areas at high risk to climate-related stressors (Reiss et al. 2015; Martinez et al. 2018; Khangaonkar et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy providing a new statewide look at floating kelp distribution, the Washington State Floating Kelp Linear Extent dataset will enable better statewide management, conservation, and restoration of kelp forests. \u0026nbsp;It helps identify spatial and temporal data gaps and can be integrated into analyses of current and future kelp stressors and other models. The approach outlined here can be applied in other areas or for other species where spatial data is available but in disparate formats.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Washington State Legislature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is available as a file geodatabase download through the Nearshore Habitat Program webpage [https://dnr.wa.gov/aquatics/aquatic-science/nearshore-habitat-program] and as hosted feature layers through ArcGIS Online [https://experience.arcgis.com/experience/e03ea8b2a6574e4094230aff5e862626/]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCode is available at https://github.com/WA-Nearshore/wa-kelp-linear-extent\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDC and GM designed the study questions. GM developed the methodology, authored all scripts, conducted all geoprocessing and analysis, and wrote the initial manuscript draft. DC provided substantial revisions to the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Samish Indian Nation and the Northwest Straits Commission provided key datasets for this project. Helen Berry laid the conceptual foundation for the project. Tyler Cowdrey provided substantial comments on the initial project design and manuscript draft, and Bart Christiaen, Pete Dowty, Lisa Ferrier, Jeff Gaeckle, and Julia Ledbetter provided comments on the manuscript draft.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBell TW, Allen JG, Cavanaugh KC, Siegel DA (2020) Three decades of variability in California\u0026rsquo;s giant kelp forests from the Landsat satellites. Remote Sens Environ 238:110811.\u003c/li\u003e\n\u003cli\u003eBerry HD, Harper JR, Mumford TF, Bookheim BE, Sewell AT, Tamayo, LJ (2001) The Washington State ShoreZone Inventory User\u0026apos;s Manual. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1-23\u003c/li\u003e\n\u003cli\u003eBerry HD, Calloway M, Ledbetter J (2019) Bull Kelp Monitoring in South Puget Sound in 2017 and 2018. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1-60\u003c/li\u003e\n\u003cli\u003eBerry HD, Mumford TF, Christiaen B, Dowty P, Calloway M, Ferrier L, Grossman EE, VanArendonk NR (2021) Long-term changes in kelp forests in an inner basin of the Salish Sea. PLOS ONE 16:e0229703.\u003c/li\u003e\n\u003cli\u003eBerry HD, Raymond WW, Claar DC, Dowty P, Spaulding E, Christiansen B, Ferrier L, Ledbetter J, Naar N, Woodard T, Palmer-McGee C, Cowdrey T, Oster D, Shull S, Mumford T, Dethier M (2023) Floating Kelp Monitoring in Washington State: Statewide Summary Report. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1-22.\u003c/li\u003e\n\u003cli\u003eBishop E (2014) A kayak-based survey protocol for Bull Kelp in Puget Sound \u0026ndash; updated 2023. Northwest Straits Commission, Mount Vernon, Washington. pp 1-10\u003c/li\u003e\n\u003cli\u003eBrooks RP, Wardrop DH, Bishop JA (2004) Assessing Wetland Condition on a Watershed Basis in the Mid-Atlantic Region Using Synoptic Land-Cover Maps. 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Coastal \u0026amp; Oceans Resources Inc., Anchorage, AK. pp 1-61\u003c/li\u003e\n\u003cli\u003eHarper JR, Morris MC, Daley S (2013) ShoreZone Coastal Habitat Mapping Protocol for Oregon. Coastal and Ocean Resources and Archipelago Marine Resources, Victoria, BC. pp 1-114\u003c/li\u003e\n\u003cli\u003eHeidorn PB (2008) Shedding Light on the Dark Data in the Long Tail of Science. Libr Trends 57:280\u0026ndash;299.\u003c/li\u003e\n\u003cli\u003eHollarsmith JA, Cornett JC, Evenson E, Tugaw A (2024) A century of canopy kelp persistence and recovery in the Gulf of Alaska. Ann Bot 133:105\u0026ndash;116.