Hotspots of Seabird Abundance in the California Current ... change, fisheries, coastal development,...

Hotspots of Seabird Abundance in the California Current: Implications for Important Bird Areas

William J. Sydeman, Marcel Losekoot, Jarrod A. Santora, Sarah Ann Thompson Farallon Institute for Advanced Ecosystem Research

101 H. Street, Suite Q, Petaluma, CA 94952 www.faralloninstitute.org

Trisha Distler and Anna Weinstein Audubon California

4225 Hollis St. Emeryville, CA 94608 http://ca.audubon.org/

Melanie A. Smith and Nathan Walker

Alaska Audubon 441 West 5th Street, Suite 300

Anchorage, AK 99501 http://ak.audubon.org/

Ken H. Morgan

Canadian Wildlife Service c/o Institute for Ocean Sciences

Department of Fisheries and Oceans, Canada Sidney, British Columbia

www.pac.dfo-mpo.gc.ca/science/facilities-installations/ios-ism/index-eng.htm

Abstract Climate change, fisheries, coastal development, and other human uses of the marine environment threaten marine biodiversity, including seabirds. The highly productive but variable California Current Ecosystem (CCE) supports millions of resident and migratory seabirds whose distributions are influenced by static and dynamic physical and biological conditions. To aid in developing coastal and marine spatial planning and ecosystem-based management for the CCE, we identified ‘hotspots’ of seabird relative abundance in this system. We define a ‘hotspot’ as an area of consistently elevated abundance for a species. We developed and applied a kernel density smoothing procedure to data on 23 species of seabirds based on long-term shipboard observations from the California Cooperative Oceanic Fisheries Investigations (CalCOFI), NMFS Juvenile Rockfish Survey (NMFS-JRS), Canadian Line P (Line P), and other data sources contained within the North Pacific Pelagic Seabird Database (NPPSD v.2). We identified

‘hotspots’ of abundance for all species, but found no differences in the size of ‘hotspots’ by trophic level (comparing planktivorous, piscivorous, or squid-eating seabirds), broad taxonomic categories (comparing orders and families), or eco-region, as definied by the U.S. GLOBEC program. Mean species density per ‘hotspot’, however, varied significantly with each of these factors. ‘Hotspots’ for single species tended to cluster and were aggregated using minimum convex polygons to derive the coursest scale potential Important Bird Areas. We refer to these as multispecies ‘hotzones’. By this process, we established five seabird ‘hotzones’ in the CCE: North Vancouver Island, Olympic Coast, Central Oregon, Gulf of the Farallones/Monterey Bay, and Santa Barbara Basin. Notably, ‘hotzones’ are associated with at least one and frequently many relatively large, diverse seabird breeding colonies and showed differences in community structure related mostly to breeding seabirds. In addition, most ‘hotzones’ contained one or more shallow-water topographies (seamounts) or bathymetric discontinuities (canyons and escarpments), and variation in freshwater inputs that may contribute to productivity or prey concentrating mechanisms which attract and aggregate seabirds. We propose that seabird ‘hotzones’ are appropriately considered marine Important Bird Areas (mIBA) as this scale of spatial organization captures both potential foraging and reproductive considerations in a multispecies multi-life history context. Finally we used the NPPSD v.2 survey data to validate ‘hotspots’ within these larger ‘hotzones’ against threshold criteria set forth by Birdlife International. Despite being relatively large, ‘hotzones’ of the California Current comprised 25.6% of the U.S. and Canadian EEZ for this ecosystem. Seabird ‘hotzones’ could be useful in meeting seabird and ecosystem conservation goals (e.g., colony and foraging area protection, seabird bycatch reduction, maintaining ecosystem resilience) if warranted through the application of zoning and protected area designations. Seabird ‘hotspots’ could be useful in identifying species specific areas of importance for marine spatial planning purposes.

Acknowledgments We thank Drs. John Piatt and Gary Drew of the U.S. Geological Survey, Biological Research Division for designing, populating, and providing the North Pacific Pelagic Seabird Database version 2 for our study. Funding for this work was provided by the National Audubon Society. We thank Gary Langham for serving as project officer and providing general support to the project. Introduction Seabirds are one of the most threatened taxonomic groups of organisms in the world, with 27% of species currently listed as species of special concern (IUCN 2008). Threats include but are not limited to habitat loss and degradation from invasive species and coastal development, pollution, loss as by-catch, and changes in prey availability from fisheries and climate impacts. Despite the recovery of some populations, notably the California Brown Pelican (Pelecanus occidentalis), which was recently removed from the endangered species list (Federal Register 2009), many populations of seabirds continue to decline with substantial concerns for their long-term viability. At the same time and for largely the same reasons, there has been growing interest and emphasis in developing new approaches and methods to manage marine ecosystems in a holistic and comprehensive manner. Largely, this ecosystem-based management (EBM) approach has been developed due to complexities and interactions among fisheries and other human uses of marine systems (Botsford et al.1997, Pikitch et al. 2004). The need for multispecies approaches

in fisheries management has been recognized for decades (May et al. 1979), but implementation of fisheries management following ecosystem goals, such as maintaining adequate food supplies for ecologically-dependent species like seabirds, is still under development both scientifically and in practice. One of the newest approaches to solving issues of biological complexity and overlapping jurisdictions of marine ecosystems involves coastal and marine spatial planning (CMSP), in which multiple human uses of marine environments and area-based biological significance are synthesized and evaluated to avoid management and conservation conflicts (Crowder et al. 2006, Douvere and Ehler 2009). Within CMSP, some areas may result in the establishment of Marine Protected Areas (MPA), where certain human activities could be prohibited to protect ecosystem structure and functions. Seabirds are part of these novel marine ecosystem management and conservation approaches, though are often overlooked probably because they are not directly exploited by humans and less charismatic than other marine taxa such as whales. Seabirds, however, derive all of their sustenance from the sea, are influenced by the same anthropogenic influences that affect many taxa and processes in marine systems, and, importantly, may be useful as indicators of temporal and spatial variability in the structure and function of marine ecosystems (Piatt et al. 2007, Durant et al. 2009). Seabirds may be particularly useful in providing information on change or the status of marine ecosystems or underlying biological processes that are difficult to obtain when investigating exploited taxa such as fish and marine mammals. These factors are leading to greater acceptance of - and attention to seabirds as marine organisms capable of contributing to ecosystem-based fisheries management as threshold indicators (Cury et al. 2011). The need to improve seabird and marine conservation is leading to new syntheses and analyses of seabird datasets. As conspicuous marine organisms, seabirds provide a window into local, mesoscale, and regional spatial dynamics in the ocean which is often difficult to obtain by other means. Seabird observations from ships (Ainley et al. 2005) or from remotely-sensed tracking studies (Suryan et al. 2006) provide spatially contiguous data for contouring. Other continuous underway data, such as hydroacoustic prey surveys, may be coupled with seabird data to provide an expanded upper and lower trophic level perspective (Russell et al. 1992, Croll et al. 1998, Croll et al. 2005, Santora et al. 2011). For seabird conservation, synthesis and analysis of available observational data is also needed to objectively delineate marine Important Bird Areas (mIBA) (Amorim et al. 2009). For marine ecosystems overall, synthesis and analysis of physical oceanographic, lower trophic level, and seabird data may contribute to robust definitions of Important Ecological Areas (IEA) where the dynamics of marine ecosystem functions, such as trophic interactions, are particularly active. If derived from observations in a transparent manner, mIBA and IEA could play a critical role in seabird conservation as well as EBM of fisheries and zoning associated with CMSP (Game et al. 2009, Grantham et al. 2011). To evaluate potential ‘hotspots’ of abundance and implications for marine-based Important Bird Areas, we conducted a spatial analysis on the distribution and abundance of seabirds in the California Current Ecosystem of the eastern North Pacific (Checkley and Barth 2009), focusing on the continental shelf region from southern British Columbia, Canada to Baja California, Mexico. The California Current is a highly productive yet impacted marine environment (Halpern et al. 2009) which nonetheless supports millions of resident and migratory seabirds (Briggs et al. 1987, Carter et al. 1992). We evaluated whether the identified seabird ‘hotspots’ varied by seabird trophic level, taxonomy, or latitude (region) and investigated whether ‘hotspots’, either independently or in combination (which we refer to as ‘hotzones’), may be useful to infer Important Bird or Ecological Areas (IBA/IEA) in this coastal

environment. To conduct this analysis we synthesized seabird observations contained within the recently updated North Pacific Pelagic Seabird Database (NPPSD v.2). We also used this database to compare numbers of individuals for species of concern against the thresholds set forth by Birdlife International (2010); essentially validating our ‘hotspots’ according to current IBA criteria. Where observational data were limited, we considered different approaches, some currently under development, to estimate seabird distributions using dynamic habitat modeling techniques. In particular, we reviewed and evaluated various tracking studies of seabirds in this environment to determine if these studies corroborated our ‘hotspot’ delineations and could be used to provide information on species’ linkages between ‘hotspots’. Furthermore, we considered the recently published model of Nur et al. (2011) and novel approach of Suryan et al. (2012) to delineate seabird ‘hotspots’ across this ecosystem in regions with poor or limited survey effort. We also evaluate similarities or differences in the model outputs from Nur et al. for corroboration of our empirical analyses.

