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Note that a Preprint of this article also exists, first published October 24, 2014.


Ecologists and conservation practitioners have long focused on describing species distribution and estimating changes in abundance (Holmes, 2001) or occurrence through time (MacKenzie et al., 2006). Using species distribution modeling to identify the spatial variability and hotspots of a species’ distribution has multiple implications for science and management. From a conservation perspective, incorporating spatial variation in models may assist in selecting areas to protect or predicting where species are likely to persist (Cabeza & Moilanen, 2001; Naujokaitis-Lewis et al., 2009). From a theoretical ecology perspective, null or neutral models of species’ occurrence may be useful in predicting species diversity or community assembly (Gotelli, 2000; Gotelli & McGill, 2006). Finally, the inclusion of spatial variation has implications for management and policy in that accounting for spatial heterogeneity may help in forecasting how species may respond to future environmental conditions, such as range shifts in response to climate change (Jetz, Wilcove & Dobson, 2007).

In addition to quantifying spatial variation, species distribution modeling can be used to assess temporal trends in occurrence, which themselves may be spatially structured as well. Mechanisms responsible for spatially structured trends may include changing habitat conditions, behavior, or prey availability (Ward et al., 2010). Spatially structured anthropogenic disturbances (e.g., wildfires, oil spills, climate change, urbanization) may have similar impacts, and collectively ignoring such underlying spatial variation when it exists may lead to poor estimation of temporal trends (Hoeting et al., 2006).

Models that incorporate both spatial and temporal variation represent a rapidly evolving field in ecology (Hooten & Wikle, 2008; Latimer et al., 2006; Shelton et al., 2014). While many of these methods have been in the statistical literature for decades (Banerjee, Gelfand & Carlin, 2005; Cressie & Wikle, 2011), ecological data often present a unique set of challenges relative to data from other fields. Compared to other disciplines, ecological data on species abundance is often corrupted by observation error, which represents uncertainty arising from taking measurements or sampling a fraction of the population (Holmes, 2001). Similarly, in conducting studies of species presence–absence, detections may be missed, resulting in false-negatives (MacKenzie et al., 2006). Because of recent computational advances, statistical models that include both spatial and temporal variation are now widely available to ecologists and offer a powerful tool for assessing changes in species distributions through time.

As data hungry methods have advanced, monitoring programs have suffered in recent years because of declining budgets and an increased need for cost efficient survey techniques. In the face of recent reductions in monitoring programs, one potentially underutilized resource is citizen science, which may be a useful tool for conducting baseline environmental monitoring or helping to inform management actions or restoration activities (Cooper et al., 2007). Participation in these volunteer-based programs appears to have increased in recent years (Silvertown, 2009). Some of the longest running citizen-science programs in North America are related to bird watching. Large-scale volunteer programs like the Audubon Christmas Bird Count and the North American Breeding Bird Survey (BBS) have been effective at collecting vast amounts of survey data on commonly occurring bird species (Sauer et al., 2014). The strength of these programs is their duration, large spatial extent, and consistent methodologies over time, enabling them to be useful in monitoring species assemblages and distribution shifts in response to changing climate (Hitch & Leberg, 2007).

In the Pacific Northwest, similar regional-scale citizen-science programs focused on seabirds have been established. These include the Coastal Observation and Seabird Survey Team (COASST) (Hamel et al., 2009; Litle, Parrish & Dolliver, 2007; Parrish et al., 2007) and the British Columbia Coastal Waterbird Survey (Crewe et al., 2012), which have also been developed to address conservation questions and establish baseline monitoring. These regional citizen-science programs help inform restoration actions in Puget Sound (Washington State), where one of the largest ecosystem restoration programs in the nation is underway (Puget Sound Partnership, 2014). The Puget Sound ecosystem is part of the Salish Sea (which also includes the Strait of Juan de Fuca and the Strait of Georgia), and has been affected by widespread environmental degradation largely associated with increased urbanization (effects summarized in Puget Sound Partnership, 2009; Ruckelshaus & McClure, 2007). Puget Sound consists of over 4,000 km of coastline, with a suite of high-value ecosystem services, including commercial fisheries and various recreation opportunities (e.g., Tallis & Polasky, 2009). A portfolio of ecosystem indicators has been developed and incorporated into restoration goals for the Puget Sound region to monitor ecological conditions, including seabirds (Kershner et al., 2011; Puget Sound Partnership, 2013).