\u003c/li\u003e\n\u003cli\u003eJayathilake D, Costello MJ (2020) A modelled global distribution of the kelp biome. Biol Conserv 252:108815.\u003c/li\u003e\n\u003cli\u003eJepsen DJ, Norberg DJ (2017) Contested boundaries: A new Pacific Northwest history. 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University of Washington Press, Seattle.\u003c/li\u003e\n\u003cli\u003eLedbetter J, Berry HD (2025) Long-term kayak monitoring of floating kelp in Puget Sound: Results through field year 2024. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1 - 129\u003c/li\u003e\n\u003cli\u003eMahdavi S, Amani M, Parsian S, MacDonald C, Teasdale M, So J, Zhang F, Gullage M (2024) A Combination of Remote Sensing Datasets for Coastal Marine Habitat Mapping Using Random Forest Algorithm in Pistolet Bay, Canada. Remote Sens 16:2654\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez B, Radford B, Thomsen MS, Connell SD, Carre\u0026ntilde;o F, Bradshaw CJA., Fordham DA, Russell BD, Gurgel CFD, Wernberg T (2018) Distribution models predict large contractions of habitat‐forming seaweeds in response to ocean warming. Divers Distrib 24:1350\u0026ndash;1366.\u003c/li\u003e\n\u003cli\u003eMcHenry J, Okamoto DK, Filbee-Dexter K, Krumhansl KA, MacGregor KA, Hessing-Lewis M, Timmer B, Archambault P, Attridge CM, Cottier D, Costa M (2025) A blueprint for national assessments of the blue carbon capacity of kelp forests applied to Canada\u0026rsquo;s coastline. Npj Ocean Sustain 4:30.\u003c/li\u003e\n\u003cli\u003eMcPherson ML, Finger DJ, Houskeeper HF, Bell TW, Carr MH, Rogers-Bennett L, Kudela RM. (2021) Large-scale shift in the structure of a kelp forest ecosystem co-occurs with an epizootic and marine heatwave. Commun Biol 4:298.\u003c/li\u003e\n\u003cli\u003eMcKenna G, B Allen, H Hayford, D Tonnes (2022) Kelp forest restoration in Puget Sound: Outplant techniques and lessons learned. Salish Sea Ecosystem Conference, online.\u003c/li\u003e\n\u003cli\u003eMcKenna G, Berry H, Claar D, Cowdrey T (2025a) Mapping floating kelp presence along Seattle shorelines in 1984 using historical aerial imagery. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1-36\u003c/li\u003e\n\u003cli\u003eMcKenna G, Cowdrey T, Claar D (2025b) Washington Floating Kelp Linear Extent Data User Guide. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington. pp 1-18\u003c/li\u003e\n\u003cli\u003eMichener WK, Jones MB (2012) Ecoinformatics: supporting ecology as a data-intensive science. Trends Ecol Evol 27:85\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eMumford TJ (2007) Kelp and Eelgrass in Puget Sound. Washington Department of Fish and Wildlife, Olympia, Washington. pp 1-34.\u003c/li\u003e\n\u003cli\u003eNaar N (2020) Appendix B: The Cultural Importance of Kelp for Pacific Northwest Tribes. Puget Sound Kelp Conservation and Recovery Plan. Seattle, Washington. pp B1 \u0026ndash; B12\u003c/li\u003e\n\u003cli\u003eNathan R, Monk CT, Arlinghaus R, Adam T, Al\u0026oacute;s J, Assaf M, Baktoft H, Beardsworth CE, Bertram MG, Bijleveld AI, Brodin T (2022) Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375: eabg1780.\u003c/li\u003e\n\u003cli\u003eNearshore Habitat Program (2022) WA DNR COSTR/AQRES Aerial Imagery (v2021.0) [Data set]. Nearshore Habitat Program, Washington State Department of Natural Resources, Olympia, Washington.\u003c/li\u003e\n\u003cli\u003ePacifici K, Reich BJ, Miller DA, Gardner B, Stauffer G, Singh S, McKerrow A, Collazo JA. (2017). Integrating multiple data sources in species distribution modeling: a framework for data fusion. Ecology 98:840\u0026ndash;850\u003c/li\u003e\n\u003cli\u003ePfister CA, Berry HD, Mumford T (2018) The dynamics of Kelp Forests in the Northeast Pacific Ocean and the relationship with environmental drivers. J Ecol 106: 1520\u0026ndash;1533.\u003c/li\u003e\n\u003cli\u003eReed DC, Rassweiler AR, Miller RJ, Page HM, Holbrook SJ (2015) The value of a broad temporal and spatial perspective in understanding dynamics of kelp forest ecosystems. Mar Freshwater Res 67:14\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eReiss H, Birchenough S, Borja A, Buhl-Mortensen L, Craeymeersch J, Dannheim J, Darr A, Galparsoro I, Gogina M, Neumann H, Populus J (2015) Benthos distribution modelling and its relevance for marine ecosystem management. ICES J Mar Sci 72:297\u0026ndash;315.