Methods Study system The California Current ecosystem (CCE) is a large, dynamic and spatially heterogeneous marine environment in the eastern North Pacific Ocean off the west coast of North America (Duda and Sherman 2002). It spans nearly 3,000 km of latitude from Vancouver Island, British Columbia, Canada to Punta Eugenia, Baja California, Mexico (Figure 1). Several major physical oceanographic processes, linked to variability in the atmospheric pressure cells that force winds and ocean circulation, determine ecosystem productivity, structure, and services to society. These include local effects from coastal upwelling and basin-scale sub-arctic and sub-tropical water mass intrusions. The California Current is formed as the eastern leg of the North Pacific Gyre. From an oceanographic perspective, the California Current Large Marine Ecosystem (CCLME) is influenced by the northern and western Pacific, as well as the tropical eastern North Pacific. The intensity of transport in the California Current is not well known, but varies by season, year, and decade (Checkley and Barth 2009). It fluctuates, in part, according to the position and strength of the North Pacific Current/West Wind Drift, which traverses the sub-arctic North Pacific Ocean and bifurcates from British Columbia to northern Oregon into the Alaska and California currents, and the positioning of this current may have influences on ecosystem dynamics (Sydeman et al. 2011). While Washington and southern British Columbia may be considered a transition zone, we define the northern boundary of the California Current as the northern tip of Vancouver Island, B.C., due to frequent upwelling along this section of the coastline in spring and summer (Allen et al. 2001). The southern extent of the ecosystem may be defined as Punta Eugenia, Baja California, Mexico, another biogeographic boundary; coastal upwelling ends at this location. Seabird Datasets We used individual data sets contained within the North Pacific Pelagic Seabird Database (NPPSD v. 2) for this analysis. A complete and detailed description of the NPPSD can be found at http://alaska.usgs.gov/science/biology/nppsd/index.php. Briefly, the NPPSD database integrates a variety of data sets obtained over the past 30-40 years in the North Pacific. Mostly it combines shipboard surveys of seabirds using standardized techniques, made from Russia to Alaska to California. For the California Current, the NPPSD contains datasets from three long-term studies: the California Cooperative Oceanic Fisheries Investigations (CalCOFI; WJ

Sydeman, PI; Hyrenbach and Veit 2003; Yen et al. 2006), the National Marine Fisheries Service Juvenile Rockfish Survey (NMFS-JRS; WJ Sydeman, PI; Yen et al. 2004; Ainley and Hyrenbach 2010, Santora et al. 2011a), and the Canadian Line P program (Line P; KH Morgan, PI; Yen et al. 2005; O’Hara et al. 2006, Thompson et al. 2012). These three studies contain data from the mid 1980s to late 2000s and form the primary basis for this study (Table 1). The CalCOFI data set provides information for the Southern California Bight offshore to ~700 km (Bograd and Lynn 2003), the NMFS-JRS provides information on shelf-based habitats primarily from central-northern coastal California (Sakuma et al. 2006), and Line P provides information on southern British Columbia and offshore to station Papa, approximately 1425 km from the coast (Crawford et al. 2007). We used seabird colony datasets to compile areas of importance for breeding colonies within our study area. These datasets included Breeding Populations of Seabirds in California (Carter et al., 1992; updated in Appendix 1 of the California MLPA Master Plan Science Advisory Team, 2008), Catalog of Oregon Seabird Colonies (Naughton et al., 2007), Catalog of Washington Seabird Colonies (Speich and Wahl, 1989), and the British Columbia Seabird Colony Inventory: Digital dataset (Canadian Wildlife Service, 2008). Finally, we used data obtained from Wolf (2002) and references therein, to estimate numbers of breeding birds for the islands in Baja California, Mexico. Species Selected for Analyses The analyses performed for this study concentrated on the bird species listed in Table 2. This selection of species was made by identifying species that were sufficiently abundant to create robust distribution maps and augmented with species that were of conservation concern due to their vulnerability. Some species are present throughout the CCE domain while others are limited to specific regions. Statistical Methodology Our analysis began with evaluation of the NPPSD, a large database of temporally and spatially variable surveys taken over many decades. These data consist of counts of individuals of species observed on oceanographic survey transects of varying duration (typically from 5 to 30 minutes). Each transect is of known length and width (area) and has associated geo-referencing codes. For analysis, we retained survey data for transects with areas between 0.1 and 25 km2; we deleted these rare larger (25km2) transects because it was not possible to assign these to grid cells (see below). We selected transects surveyed during the period March-August each year to focus on the spring and summer season when the ecosystem was most productive; resident birds are breeding, and southern hemisphere migrants are wintering in the region. This filter eliminated most northern hemisphere migrants wintering in the California Current. Data for fall and winter also were sparse and less amenable to statistical treatments. We did not filter the data for behavioral codes such as flying, sitting on the water, foraging, or ship-following. We binned transects on a grid composed of 10 x 10 km (100 km2) cells and derived total counts (across all surveys) for each species per cell. We also estimated a count of survey effort (sample size of transects) for each grid cell. We estimated the average number of individuals counted for each species per cell by dividing the total number of individuals counted in a cell by the number of times the cell was surveyed. We applied a Kernel Density Smoothing (KDS) function (Silverman 1986, Keating and Cherry 2009 Ecology) to these raster data, using a bandwidth (i.e., smoothing radius) of 25 km. The result of this process was a smoothed density

distribution map for each species with a 1 x 1 km resolution. ‘Hotspots’ were delineated with contours to define and illustrate specific percentile density values. We evaluated ‘hotspots’ at the 5%, 10%, and 20% quantiles, and selected 10% as being the most representative and appropriate across the range of species and habitats examined. Last, based on the apparent overlapping distribution and clustering of ‘hotspots’, we aggregated them into ‘hotzones’ using minimum convex polygons. ‘Hotspots’ of abundance that were separated by more than three grid cells (>30 km apart) generally were not included in ‘hotzone’ aggregations. This analytical methodology produced smoothed density contours to derive and interpret seabird ‘hotspots’, but with three caveats. First, the choice of original cell size is a tradeoff between choosing small cells to maintain spatial detail, which is important for correctly identifying the boundaries of a ‘hotspot’, and large cells that combine sufficient samples from multiple surveys, to provide a robust estimate of the abundance at each cell. Second, the choice of the smoothing radius for Kernel Density Smoothing is again a tradeoff between using a small radius to maintain spatial detail and a large radius to remove sampling artifacts. To determine the choice of smoothing radii for KDS we visually assessed the effectiveness of different values and chose the most appropriate value for illustrating ‘hotspots’ rather than describing habitat for each species. This resulted in a smoothing radius of 25km. Last, the choice of using a density quantile to delineate a ‘hotspot’ affects the resulting size of the ‘hotspot’. We chose to use a density quantile instead of a minimum density (threshold), so we could apply this criterion uniformly across species with differing ranges in density. To identify areas of importance for seabird colonies we applied a KDS to estimated counts of individuals for each species using a 10km bandwidth and then applied contours at the 10% quantile. Population estimates presented in the colony catalogs were compiled from the results of several surveys over the course of years (and in some cases decades) of data. In order to validate these areas based on Birdlife criteria we calculated the number of estimated individuals within 10% quantile contours for each species and matched this to the A4ii criteria thresholds accordingly. Thus our results include ‘colony hotspots’ that satisfy the A4ii criteria thresholds for one or more species.

Other sources of information For marbled murrelet, we used distribution maps for densities of marbled murrelets in near and off shore primary sampling units (PSUs) based on at-sea population surveys performed from 2000 through 2007 (Huff et al., 2006; Raphael, 2007). PSUs with average estimated density values greater than four are nominated in this report for inclusion as proposed marine IBAs for marbled murrelet along the coast of Washington, Oregon, and California.

Results Survey Effort Survey effort across the California Current was variable, with 1 to 212 observations (i.e., transects) per 100-km2 grid cell (n=25,312 cells; mean=89.4 observations per cell, standard deviation = 53.4; mode=1 [n=6,488, 25.6% of cells]). There was one cell with 324 observations that was deleted as an outlier from the above summary. There were regions with extensive survey effort (e.g., southern California) and other regions with very sparse coverage (Figure 2). The only obvious gap in coverage across the ecosystem was from Cape Mendocino (40°N) in northern California to the Oregon border, although even in this region there was some survey effort, albeit very limited. Data from the California-Oregon border to Cape Blanco (42°N),