A limitation of using the data from many citizen-science programs—including those from regional seabird monitoring programs in the Pacific Northwest—has been that survey effort is generally not quantified. A limited number of agency-funded seabird surveys have been conducted that allow the assessment of trends. To assess winter seabird densities, the Washington Department of Fish and Wildlife (WDFW) has conducted annual aerial surveys since 1992, representing a 6-week snapshot of density (Anderson et al., 2009; Nysewander et al., 2005). These annual transects occur in 13–18% of the nearshore (<20 m depth) and 3–6% of the offshore (>20 m depth) marine waters in Puget Sound, ranging from southern Puget Sound to the Canadian border. Results from the WDFW aerial seabird surveys suggested that the density of some species, including Western Grebes (Aechmophorus occidentalis), has declined over the last two decades (Bower, 2009; Evenson, 2010; Vilchis et al., 2014). However, the cause(s) of these declines and the effects of environmental drivers on seabird density remain largely unknown. To complement the WDFW seabird survey both spatially and temporally, and to establish further baseline monitoring of local seabird species occurrence and abundance in winter months, the Seattle Audubon Society initiated the shore-based Puget Sound Seabird Survey (PSSS) in 2007. This program is unique in Puget Sound, in that volunteers monitor study sites in nearshore habitats monthly, from late fall to early spring (October–April). The October–April period was chosen because this is the window of greatest seabird abundance and diversity for this ecosystem. This survey also represents a good example of a scientifically rigorous citizen-science effort because survey effort is quantified and volunteers are trained annually and are the subject of ongoing validation studies to quantify biases, such as species misidentification.

Recent research has demonstrated that rigorous statistical models can be applied to volunteer-based surveys, yielding a relatively large impact, particularly when agency or industry-led data collection efforts are limited (Thorson et al., 2014). The primary objective of our analysis was to apply spatiotemporal models to data from the Puget Sound Seabird Survey to (1) evaluate relative hotspots of occurrence over the period 2007–2014, (2) evaluate temporal trends in occupancy over this period, and (3) establish a baseline for future monitoring in the region. These spatial and temporal estimates of occupancy may also be useful to refine the list of indicator species used to quantify ecosystem change or restoration progress.


PSS survey data collection

Beginning in October 2007, teams of 2–4 volunteer birdwatchers were trained by Seattle Audubon staff to collect data on birds in the nearshore environment of Puget Sound. Though the species encountered includes waterfowl, we collectively refer to all species as ‘seabirds.’ Each observer team was responsible for monthly surveys (October–April) at selected sites. Many of the seabird species in the region overwinter in Puget Sound, and are of highest abundance in late fall—early spring. The PSSS survey sites were selected non-randomly due to dependence on public access (parks, beach access), but they were selected to be spaced at least 1.6 km apart. Observer teams recorded all species present out to 300 m from shore for a minimum of 15 min, but some site visits lasted up to 60 min. To minimize the variability of weather conditions, tidal stage, and the risk of double counting birds at multiple survey sites, volunteer teams completed their monthly surveys on the same date within a specific four-hour window (two hours on either side of daylight high tide) on the first Saturday of each month. In each subsequent year of surveys, we added sites to cover parts of northern and southern Puget Sound. For this study, we limited our analysis to 62 sites with at least 15 visits (Fig. 1; Table 1). Additional details on the survey and monitoring, as well as additional maps can be found on the PSSS website, www.seabirdsurvey.org.