\u003c/li\u003e\n\u003cli\u003eReshitnyk L, Saccomanno V, Bell T, Cavanaugh KC (2023) Mapping Canopy-Forming Kelps in the Northeast Pacific: A guidebook for Decision-Makers and Practitioners. Hakai Institute. 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Academic Press, Cambridge, pp. 57\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eWernberg T, Thomsen MS, Burrows MT, Filbee-Dexter K., Hobday AJ, Holbrook NJ, Montie S, Moore PJ, Oliver ECJ, Sen Gupta A, Smale DA, Smith K (2025). Marine heatwaves as hot spots of climate change and impacts on biodiversity and ecosystem services. Nat Rev Biodivers 1:461\u0026ndash;479.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"","identity":"journal-of-applied-phycology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10811","submissionUrl":"https://submission.nature.com/new-submission/10811/3","title":"Journal of Applied Phycology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Floating kelp, Nereocystis luetkeana, Macrocystis pyrifera, mapping, spatial analysis","lastPublishedDoi":"10.21203/rs.3.rs-7482762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7482762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the current distribution of floating kelp forests is critical for tracking changes to these ecologically, economically, and culturally important ecosystems. It also informs management efforts in response to threats from urbanization, climate change, and other environmental stressors. However, current floating kelp spatial datasets represent a diverse patchwork of surveys from varied time periods and with varied spatial resolution, making comparison and change analysis difficult. Here, we present a recent effort to integrate multiple datasets to comprehensively map kelp canopies in Washington State, USA. By synthesizing remote sensing data, in-situ boat-based surveys, and historical records to 1-km coastal line segments, we created a novel spatial dataset that describes the statewide distribution of floating kelp. Analysis of the most recent survey data shows that the proportion of floating kelp-containing shorelines is highest in the Western Strait of Juan de Fuca, followed by the North Coast, Eastern Strait of Juan de Fuca, and San Juan Islands. While a long time series of data exists in most of these areas, the San Juans has relatively few years of data. Proportional kelp presence is much lower throughout the rest of the State, including Puget Sound, and temporal data availability for these areas is similarly sparse. The dataset produced through this effort has enabled new approaches for statewide analyses and status reporting for floating kelp forests, and the synthesis methodology developed is deployable to other regions and species.\u003c/p\u003e","manuscriptTitle":"Synthesizing disparate data for a comprehensive view of floating kelp distribution in Washington State, USA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 09:10:51","doi":"10.21203/rs.3.rs-7482762/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-13T10:24:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T01:11:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T15:10:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Applied Phycology","date":"2025-08-28T18:23:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"","identity":"journal-of-applied-phycology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10811","submissionUrl":"https://submission.nature.com/new-submission/10811/3","title":"Journal of Applied Phycology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4a86a6ac-2ebe-4cb0-a1c9-c32c7aa9bc7f","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T01:38:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 09:10:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7482762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7482762","identity":"rs-7482762","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}