Oregon was sparse, but sufficient to derive some ‘hotspots’ in this region, particularly in the vicinity of Cape Blanco. Data from central-northern Oregon to Washington also were limited but sufficient to delineate seabird aggregations. However, in general data from central-northern Oregon and Washington were old (Table 1), with many surveys conducted in the mid to late 1970s. The Line P (southern British Columbia), NMFS-JRS (central-northern California) and CalCOFI (southern California) survey efforts were the most extensive in the study area. ‘Hotspots’ Using criteria that a ‘hotspot’ must represent the top 10% of a species’ density contour, we established ‘hotspots’ for 23 of 28 species considered. Overall, we found 99 ‘hotspots’ in our study region (Table 3; Figures 3 & 4), but 7 were questionable due to their size or number of observations contributing to their determination (‘hotspots’ 9, 33, 36, 45, 69, 87, 91). After dropping these, black-footed albatross and Leach’s storm-petrel, pink-footed shearwater and sooty shearwater accounted for 40% of the ‘hotspots’ (Table 4; Figure 4). Size of ‘hotspots’ for all species varied from ~500 km2 to 3000 km2, and averaged 1664 km2. Mean density is highest in ‘hotspots’ for common murre, red-necked phalarope, and sooty shearwater, followed by Leach’s storm-petrel and black-vented shearwater, with an overall average for all species of 6.67 birds per km2 within ‘hotspots’. ‘Hotspots’ for resident breeding species are related to their biogeography. For example, only one ‘hotspot’ was derived for ashy storm-petrel and two for Xantus’ murrelet, all located south of Point Conception (34°N; Figure 5). All three rhinoceros auklet and three of six Cassin’s auklet ‘hotspots’ were located north of Cape Flattery, Washington (49°N). Widely distributed migrants, such as pink-footed and sooty shearwaters, have ‘hotspots’ situated in all regions. To determine whether ‘hotspots’ were valid for triggering an IBA according to criteria set forth by Birdlife International, we calculated the number of times within each ‘hotspot’ species-specific thresholds were reached, and at what time period. Overall, 35 of the 99 ‘hotspots’ were validated for meeting IBA criteria (Table 3; Figures 6 & 7). Using the NPPSD v.2 raw survey data we found 21 ‘hotspots’ met A1 criteria, while 5 ‘hotspots’ met A4ii criteria because they contained a ‘colony hotspot’ for the associated species. Lastly, for all remaining non-validated ‘hotspots’ we used our KDS surfaces to extrapolate the number of birds in these polygons and compared these numbers to the species specific A4ii thresholds; this resulted in nine additional validated ‘hotspots’ for western gulls, Cassin’s auklets, California gulls, and pink-footed shearwaters (Table 3; ‘hotspots’ 25, 29, 66, 67, 92 – 94, 96, 97). None of the ‘hotspots’ in ‘hotzone’ 3 were validated for A1 criteria due to the older age of the survey data (Table 1) even though two ‘hotspots’ (8 & 10) met the threshold criteria for number of individuals for BFAL. To investigate variation in the size and mean density of ‘hotspots’ we categorized species by taxonomic (order to family) and trophic level (planktivore, piscivore, squid-eating/albatrosses) classifications. We found no significant differences in the size of ‘hotspots’ by taxonomy, trophic level, or region (ANOVA: n=92, F7,84=0.36, p=0.9508). However, we found that mean ‘hotspot’ density varies significantly with each of these categories (ANOVA: F7,84=5.08, p<0.0001, r2=36). Mean density per hotspot is significantly greater for (1) planktivores than squid-eating albatross (Bonferroni inequality, p=0.06), (2) phalaropes than procellariforms, pelicaniforms, and gulls (p<0.05), and (3) the Cape Mendocino to Cape Flattery region over south of Point Conception.

‘Hotzones’ Synthesis of overlapping and clustered ‘hotspots’ resulted in the derivation of five ‘hotzones’ of

seabird abundance in the California Current (Table 5; Figure 6). These include: hotzone 1 (North Vancouver Island), hotzone 2 (greater Juan de Fuca Canyon), hotzone 3 (Central Oregon), hotzone 4 (greater Gulf of the Farallones), and hotzone 5 (Santa Barbara Basin). The North Vancouver Island ‘hotzone’ was the largest at 52,100 km2 (Table 6; Figure 7a). Two ‘hotzones’, greater Juan de Fuca Canyon (Figure 7b) and Santa Barbara Basin (Figure 7e), had the greatest species richness (10)(Table 5). Overlapping ‘hotspots’ within each hotzone was highest for the greater Juan de Fuca Canyon (Figure 7b) and Santa Barbara Basin (Figure 7e) (~40% overlap) and lowest for North Vancouver Island and Central Oregon. The area with the greatest amount of habitat covered by ‘hotspots’ is the greater Gulf of the Farallones ‘hotzone’ (Figure 7d; 83% measured) whereas North Vancouver Island and Central Oregon (Figures 7a,c) had just over half of the area covered. ‘Colony Hotspots' Major or numerous minor breeding colonies were associated with each ‘hotzone’ and a total of 44 ‘colony hotspots’ satisfied the A4ii Birdlife criteria (Table 7; Figure 7). With two exceptions, all ‘colony hotspots’ were generally within or near a ‘hotzone’ (Figures 7a-e). The gap in data within the NPPSD v.2 for the area from Cape Mendocino (40°N) in northern California to the Oregon border and the area of Baja California results in these two areas having ‘colony hotspots’ outside of any ‘hotzone’ (Figure 7). Data for mapping marbled murrelet colonies was not available in the colony databases used in this study. Maps created from the Northwest Forest Plan (Huff et al., 2006; Raphael, 2007) do not specifically highlight areas of nesting. Marbled Murrelet Primary sampling units (PSUs) based on at-sea population surveys performed from 2000 through 2007 (Huff et al., 2006; Raphael, 2007) were used to estimate density of marbled murrelet along the CCE. Areas with density greater than 4 are nominated in this report as proposed marine IBAs for marbled murrelet (Figures 9 - 11).

Discussion For decades, the distribution and abundance of seabirds at sea has been studied for a variety of purposes, including (1) assessment of the potential impacts of oil and gas production on seabirds (Briggs et al. 1987), (2) process studies of seabird predator-prey associations (Hunt et al. 1998), and (3) growing interest in marine ecosystem dynamics in which seabirds play a role as predators and competitors with fisheries (Fauchald 2009). Using a combination of surveys conducted for disparate reasons, we have produced “spatial climatologies” for a set of seabird species throughout the California Current. In this study, our focus is to inform marine spatial planning by establishing geospatial reference points that provide baseline information on seabird aggregations and marine Important Bird Areas. As seabirds are known to aggregate in response to prey abundance (Piatt et al. 1990), knowledge of seabird ‘hotspots’ may provide proxies for the spatial climatology of various prey species as well. For example, comparison of top predator and prey hotspots, along with physical ocean conditions, is a promising way forward to understand spatial dynamics of marine ecosystems (i.e., trophic focusing; Santora et al. 2010; Santora et al. 2011b) The NPPSD and Statistical Treatment of the Data The NPPSD contains data from a variety of sources, but most of the data we used came from

shipboard counts of seabirds at sea. However, only three of the source datasets are long-term (>10 years long) and in each case, ongoing (CalCOFI, NMFS JRS, and Line P). Some data, particularly those from the northern California Current (Oregon and Washington) came from the 1970s (Table 1) and there are no recent data from the 1990s or 2000s when most of the CalCOFI and all of the NMFS JRS and Line P data were collected. In producing these climatologies of seabird distribution and abundance, we combined these temporally variable datasets to obtain a California Current-wide perspective. In doing so, we are assuming that the distribution of ‘hotspots’ has not changed systematically through time; an assumption that is impossible to test without new data from regions with old or sparse coverage. Despite some of the source datasets being from the 1970s, we have confidence that many of the ‘hotspots’ derived from these data are robust as they are associated with bathymetric features, such as shallow-water topographies that may force prey concentrations (Allen et al. 2001; Yen et al. 2004) and an aggregative response in the seabirds (Santora et al. 2011a). Bathymetrically-determined ‘hotspots’ are unlikely to have changed substantially through time, and some recent studies support this idea. For example, one of the regions with the weakest data contained in the NPPSD is central Oregon (‘hotzone’ 3). Recent studies there by the U.S. GLOBEC program, which included seabird and marine mammals observations, are not yet included in the NPPSD. The importance of this region, in particular Heceta Bank as a seabird and more generally top predator ‘hotspot’, has been shown by a variety of publications on zooplankton (Reese et al. 2005), forage nekton (Reese and Brodeur 2006), seabirds (Ainley et al. 2005, 2009), and marine mammals (Tynan et al. 2005). The area illustrated by these studies overlaps substantially with some of the central Oregon ‘hotspots’ we found and described here (Figure 7c; ‘hotspots’ 46, 93), thereby providing support that seabird distributions have not changed through time in that area. The Heceta Bank ‘hotspot’ is related to the width and depth of the shelf and currents in the region (Barth et al. 2005), suggesting that aspects of benthic-pelagic coupling may be robust, long-term predictors of seabird distribution (Yen et al. 2004, 2006). There also were few data available in the NPPSD for the fall and winter seasons; consequently, we restricted our analyses to the March – August time period when most of the surveys were conducted. The avifauna of the California Current varies seasonally, with an influx of southern hemisphere migrants in the spring and summer, and an influx of northern hemisphere migrant species in the fall. Our analysis, therefore, is limited to only part of the annual seabird community and does not adequately consider many of the species that breed in Alaska and winter to the south (e.g. black-legged kittiwake, Rissa tridactyla). One of the benefits of the NPPSD is that all species counts were recorded continuously during surveys; these data are then grouped in successive “bins”, with most being 0.9 km2. The mid-point of bins was taken as the geo-location of observation, and each bin was assigned to a 10-km2 grid cell, making it possible to produce relatively high-resolution distribution maps for most of the common species. . While the NPPSD arguably is the best compilation of shipboard surveys of seabirds in the North Pacific, there are a variety of datasets that have not been integrated into this database. In British Columbia, Canada, for example, a 3-year study off southern Vancouver Island focused on the Juan de Fuca eddy, could be added (Burger 2003, Burger et al. 2004). In Washington, surveys conducted by the Olympic Coast National Marine Sanctuary could be incorporated to improve our understanding of hotspots in that region. In Oregon, the GLOBEC (Ainley et al. 2005, 2009) surveys previously mentioned could improve our understanding of seabirds there. From northern California to Washington, neritic surveys (out to 3 miles from shore) targeting