Figure 1: Minutes of sampling effort recorded (across all observer pairs and months in the Puget Sound Seabird Survey that are included in our analysis), 2007–2014.
Table 1: Name, latitude, and longitude of the 62 sites included in our analysis.
Site Lat (°N) Lon (°W) Site Lat (°N) Lon (°W)
1. 60th St Viewpoint 47.6723 122.4062 32. Mee Kwa Mooks 47.5637 122.4070
2. Alki Beach 47.5784 122.4144 33. Mukilteo State Park 47.9478 122.3071
3. Boston Harbor 47.1396 122.9029 34. Myrtle Edwards Park 47.6268 122.3775
4. Brace Point 47.5152 122.3964 35. Narrows Park 47.2671 122.5641
5. Brown’s Point 47.3058 122.4443 36. Normandy Beach Park 47.4116 122.3401
6. Burfoot County Park 47.1310 122.9046 37. North Redondo Boardwalk 47.3507 122.3238
7. Carkeek Park 47.7125 122.3796 38. Olympia waterfront 47.0582 122.9020
8. Cromwell East 47.2709 122.6110 39, Owens Beach Pt Defiance 47.3128 122.5280
9. Cromwell West 47.2714 122.6191 40. Penn Cove Pier 48.2228 122.6883
10. Dash Pt State Park 47.3204 122.4141 41. Penrose State Park 47.2601 122.7450
11. DeMolay Boys Camp (E) 47.2777 122.6662 42. Pier 57 47.6062 122.3429
12. DeMolay Boys Camp (W) 47.2775 122.6668 43. Pier 70 47.6149 122.3573
13. Discovery Park West 47.6674 122.4227 44. Point No Point 47.9122 122.5265
14. Dumas Bay Park 47.3263 122.3853 45. Pt Wilson 48.1441 122.7538
15. Duwamish Head 47.5954 122.3876 46. Purdy Spit South 47.3817 122.6348
16. Edmonds north 47.8114 122.3891 47. Raft Island north 47.3318 122.6700
17. Edmonds south 47.8033 122.3947 48. Raft Island south 47.3261 122.6675
18. Elliott Bay Water Taxi Pier 47.5898 122.3800 49. Richmond Beach 47.7636 122.3858
19. Fox Island Fishing Pier 47.2287 122.5898 50. Ruston Way 47.2948 122.4990
20. Frye Cove County Park 47.1152 122.9643 51. Saltwater State Park 47.3728 122.3249
21. Golden Gardens 47.6928 122.4056 52. Seahurst Park 47.4781 122.3638
22. Howarth State Park 47.9642 122.2407 53. Sinclair Inlet 47.5398 122.6621
23. Jack Hyde Park 47.2758 122.4622 54. South Redondo Boardwalk 47.3434 122.3328
24. Kayak Point State Park 48.1373 122.3668 55. The Cove 47.4428 122.3563
25. Kopachuck 47.3101 122.6874 56. Thea’s Park 47.2620 122.4398
26. Les Davis Pier 47.2836 122.4813 57. Three Tree Point 47.4522 122.3792
27. Libbey Beach County Park 48.2322 122.7668 58. Titlow Beach 47.2469 122.5536
28. Lincoln Park 47.5263 122.3949 59. Tolmie State Park 47.1209 122.7761
29. Lowman Park 47.5403 122.3974 60. Totten Inlet 47.1540 122.9645
30. Luhr Beach 47.1008 122.7272 61. West Point north 47.6624 122.4335
31. Magnolia Bluff 47.6313 122.3954 62. West Point south 47.6610 122.4330
DOI: 10.7717/peerj.704/table-1

Species selection

Over the first seven years of the PSSS (the most recent ending in spring 2014), observer teams recorded 75 unique seabird species. While many of these species may be useful as indicators of various ecosystem processes or human impacts, we focused our analysis on 18 species that have previously been identified as useful seabird indicator species in the region (Table 2; Pearson & Hamel, 2013) because of their relative abundance and dependence on the marine waters of the Puget Sound (Gaydos & Pearson, 2011). These species can be aggregated into five distinct groups: alcids, cormorants, grebes, loons, and waterfowl. Some of the species breed locally in Puget Sound, while others are transient in the Sound, breeding elsewhere (Table 2). Similarly, the species represent a range of diets and behaviors (Pearson & Hamel, 2013), from piscivores (alcids, loons, grebes, cormorants) to omnivores (seaducks and other waterfowl).

Table 2: The 18 species included in our analysis of the Puget Sound Seabird Survey. Rows in bold represent species that breed locally (in Puget Sound).
Common name Scientific name Group
Common murre Uria aalge Alcids
Marbled murrelet Brachyramphus marmoratus Alcids
Pigeon guillemot Cepphus columba Alcids
Rhinoceros auklet Cerorhinca monocerata Alcids
Brandt’s cormorant Phalacrocorax penicillatus Cormorants
Pelagic cormorant Phalacrocorax pelagicus Cormorants
Horned grebe Podiceps auritus Grebes
Red-necked grebe Podiceps grisegena Grebes
Western grebe Aechmophorus occidentalis Grebes
Common loon Gavia immer Loons
Pacific loon Gavia pacifica Loons
Red-throated loon Gavia stellata Loons
Brant Branta bernicla Waterfowl
Bufflehead Bucephala albeola Waterfowl
Common goldeneye Bucephala clangula Waterfowl
Harlequin duck Histrionicus histrionicus Waterfowl
Surf scoter Melanitta perspicillata Waterfowl
White-winged scoter Melanitta deglandi Waterfowl
DOI: 10.7717/peerj.704/table-2