marbled murrelets (Brachyramphus marmoratus) have been conducted since the mid 1990s (Falxa et al. 2011), but have not been included in the NPPSD. In Monterey Bay, local-scale surveys of seabirds and prey were conducted from 1995-2006 (Croll et al. 2005, Newton et al. 2009 MEPS) and from 1999-2001 (Henkel 2001). Henkel (2001) found that mean density of all species (363 birds per square kilometer) was considerably greater at 400-800 m offshore than density reported for Monterey Bay as a whole. Newton et al. (2010) conducted a survey of the Davidson Seamount off Monterey Bay in 2010 highlighting the importance of this feature to marine birds as a potential hotspot. Finally, private pelagic trips in the late summer and fall (Monterey Seabirds, Inc) have highlighted specific areas over Monterey Canyon as hotspots for ashy storm-petrels and Xantus’s storm-petrels aggregating after the breeding season. These latter areas are contained within the hotspot for greater Monterey Bay. To our knowledge, there are no shipboard surveys of seabirds at sea for Baja California, despite extensive fisheries oceanographic surveys conducted by the agency Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE). Remotely-sensed tracking data for this region may be available for a reduced number of species (e.g., Laysan albatross). Needless to say, the NPPSD could be considerably expanded to include a wide variety of surveys not currently integrated into this database. This would serve to enhance the analyses presented here and address questions pertaining to the persistence and consistency of seabird ‘hotspots’ through time. Hotzones: Multispecies Breeding and Foraging Considerations In considering how to develop multispecies ‘hotspots’ and portray similarities and differences in the California Current avifauna, one could combine species to answer specific questions of interest, but this procedure would undoubtedly result in species pooled in a somewhat unsatisfactory manner due to variations in body size, life history, foraging, or dietary characteristics. Therefore, in developing multispecies hotspots we examined the degree of overlap between individual ‘hotspots’ in specific regions of the CCE and subsequently encircled overlapping as well as neighboring ‘hotspots’ using minimum convex polygons. This simple procedure resulted in five major ‘hotzones’ of seabird relative abundance in the ecosystem from the Queen Charlotte Islands (510N) in the north to the Santa Barbara Basin (330N) in the south. This procedure captured one of the most important considerations in determining marine Important Bird Areas; the location and effects of breeding colonies on seabird distribution and abundance. During breeding and especially offspring provisioning, seabird parents are central place foragers, repeatedly returning to a colony to feed dependent offspring after foraging. Therefore, a ubiquitous finding in studies of seabirds at sea is an inverse function between density and distance to breeding colony (Oedecoven et al. 2001, Clarke et al. 2003, Parrish et al. 1998). Generally, foraging seabirds are found in proximity to their breeding colonies and this explains much of the variation in relative abundance; as such, distance to colony should be included in models of seabird habitat associations. Moreover, seabird colonies are obvious Important Bird Areas, especially when multiple species aggregate at the same locations for reproduction. Therefore, while our analyses are based on shipboard observations of seabirds at sea our ‘hotspot’ determinations would be remiss if they did not capture the largest and most diverse seabird breeding colonies in the California Current, as well as probable foraging areas of key species from these colonies. As such, we have included ‘colony hotspots’ based on the most up to date estimates of seabird colonies along the CCE (Carter et al., 1992; Naughton et al.,

2007; Speich and Wahl, 1989; Canadian Wildlife Service, 2008). The ‘hotzones’ we derived captured some of the most important breeding colonies in the California Current. For example, for the North Vancouver Island ‘hotzone’ (hotzone 1; Figure 7a), hotspots were found for Cassin’s and rhinoceros auklets and glaucous-winged gull. The hotspots for Cassin’s auklet correspond to presumed foraging areas based on tracking studies of radio-marked breeding birds from the large Triangle Island colony, where all of these species are known to breed (Boyd et al. 2008; ), and our results confirm this area as validated under the A4ii criterion for Cassin’s auklet and rhinoceros auklet (Figure 7a). For the Gulf of the Farallones/Monterey Bay ‘hotzone’ (hotzone 4; Figure 7d), hotspots were found for Brandt’s cormorant, common murre, Cassin’s auklet and western gull, the four most abundant breeding birds of the Farallon Islands colony. Overlap between these species’ hotspots was substantial. For the Santa Barbara Basin (hotzone 5; Figure 7e), numerous colonies on San Miguel, Anacapa, and Santa Barbara islands were associated with numerous ‘hotspots’ for breeding auklets, ashy storm-petrels, cormorants, and gulls. Unfortunately, we missed several key colonies in northern California (Figure 7c,d), in particular Castle Rock, which hosts an estimated 110,000 breeding common murres as well as 6,000 Cassin’s and 1,000 rhinoceros auklets, 2,500 Brandt’s cormorants, and 1,600 Leach’s storm-petrels (Carter et al. 1992). This colony is located in the only region of the California Current with almost no survey coverage (Figure 2), so this problem was not one of inappropriate procedures but rather of data limitations affecting ‘hotspot’ and ‘hotzone’ determinations. The same is true for the suite of important colonies in Baja California (Figure 7f) for which no survey coverage was available. Overall, our hotzones apparently fused breeding and potential foraging locations for 13 species. In addition to the locally breeding seabird populations, our ‘hotzones’ contained ‘hotspots’ for a diverse assemblage of migrant species including albatross, shearwaters, fulmars, and phalaropes. Given that these species were not constrained to breeding colonies, we found them distributed widely across the study area, from southern California to British Columbia. Black-footed albatross and sooty shearwater ‘hotspots’ were each found in four ‘hotzones’. As seabird communities are comprised of both migrant and resident species, ‘hotzone’ designations would have been remiss if migrants were not included.

Comparison with Seabird Tracking Studies Shipboard surveys provide information on a large number of individual seabirds, but with poor knowledge of their age or breeding status. Tracking studies provide information on few individuals, but typically of known status. Both techniques provide information on seabird concentrations at sea, and therefore it is valuable to compare and contrast results obtained using each technique if possible. With the advent of new technology, particularly satellite-based receivers, studies of seabird distribution and movements at sea have recently flourished. Studies using platform terminal transmitter (PTT) and standard radio transmitters may be used to corroborate species-specific ‘hotspots’ as derived from our kernel density smoothing technique. Birds tagged with standard radio transmitters, geo-locator tags, or PTT tags provide information on foraging locations and movement patterns. Geo-locator tags, however, do not provide information on an appropriate fine-scale resolution for comparison with our study (Shaffer et al. 2006), so will not be discussed further here. In general, tracking studies corroborated our ‘hotspot’ findings. Boyd et al. (2008) studied breeding auklets from Triangle Island, British Columbia and demonstrated foraging areas which are essentially identical to the two “hotspots’ we found for this species in hotzone 1, Northern Vancouver Island. Adams et al. (2004), however, showed auklet foraging areas in close proximity to two breeding colonies in southern

California (hotzone 5), but we found no hotspots for this species in this region, probably because numerically these concentrations were much smaller than ones to the north (near the Farallon Islands and Triangle Island breeding colonies) which reduced their importance below the upper 10% density contour we used for establishing ‘hotspots’. Adams and Takekawa (2008) show ashy storm-petrel aggregations in the Santa Barbara Channel that matched our observations of a ‘hotspot’ at this site. Hyrenbach et al. (2006) demonstrated use of the Gulf of the Farallones and Cordell Bank national marine sanctuaries by tagged black-footed albatross, and we too found two ‘hotspots’ for this species in ‘hotzone’ 4 which contained portions of these sanctuaries. Adams et al. (2012) showed that sooty shearwaters tracked in 2008 and 2009 concentrated in the Columbia River Plume, near Cape Blanco, Monterey Bay, San Luis Obispo Bay, and the Santa Barbara Channel, which matches about half of the ‘hotspots’ we found for this species. Finally, utilization distribution density contours for short-tailed albatross (N = 6; Suryan et al., 2012) and black-footed albatross (Taylor, 2011) coincided with several ‘hotzones’ in our study (Figure 12). Use of Models for Unsurveyed Areas Surveys and substantial coverage were available for much of the California Current making ‘hotspot’ delineations both practical and feasible, but there were some regions with poor coverage, and one area (northern California and southern Oregon, from Cape Mendocino, CA to Cape Blanco, OR) with essentially no data. There also were no survey data available for the Baja California, Mexico portion of the California Current. Models of seabird habitat associations and various spatial relationships among seabirds and their physical and biological environments may be useful to identify potential or even probable ‘hotspots’ of seabird abundance where survey coverage is sparse. Currently, there is only one study that has attempted to model ecosystem-wide seabird-habitat associations in the California Current. The study by Nur et al. (2011) examined habitat associations for 16 species using both spatial and temporal predictor variables. Spatial predictors included latitude, minimum and maximum water depth, rugosity (a bathymetric contour index), distance to land and the 200 m, 1000 m, and 3000 m isobaths, and SST, sea surface height (SSH), and chlorophyll-a concentrations (chl-a) from satellite-based hydrographic records. Temporal variables used in this model included the Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO) Index, North Pacific Gyre Oscillation (NPGO) Index, and of the spring transition of the upwelling index. Aside from date, the bathymetric variables explained most of the variance in seabird distribution and abundance, with the most obvious result being an onshore-offshore gradient in distribution (strong effect of distance from land – and distance to colonies which was not tested). Nur et al. considered many of the same species we did, but they also modeled Bonaparte’s gull (Chroicocephalus philadelphia), herring gull (Larus smithsonianus), Heermann’s gull (L. heermanni) and Sabine’s gull (Xema sabini), all migrant northern hemisphere species, a group that we dropped due to poor survey coverage during the fall and winter when these species are present in the California Current. Nur et al. developed and used three criteria for determining probable ‘hotspots’: (i) standardized estimates of abundance, (ii) importance (similar to ‘core’ habitat for each species), and (iii) persistence between years. Nur et al. also filtered the data they analyzed to include information only on seabirds sitting on the water or actively ‘foraging’ according to field observers (with an exception for species that may forage while flying, such as albatrosses and storm-petrels, where all observations were included). In contrast, we developed ‘hotspots’ solely on the basis of empirical observations of relative abundance (density) and used all behavioral codes to estimate abundance. Nur et al. hinted that the ‘importance’ and ‘persistence’ criteria did not appreciably change their interpretations of probable ‘hotspots’. Nur et al. did not make a list

or measure characteristics of probable ‘hotspots’ by species. Despite these differences in species selection, data filtering, and analysis, the conclusions of Nur et al. (2011) do not differ greatly with the ‘hotspots’, and more significantly ‘hotzones’, we established here. Nur et al. conclude that averaging across seasons, areas of high-summed standardized abundance were associated with the Olympic Peninsula coast (our ‘hotzone’ 2), Heceta Bank (‘hotzone’ 3), Cape Mendocino (no correspondence), Gulf of the Farallones and Monterey Bay (‘hotzone’ 4), and the mainland coast of southern California (corresponding to the Santa Barbara Basin, ‘hotzone’ 5). Nur et al. also indicate the importance of the “northwest tip of Vancouver Island”, corresponding to our ‘hotzone’ 1. Thus, at the general level our findings and those of Nur et al. are very similar. The only major difference is the probable ‘hotspot’ identified at Cape Mendocino. Due to lack of data, we have no evidence of a ‘hotspot’ or ‘hotzone’ there. Cape Mendocino is a site of extensive coastal upwelling (Strub et al. 1991), with substantial bathymetric discontinuities nearby (Gorda escarpment), so it may be a region of elevated productivity and prey concentrating mechanisms. However, high Ekman transport may also make this region turbulent and unstable for the development of feeding strata for seabirds (Santora et al. 2011b). Field studies of this region are required to confirm Cape Mendocino as a ‘hotspot’ of seabird relative abundance. In addition to Nur et al. (2011), the recent study by Suryan et al. (2012) has provided an ecosystem-wide perspective on probable seabird ‘hotspots’, including information relevant to Baja California. Nur et al. used mean chl-a concentrations in their study but found limited support for this variable as predictive of seabird abundance. The study by Suryan et al. (2012) was different in that they not only looked at average chlorophyll concentrations but developed a novel metric of chl-a “persistence” that explained almost twice the variation in seabird abundance than mean chl-a. In general, Suryan et al.'s (2012) Frequency of Peak Chlorophyll Index (FPCI) showed high values along the shelf break – shelf slope region of the California Current from Canada to Mexico (Figure 8). Along this habitat gradient, however, ‘hotspots’ of FPCI were noted at specific locations including (1) southern Vancouver Island and the Olympic Peninsula coast, (2) northern Oregon, (3) Heceta Bank, (4) Cape Mendocino to the Oregon border, (5) Point Arena to Point Reyes in central-northern California, (6) the outer Gulf of the Farallones to Monterey Bay, (7) Point Arguello to Point Conception and the northern Channel Islands, and (8) the northern reaches of Vizcaino Bay, Baja California, Mexico. The FPCI ‘hotspots’ correspond well with the seabird ‘hotzones’ we have described as well as the probable ‘hotspots’ of Nur et al. (2011). The others may be considered possible seabird ‘hotspots’ assuming the seabird-habitat associations derived by Suryan et al. (2012) for the Gulf of the Farallones/Monterey Bay and southern California Bight regions are robust for other regions of the California Current. In summary, the basic seabird ‘hotspots’ modeling approach used to date involves identifying correlations between physical state variables and seabird density and extrapolating these relationships to areas with limited or no survey data. Models of seabirds and their biological environment may be more robust than physical relationships to predict distribution and abundance. Satellite imagery of chlorophyll-a concentration provides the only holistic perspective of the biological environment, yet its utility for understanding and predicting the distribution of seabirds remains equivocal (Gremillet et al. 2008, Nur et al. 2011). While still imperfect, the FCPI may integrate food web processes (productivity and turnover rates, including inherent time lags) that have previously thwarted attempts to model seabird distributions in pelagic ecosystems. New large-scale datasets on seabird prey abundance (Santora et al. 2011b)

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Table 1. A list of surveys that contributed to the California Current portion of the North Pacific Pelagic Seabird Database, ver. 2.

Provider Survey Begin Year End Year Area Surveyed Transects

Benson FW5011 1975 1975 Seattle to Alaska Peninsula South 95

Hatch FW5004 1975 1975 Gulf of Alaska 434

Isleib FW5002 1975 1975 Seattle-Northest Gulf of Alaska-Seattle 131

Hatch FW5013 1975 1975 Southern California to Nome, AK. 174

Rauzon FW5015 1975 1975 NE Pacific, Gulf of Alaska, South. Bering Sea. 223

Nysewander FW5018 1975 1975 NE Pacific to E. Bering Sea. 148

Hatch FW5032 1975 1975 Kodiak Basin, Alaska Peninsula, Southern WA. 60

Sowls FW5021 1975 1975 Long Beach, CA. to Point Barrow, AK. 197

Kirchhoff FW5033 1975 1975 Bering Sea and N. Pacific. 105

Hardy FW5034 1975 1975 N. Gulf of Alaska, BC, Queen Charlotte I., Str. Juan de Fuca. 59

Metzner FW6077 1976 1976 Seattle to Kodiak 50

Baird FW6087 1976 1976 Seattle to Dutch Harbor 25

Baird FW6070 1976 1976 Gulf of Alaska, Bering Sea & NE Pacific. 85

Sanger FW6002 1976 1976 Seattle to Kodiak 72

Wiens TR0609 1976 1976 Northeast Pacific Ocean 39

Hardy FW6005 1976 1976 Seattle to Kodiak 119

Handel FW6008 1976 1976 Seattle to Kodiak 50

Sanger FW7027 1977 1977 Port Angeles, WA to Homer, AK 109

Baird FW7026 1977 1977 Seattle to Kodiak 91

Morgan CWS/LIP 1982 2005 B.C. Coast 22996

Day F85045 1985 1985 Along 155' W to Newport, OR. 259

Day F85046 1985 1985 Newport, Oregon to Hakodate, Japan 478

Day F87041 1987 1987 Seward to Seattle 245

Day F87042 1987 1987 Seattle to N. Pacific 969

Sydeman CalCOFI/NMF 1987 2006 California Coast 138299

Table 2. The 23 species considered and a summary of their relevant characteristics for the purpose of ‘hotspot’ and IBA/IEA analyses.

Common Name, Scientific Name, Abbreviation

IUCN Status

Habitat Diet Nest Sites Seasonality (peak abundance)

Comments

Ashy Storm Petrel Oceanodroma homochroa ASSP

EndangeredA1

Oceanic Plankton/icthyoplankton (neuston layer, krill, amphiods, larval fish)

CA islands Low (resident) Limited observations

Black-footed Albatross Phoebastria nigripes BFAL

EndangeredA1

Oceanic Nekton (mostly squid, less forage fish)

Hawaiian islands Moderate (year-round forager in CCE)

Brandt’s Cormorant Phalacrocorax penicillatus BRAC

Least Concern

Coastal Nekton (forage fish) CA coast and islands

Low (resident)

Brown Pelican Pelecanus occidentalis BRPE

Least Concern

Coastal Nekton (forage fish) CA and Baja CA islands

Moderate (resident, but migrates to northern CCE)

Buller's Shearwater Puffinus bulleri BUSH

VulnerableA1

Shelf to Oceanic

Nekton (forage fish, squid)

S. Pacific islands High (summer)

Black-vented Shearwater Puffinus opisthomelas BVSH

Near Threatened A1

Coastal Nekton (forage fish) Baja CA coastal islands

High (winter)

Cassin's Auklet Ptychoramphus aleuticus CAAU

Least Concern

Shelf Plankton/icthyoplankton (krill, larval fish)

West Coast islands

Low (resident)

California Gull Larus californicus CAGU

Least Concern

Coastal. Omnivorous Inland breeder High (migrant)

Common Murre Uria aalge COMU

Least Concern

Shelf Nekton (forage fish, squid, krill)

West Coast islands

Low (resident)

Cook's Petrel Pterodroma cookii COPE

Vulnerable Oceanic Nekton (forage fish, squid, krill)

NZ coastal islands

High (summer) Observed offshore only.

Elegant Tern Sterna elegans ELTE

Near Threatened A1

Coastal Nekton (forage fish) Baja CA coastal islands

Moderate (breeder, but mig. north)

Common Name, Scientific Name, Abbreviation

IUCN Status

Habitat Diet Nest Sites Seasonality (peak abundance)

Comments

Fork-tailed Storm-Petrel Oceanodroma furcata FTSP

Least Concern

Oceanic Plankton/icthyoplankton (neuston)

N. Pacific coastal islands

Low

Glaucous-winged Gull Larus glaucescens GWGU

Least Concern

Coastal to Shelf

Omnivorous N. Pacific coastal islands

Low

Laysan Albatross Phoebastria immutabilis LAAL

Near Threatened A1

Oceanic Nekton (mostly squid, forage fish)

Hawaiian islands Moderate (spring)

Leach's Storm Petrel Oceanodroma leucorhoa LHSP

Least Concern

Oceanic Plankton/icthyoplankton (krill, larval myctophids, amphipods)

West Coast islands

Low (resident)

Northern Fulmar Fulmarus glacialis NOFU

Least Concern

Shelf to Oceanic

Plankton/icthyoplankton (krill, copepods, larval fish)

Alaskan islands High (winter)

Pink-footed Shearwater Puffinus creatopus PFSH

VulnerableA1


Chilean islands High (Seasonal migrant)

Red Phalarope Phalaropus fulicaria REPH

Least Concern

Coastal to Oceanic

Plankton/icthyoplankton (neuston)

High (Seasonal migrant)

Rhinoceros Auklet Cerorhinca monocerata RHAU

Least Concern


CA and N. Pacific islands

Moderate (resident, winter)

Red-necked Phalarope Phalaropus lobatus RNPH

Least Concern

Coastal to Oceanic

Plankton/icthyoplankton (neuston)

High (Seasonal migrant)

Sooty Shearwater Puffinus griseus SOSH

Near Threatened A1


New Zealand, Chilean islands

High (summer)

Western Gull Larus occidentalis WEGU

Least Concern

Coastal to Shelf

Nekton (forage fish, squid, krill)

CA coastal islands

Low (resident)

Xantus’ Murrelet Synthliboramphus hypoleucus XAMU

VulnerableA1

Shelf Plankton/icthyoplankton (krill, larval fish)

CA and Baja CA islands

Low (resident)

Table 3. ‘Hotspots derived for each species, with summary characteristics. See text for methods. The measure "Obs / Years" refers to the number of times that a species was observed and the number of different years for which the species was observed within each ‘hotspot’. As ‘hotspots’ reflect density contours, it is possible to have no observations within the exact ‘hotspot’ region. "Max. Density" is the peak density value within the hotspot and "Mean Density" is the mean for all density values within each ‘hotspot’. “Validated” refers to whether the ‘hotspot’ met the Birdlife International A1 or A4ii threshold criteria. Of the 99 hotspots, 7 were labeled "Questionable" based on their limited size or limited number of observations. ‘HotZone’ refers to the assignment to region of overlapping or clustered hotspots (see Table 5). Of the 99 hotspots, 74 fell within a ‘hotzone’.

Hot Spot

Species Size km2

Obs / Years

Max. Density

Mean Density

Centroid Lon.

Centroid Lat.

Name Hot Zone

Validated

1 ASSP 520 29/12 1.29 1.13 -120.246 34.342 Point Conception, CA

5 A1

2 BFAL 1086 59/2 1.03 0.84 -130.575 51.702 Queen Charlotte Sound, BC

1 A1

3 BFAL 646 31/3 0.8 0.73 -129.555 51.279 Queen Charlotte Sound, BC

1 A1

4 BFAL 806 28/5 0.89 0.77 -126.566 48.849 La Pérouse Bank, BC

2 A1

5 BFAL 1576 196/8 0.91 0.78 -125.935 48.403 La Pérouse Bank, BC

2 A1

6 BFAL 3665 86/7 2.84 1.44 -125.004 47.208 Olympic Coast, WA

2 -

7 BFAL 419 9/5 0.78 0.72 -124.583 46.283 Astoria Canyon, WA

2 -

8 BFAL 1343 23/7 1.15 0.87 -124.771 44.698 Newport, OR 3 - 9 BFAL 27 0/0 0.67 0.67 -124.853 44.360 Heceta Valley, OR 3 -

10 BFAL 913 14/7 0.99 0.82 -124.830 43.068 Cape Blanco, OR 3 11 BFAL 5276 386/9 1.65 1.06 -123.583 38.286 Mendocino Coast,

CA 4 A1

12 BFAL 1499 132/11 1.14 0.88 -122.852 37.273 San Mateo Coast, CA

4 A1

13 BRAC 2005 153/10 3.5 2.24 -122.854 37.709 Farallon Islands, CA

4 -

14 BRAC 538 26/11 1.7 1.49 -120.887 35.140 Point Sal, CA - A4ii

15 BRAC 314 7/8 1.53 1.41 -117.340 32.825 San Diego, CA - 16 BRAC 970 22/4 2.11 1.68 -120.373 34.127 San Miguel Island,

CA 5 A4ii

17 BRPE 1633 17/7 3.49 2.38 -118.736 34.035 Santa Monica Basin, CA

- -

18 BRPE 1426 276/11 2.33 1.84 -119.418 34.062 Anacapa Island, CA

5 -

19 BRPE 1419 21/6 2.26 1.83 -120.155 34.103 Santa Barbara Basin, CA

5 -

20 BUSH 1430 49/10 0.73 0.51 -126.027 48.531 La Pérouse Bank, BC

2 A1

21 BUSH 787 8/3 0.47 0.4 -122.467 34.429 Offshore near Point Sal, CA

- -

22 BVSH 661 6/6 14.39 12.06 -118.689 34.048 Santa Monica Basin, CA

- -

23 CAAU 216 8/3 8.79 8.33 -131.363 52.159 S. Queen Charlotte Island, BC

1 -

24 CAAU 959 13/5 11.15 9.31 -129.930 50.954 Queen Charlotte Sound, BC

1 -

25 CAAU 3287 122/5 50.33 23.3 -129.233 50.513 Triangle Island, BC 1 A4ii

26 CAAU 345 15/4 8.98 8.42 -124.625 42.720 Cape Blanco, OR 3 - 27 CAAU 510 113/7 9.73 8.76 -123.173 37.788 Farallon Islands,

CA 4 -

28 CAAU 144 11/6 8.42 8.14 -119.478 33.918 Anacapa Island, CA

5 -

29 CAGU 3078 103/11 13.72 6.49 -125.167 48.581 Juan de Fuca Canyon, BC

2 A4ii

30 CAGU 348 2/3 3.47 3.17 -124.003 44.628 Newport, OR 3 - 31 CAGU 1602 74/10 7.52 4.98 -118.023 33.660 Bolsa Bay, CA - - 32 COMU 157 10/5 18.16 17.43 -124.420 47.367 Olympic Coast,

WA 2 -

33 COMU 1 0/0 93.15 93.15 -123.984 44.632 Newport, OR 3 - 34 COMU 2360 34/5 93.09 49.29 -123.990 44.634 Newport, OR 3 - 35 COMU 2127 523/9 28.53 22.23 -122.923 37.802 Farallon Island, CA 4 - 36 COPE 66 1/1 0.22 0.22 -123.727 32.805 Off Jasper

Seamount, CA - -

37 COPE 630 17/3 0.29 0.26 -122.926 32.492 Off Jasper Seamount, CA

- -


- -


- -


- -


- -

42 ELTE 2388 137/12 0.53 0.4 -118.258 33.627 Palos Verdes / Bolsa Chica,

- A1

43 FTSP 3182 99/7 20.67 10.32 -125.472 48.195 Juan de Fuca Strait, BC/WA

2 -

44 FTSP 840 43/4 6.1 5.26 -125.046 47.115 Olympic Coast, WA

2 -

45 FTSP 107 1/1 4.65 4.54 -124.781 46.292 Astoria Canyon, WA

2 -

46 FTSP 1284 25/8 8.46 6.3 -124.882 44.066 Heceta Valley, OR 3 - 47 GWGU 1377 25/8 1.95 1.5 -128.196 50.909 North Vancouver

Island, BC 1 -

48 GWGU 1203 37/3 1.79 1.43 -123.206 48.627 San Juan, BC - - 49 GWGU 3494 247/11 2.8 1.78 -125.137 48.347 Juan de Fuca

Canyon, BC/WA 2 -

50 GWGU 831 14/7 1.5 1.3 -124.995 47.688 Olympic Coast, WA

2 -

51 LAAL 3018 5/3 0.05 0.03 -122.463 31.785 Off Jasper Seamount, CA

- A1

52 LHSP 377 26/1 2.28 2.13 -131.157 51.417 Queen Charlotte Sound, BC

1 -

53 LHSP 6317 233/9 7.19 3.25 -129.825 51.265 Queen Charlotte Sound, BC

1 -

54 LHSP 2240 64/5 7.06 4.12 -129.450 50.577 Triangle Island, BC 1 - 55 LHSP 4102 213/8 4.94 2.85 -129.388 49.873 Off Vancouver

Island, BC 1 -

56 LHSP 228 28/1 2.12 2.04 -129.149 49.165 Off Vancouver Island, BC

- -


- -


- -


- -

60 LHSP 3872 75/5 9.92 4.75 -125.380 42.672 Cape Blanco, OR 3 - 61 LHSP 1191 23/0 2.98 2.43 -126.355 42.674 Cape Blanco, OR 3 - 62 NOFU 1354 70/7 26.09 18.76 -125.412 48.355 Juan de Fuca

Canyon, BC/WA 2 -

63 NOFU 248 21/4 14.39 13.51 -124.969 48.050 Juan de Fuca Canyon, BC/WA

2 -

64 NOFU 554 18/6 16.82 14.66 -125.045 47.056 Olympic Coast, WA

2 -

65 PFSH 450 80/7 1.23 1.12 -125.961 48.534 La Pérouse Bank, BC

2 A1

66 PFSH 2045 21/8 3.02 1.87 -125.031 47.717 Olympic Coast, WA

2 A4ii

67 PFSH 1311 11/4 2.19 1.56 -124.456 43.465 Heceta Valley, Cape Blanco

3 A4ii

68 PFSH 1839 66/4 1.95 1.42 -123.699 38.414 Point Arena, CA 4 A1 69 PFSH 80 15/5 1.04 1.03 -122.698 37.091 San Mateo, CA 4 A1 70 PFSH 348 4/5 1.17 1.09 -118.965 33.957 Santa Monica

Basin, CA - -

71 PFSH 1293 95/14 1.39 1.19 -121.026 34.876 Point Sal, CA - A1 72 PFSH 2296 138/15 2.76 1.77 -120.712 34.234 Point Conception,

CA 5 A1

73 PFSH 272 42/7 1.1 1.06 -120.344 33.792 S. San Miguel Island, CA

5 -

74 PFSH 4330 208/14 1.91 1.39 -119.639 33.503 Santa Cruz Basin, CA

5 A1

75 REPH 935 25/6 10.51 8.27 -120.603 32.977 Off San Juan Seamount, CA

- -

76 REPH 1679 11/6 19.38 12.16 -123.658 33.848 Off Point Conception, CA

- -

77 RHAU 416 10/2 2.99 2.71 -128.359 51.722 Queen Charlotte Sound, BC

1 -

78 RHAU 1662 54/6 6.15 4.1 -131.163 52.080 S. Queen Charlotte Island, BC

1 A4ii

79 RHAU 5048 220/9 6.88 4.07 -128.199 50.971 North Vancouver Island, BC

1 A4ii

80 RNPH 110 18/5 16.87 16.42 -122.235 36.976 Año Nuevo Canyon, CA

4 -

81 RNPH 2143 18/10 67.03 37.99 -119.930 33.956 Santa Cruz Island, CA

5 A1

82 SOSH 1132 130/5 21.3 18.44 -128.756 50.953 Triangle Island, CA

1 -

83 SOSH 1552 94/8 35.02 24.5 -126.744 49.343 Vancouver Island, CA

2 A1

84 SOSH 4326 356/13 49.9 26.82 -124.992 48.133 Juan de Fuca Canyon, WA/BC

2 A1

85 SOSH 1311 42/7 28.27 21.31 -124.496 47.256 Olympic Coast, WA

2 -

86 SOSH 2785 96/7 29.15 21.57 -124.797 46.904 Olympic Coast, WA

2 -

87 SOSH 38 1/0 16.06 15.92 -124.153 39.060 Arena Canyon, CA - - 88 SOSH 4930 1195/14 33.41 23.25 -122.402 37.029 San

Mateo/Monterey, CA

4 A1

89 SOSH 1447 34/6 35.44 24.59 -121.473 35.509 Piedras Blancas, CA

- A1

90 SOSH 334 20/2 17.5 16.66 -120.525 34.152 San Miguel Island, CA

5 -

91 SOSH 13 0/0 15.88 15.83 -119.855 33.376 Santa Rosa/Cortes Ridge, CA

5 -

92 WEGU 2147 9/5 12.73 7.12 -123.985 44.635 Newport, OR 3 A4ii 93 WEGU 875 6/6 4.62 3.69 -124.600 44.046 Heceta Valley, OR 3 A4ii 94 WEGU 2463 541/12 6.18 4.29 -122.852 37.601 Farallon Islands,

CA 4 A4ii

95 WEGU 757 130/9 3.53 3.17 -119.120 33.524 Santa Barbara Island, CA

5 A4ii

96 WEGU 6151 940/15 14.53 5.7 -119.713 34.004 Santa Barbara Basin, CA

5 A4ii

97 WEGU 459 99/6 3.18 3 -119.652 33.442 Santa Cruz Basin, CA

5 A4ii

98 XAMU 437 10/7 0.27 0.25 -119.631 34.011 Santa Cruz Island, CA

5 -

99 XAMU 2251 22/8 0.4 0.31 -118.736 32.930 San Clemente Island, CA

5 -

Table 4. ‘Hotspot’ statistics grouped by species, showing the mean and s.d. for hotspot size, mean and s.d. for bird density within ‘hotspots’ and the number of ‘hotspots’ analyzed. Questionable ‘hotspots’ (n=7) were omitted from this summary.

Species Common Name Abbreviation Mean size, s.d.

(km2) Mean density, s.d.

(birds/km2) N

Ashy Storm-Petrel ASSP 520 0 1.13 0 1

Black-footed Albatross BFAL 1723 1541 0.89 0.22 10

Brandt's Cormorant BRAC 957 750 1.71 0.37 4

Brown Pelican BRPE 1493 122 2.02 0.32 3

Buller's Shearwater BUSH 1109 455 0.46 0.08 2

Black-vented Shearwater BVSH 661 0 12.06 0 1

Cassin's Auklet CAAU 910 1200 11.04 6.02 6

California Gull CAGU 1676 1367 4.88 1.66 3

Common Murre COMU 1548 1210 29.65 17.18 3

Cook's Petrel COPE 1946 2410 0.27 0.04 5

Elegant Tern ELTE 2388 0 0.40 0 1

Fork-tailed Storm-Petrel FTSP 1769 1244 7.29 2.67 3

Glaucous-winged Gull GWGU 1726 1200 1.50 0.20 4

Laysan Albatross LAAL 3018 0 0.03 0 1

Leach's Storm-Petrel LHSP 2003 2092 2.81 0.95 10

Northern Fulmar NOFU 719 571 15.65 2.76 3

Pink-footed Shearwater PFSH 1576 1276 1.39 0.30 9

Red Phalarope REPH 1307 526 10.22 2.75 2

Rhinoceros Auklet RHAU 2375 2397 3.63 0.79 3

Red-necked Phalarope RNPH 1127 1438 27.21 15.25 2

Sooty Shearwater SOSH 2227 1635 22.14 3.37 8

Western Gull WEGU 2142 2123 4.50 1.61 6

Xantus' Murrelet XAMU 1344 1283 0.28 0.04 2

Table 5. A list of ‘hotzones’, based on overlapping or clustered of hotspots. ‘Hotzones’ were constructed by drawing a minimum convex bounding box around each cluster of hotspots. The table lists which species-specific hotspots are present within each hotzone. The "HotZone Fill Factor" is a measure of how much of a hotzone is filled by hotspots, indicating how closely grouped the hotspots occur within the hotzone. The Fill Factor is calculated from the size of the combined hotspots within a hotzone, relative to the size of the hotzone. "HotSpot Overlap" is a measure of how much the species-specific hotspots overlap, an indication that an area is a hotspot for several species. The Overlap is the ratio of the total overlapping area of the separate hotspots within a hotzone, relative to the size of the combined hotspots within that hotzone. The colonies listed are the largest colonies within or nearby the hotzone that are associated with the hotzone. The oceanographic features are the most important features known to contribute to primary productivity and influence the availability of food.

Hot Zone

Area km2

Species Present Species HotZone Fill Factor

HotSpot Overlap

Largest Colonies Present

Oceanographic Features Present

1 52094 BFAL CAAU GWGU LHSP RHAU SOSH

6 42.4 % 19.2 % Triangle I., BC South Queen Charlotte I., BC North Vancouver I., BC

Queen Charlotte Sound, BC

2 33337 BFAL BUSH CAAU CAGU COMU FTSP

GWGU NOFU SOSH PFSH

10 57.4 % 42.7 % Tatoosh I., WA South Vancouver I., BC

La Pérouse Bank, BC Juan de Fuca Canyon, BC/WA Astoria Canyon, WA/OR

3 34928 BFAL CAAU CAGU COMU FTSP LHSP

PFSH WEGU

8 33.1 % 19.3 % Saddle Rock, OR Heceta Valley, OR Blanco Saddle, OR

4 16102 BFAL BRAC CAAU COMU PFSH REPH

RNPH SOSH WEGU

9 82.5 % 34.7 % Farallon I.s, CA Ano Nuevo, CA

Cordell Bank, CA Bodega Canyon, CA Monterey Canyon, CA

5 17823 ASSP BRAC BRPE BVSH CAAU PFSH

RNPH SOSH WEGU XAMU

10 76.1 % 41.9 % Prince I., CA San Miguel I. CA Santa Barbara I., CA Anacapa I., CA

Channel Islands, CA Santa Barbara Basin, CA Santa Cruz Basin, CA Cortez Ridge

None N/A BUSH CAGU COPE ELTE LAAL

5 N/A N/A Bolsa Chica, CA (ELTE)

Table 6. Hotspot statistics grouped by hotzone, showing the mean and standard deviation for hotspot size, mean and standard deviation for bird density, maximum and standard deviation for bird density, and the number of hotspots.

Hotzone

Mean hotspot size, s.d.

(km2)

Mean hotspot bird density, s.d. (birds/km2)

Max. hotspot bird density, s.d. (birds/km2)

N

1 2062 1908 6.12 6.79 9.49 12.89 14

2 1705 1278 9.55 9.35 13.65 14.19 20

3 1454 1018 8.04 13.91 13.51 26.68 11

4 2307 1760 8.95 9.27 11.44 12.20 9

5 1594 1687 5.73 9.87 8.57 16.97 15

Table 7. ‘Colony hotspots’ with summary characteristics. See text for methods. “A4ii Threshold Met” refers to the species in that colony that met or exceeded the thresholds for number of birds set forth by Birdlife International. BC=British Columbia, WA=Washington, OR=Oregon, CA=California, Baja=Baja California. Centroid Lon., Centroid Lat. = Centroid Longitude & Latitude.

Name State A4ii Threshold Met

Centroid Lon.

Centroid Lat.

Hotzone

Solander Is. BC CAAU, LSPE 50.11 -127.94 - Gilliam Is. BC FTSP, LSPE 50.45 -127.97 1 Triangle Is. BC CAAU, RHAU 50.83 -128.97 1 Storm Is. BC FTSP, LSPE,

RHAU 51.01 -127.72 1

Kerouard Is. BC CAAU 51.93 -131.00 1 S’Gaang Gwaii BC CAAU 52.10 -131.22 1 Rankine Is. BC ANMU, CAAU 52.26 -131.05 1 George Is. BC ANMU 52.35 -131.21 1 Moore/Byers Is. BC CAAU, RHAU 52.61 -129.40 - Lihou/Helgesen Is. BC ANMU, RHAU 53.01 -129.40 - Hippa Is. BC ANMU 53.54 -133.00 - Frederick Is. BC ANMU, CAAU 53.93 -133.18 - Langara Is. BC ANMU 54.25 -132.98 - Protection Is. WA GWGU 48.13 -122.93 - Alexander Is. WA CAAU 47.80 -124.51 2 Destruction Is. WA RHAU 47.68 -124.48 2 Gunpowder Is. WA GWGU 46.68 -124.04 - East Sand Is. OR GWGU, DCCO,

CATE 46.26 -123.98 -

Three Arch Rocks OR > 80,000 COMU 45.46 -123.99 - Hunter’s Is./Saddle Rock/Goat Is.

OR LESP 42.18 -124.38 -

False Klamath Rock

CA 8 species; >45000 birds

41.59 -124.11 -

Castle Rock CA COMU 41.76 -124.25 - Trinidad Complex CA 12 species;

COMU

Humboldt Bay CA > 100,000 birds 40.707 -124.25 - False Cape Rock CA >10,000 birds - Steamboat Rock CA 40.42 -124.40 - Farallon Is. CA 13 species;

>250,000 birds 37.73 -123.05 4

Pt. Reyes CA 8 species; >45,000 birds

37.99 -123.00 4

Spooner’s Cove CA BRCO 35.22 -120.88 - Channel Is. North CA ASSP, BRCO,

WEGU, XAMU 33.99 -119.93 5

Santa Barbara Is. CA ASSP, WEGU, XAMU

33.46 -119.35 5

Bolsa Chica Ecol. Reserve

CA ELTE 33.70 -118.03 -

Islas los Coronados BAJA XAMU 32.42 -117.27 - Todos Santos BAJA XAMU 31.81 -116.75 - Punta Colnett BAJA BLAL 30.62 -116.28 - San Jeronimo BAJA XAMU 29.83 -115.94 - Isla Guadalupe BAJA BVSH; XAMU 29.04 -118.30 - Bahia Santa Rosalillita

BAJA 28.63 -114.31 -

Islas San Benito BAJA BVSH; XAMU >100,000 birds

28.31 -115.58 -

Isla Cedros BAJA 28.18 -115.24 - Oja de Libre BAJA 27.94 -114.23 - Isla Natividad BAJA BVSH; WEGU 27.87 -115.18 - Bahia Magdalena BAJA 24.78 -112.11 -

Figure 1. The California Current is considered to begin at the northern tip of Vancouver Island, British Columbia, Canada, and flow down the US coast towards the equator. Punta Eugenia, Baja California, MX is considered to be the southern limit of this ecosystem.

Figure 2. The analysis domain for this report is represented by a rectangle in this projection. Within the analysis domain, the survey effort for the bird observation dataset used (NPPSD v2) is represented as the number of times that each 10x10 km grid cell was surveyed.

Figure 3. An overview map of all hotspots detected within the analysis domain. The numbers refer to the hotspots listed in Table 3.

Figure 4. A map of hotspots detected for each species. The numbers refer to the hotspots listed in Table 3.

Figure 4 (continued). A map of hotspots detected for each species. The numbers refer to the hotspots listed in Table 3.

Figure 5. The spatial distribution of hotspots by species throughout the California Current. Regions were modified after GLOBEC (1992).

Figure 6. Map of global marine Important Bird Areas (IBAs) in the California Current. Zones refer to areas with clusters of IBAs.

105°W110°W115°W120°W125°W130°W135°W

50°N

45°N

40°N

35°N

30°N

25°N0 210 420105 Kilometers¯

Zone 1

Zone 2

Zone 3

Zone 4

Zone 5

Ca

li

fo

rn

ia

C

ur

re

nt

Gu

l f of A

l as

ka

Global Marine Important Bird Areas

Purple areas are validated under A1/A4ii criteriaYellow areas are colony hotspots (A4ii criteria)

Solid outlines represent 'Hotzones' 1 - 5

California

94

11

88

68

12

69

wegu

bfal

sosh

pfsh

bfal

pfsh

Farallon Islands

Trinidad Complex

Humboldt Bay

Pt. Reyes

False Cape RockSteamboat Rock

122°W124°W

40°N

38°N

Three Arch Rocks

Protection IslandAlexander Island

Destruction Island

Gunpower Island

East Sand Island

29

66

84

5

4

83

20 65 cagu

pfsh

sosh

bfal

sosh

bush

bfal

pfsh

122°W124°W126°W

50°N

48°N

46°N

bb

0 75 15037.5 KilometersZone 2

0 50 10025 Kilometers

Oregon

California

Three Arch Rocks

Hunters IslandSaddle Rock

Goat Island

92

67

93

wegu

pfsh

wegu

False KlamathCastle Rock

124°W126°W

44°N

42°N

cc dd

Zone 4Zone 30 50 10025 Kilometers

25

79

2

78

3

82rhau

rhau

sosh

bfal

bfal

caau

so sh

sosh

Triangle I.Storm I.

Frederick I.

Gil lam I.

George I.

Langara I.

Moore/Byers I.

Solander I.

Hippa I.

Lihou/Helgesen I.

Rankine I.

Kerouard I.S'Gaang Gwaii

128°W130°W132°W134°W

54°N

52°N

50°N

aa

0 75 15037.5 KilometersZone 1

Global Marine Important Bird Areas


Figure 7a-d. Map of 'hotspots', 'hotzones', and 'colony hotspots'. Numbers in polygons correspond to 'hotspot' IDs in Table 3. Boundaries for non-colony polygons in the map are based on the 10% quantile density distribution for species in Table 3. Four letter labels refer to the abbreviation for the common name of the species that triggeredthe Important Bird Area. Colonies are detailed in Table 7.

Figure 7e-f. Map of 'hotspots', 'hotzones', and 'colony hotspots'. Numbers in polygons correspond to 'hotspot' IDs in Table 3. Boundaries for non-colony polygons in the map are based on the 10% quantile density distribution for species in Table 3. Four letter labels refer to the abbreviation for the common name of the species that triggeredthe Important Bird Area. Colonies are detailed in Table 7.

Bahia Magdale na

Punta Colnett

San Jeronimo

Oja de Libre

Isla Guadalupe

Isla Cedros

Islas San Benito

Todos Santos

Isla Natividad

Bahia Santa Rosalillita

Islas los Coronados

112°W114°W116°W118°W

32°N

30°N

28°N

26°N

24°N

0 40 8020 Kilometers Baja Colonies

Davidson Seamount

96Channel Islands North

Spooner 's Cove

Santa Barbara Island

Bolsa Chica

wegu

wegupfsh

laal

el te

pfsh

sosh

pfsh

brco

wegu

brco

assp

74

51

42

72

89

71

1

16

95

14

97

118°W120°W122°W

36°N

34°N

32°N

0 40 8020 Kilometers Zone 5

ee ff Global Marine Important Bird Areas


Figure 8. Chlorophyll mean (A) and persistence (B) values for three regions of the California Current from Suryan et al. (2012).

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Figure 9. Average estimated population density of marbled murrelet by primary sampling unit (PSU) in Oregon. See Raphael et al., 2007 for details.

Figure 10. Average estimated population density of marbled murrelet by primary sampling unit (PSU) in California. See Raphael et al., 2007 for details.

Figure 11. Average estimated population density of marbled murrelet by primary sampling unit (PSU) in Washington. See Raphael et al., 2007 for details.

Figure 12. Map of utilization distribution density contours (UDs) for two species of albatross in the California Current. Short-tailed albatross UDs are represented by red (20%) and yellow (50%) outlines, and black-footed albatross UDs are represented by light grey (30%) and dark grey (60%). Dotted outlines delineate 'hotzones'.

120°W125°W130°W135°W

50°N

45°N

0 100 20050 Kilometers

Zone 1

Zone 2

Zone 3

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