Statistical modeling

For each species, we constructed matrices of presence–absence, dimensioned by the number of unique month-year combinations (t = 49) and sites (n = 62). Sites that were not visited during a given month were treated as NA values. We constructed a spatial occupancy model separately for each species to incorporate spatial patchiness as well as annual and seasonal variation. The model describing the probability of species presence can be represented as zi,jBer(ϕi,j), where zi,j represents the unobserved presence–absence (1, 0), and logit(ϕi) = BXi + ETi + ε, where ϕi represents the site-specific occupancy probabilities at time i, Xi represents a matrix of covariates (Intercept, Month, Month2, Year, Y ear2), B represents a vector of estimated coefficients (shared across sites and time periods), E represents a linear offset coefficient for sampling effort (Ti), and ε represents a vector of spatially correlated random effects. We included time spent (Ti in minutes, ranging from 15 to 60) as a measure of effort to account for the higher chance of recording a species present during longer visits. The spatially correlated random effects are assumed to have the distribution ε ∼ MV N(0, Σ). For simplicity, we modeled the covariance matrix Σ as an exponential covariance function, Σi,j = σ2Ii,j + τ⋅exp(−di,j/γ), where I represents an identity matrix, di,j is the Euclidian distance between sites i and j, and the scaling parameters (τ, γ) control how quickly covariance decays as a function of distance (Banerjee, Gelfand & Carlin, 2005; Ward et al., 2012). Our model could be modified to include more complex covariance functions (Cressie & Wikle, 2011) or spatial random effects that also vary temporally (Shelton et al., 2014). Because our model also includes an observation error component, however, we chose to make these spatial deviations temporally constant. The observation model, linking latent unobserved states (zi,j) to data (yi,j) can be written as yi,j|zi,jBer(pzi,j) (Royle & Kery, 2007), where p represents the probability of detection when a species is present.

All Markov Chain Monte Carlo (MCMC) estimation was conducted in R and JAGS (Plummer, 2003; R Development Core Team, 2014), using the R2jags package (Su & Yajima, 2014). We ran five parallel MCMC chains for each species, with a burn-in of 100,000 draws and additional sampling of 50,000 MCMC draws. Trace plots were used to visually assess convergence, and the Gelman–Rubin statistic (Gelman & Rubin, 1992) was used to quantify successful convergence. Not surprisingly, the only parameters that did not successfully converge (potential scale reduction factor >1.05) were several latent states (z) at sites that were not visited by observers in certain months. For the purposes of visualizing predicted hotspots of occupancy in Puget Sound, we used our model output to generate predictions (spatial maps, temporal trends) of species occupancy for a standardized 15 min survey. In addition to making these predictions for each of the 18 species included in our analysis, we attempted to identify hotspot areas, or the sites whose estimated occupancy probability was above the upper quartile (75%) across all sites. Finally, we generated specific occupancy probabilities for the five seabird groups: alcids, cormorants, loons, grebes, and waterfowl. For each group, the probability of occupancy for a group (corresponding to any species from that group being present) was calculated as 1i=1sp1ϕi.


Our species occupancy maps reveal localized hotspots of occurrence in Puget Sound for some alcid and cormorant species (rhinoceros auklet, pelagic cormorant, Brandt’s cormorant (Fig. 2) as well as for loons and some waterfowl species like harlequin ducks (Fig. 3)). The individual species maps show that some species are ubiquitous in all nearshore habitat (horned grebes, goldeneyes, scoters), while others have a much more patchy distribution of occurrence (loons, rhinoceros auklets, pigeon guillemots). These patterns become even more apparent when we examine the sites in the upper quartile (75–100%) of the estimated occupancy probabilities across sites (Figs. 4 and 5). Some maps of very rare or very common species may not be informative, but areas of high bird density become more apparent when our estimated occupancy probabilities are calculated by group (Fig. 6). For example, each loon species in the survey is relatively rare (Fig. 3), but the aggregated spatial distribution of all loons shows several patches of high and low occurrence, with the highest density of occurrence in the central-south Puget Sound (Fig. 6).

Figure 2: Estimated probability of occurrence for the 62 sites included in our analysis. Presented estimates are for alcids, cormorants, and grebes in December 2013. The color scale used to represent occurrence probabilities ranges from 0 (a species is not present) to 1 (occurrence is 100%).

The 18 species included in our analysis showed a range of seasonal variation, with waterfowl species (bufflehead, common goldeneye, surf scoter) and grebes varying the most and peaking in December–January (Fig. 7). Although most of the 18 species had relatively small variation over the 7-month survey period, several species exhibited monotonic increases (pelagic cormorant, pigeon guillemot). Of the 18 species, the probabilities of trends in occurrence being positive over the 7-year survey were greater than 80% for 14 species (Fig. 8). Western grebes, white-winged scoters, and brants showed relatively strong negative trends in occurrence (probabilities of negative trends >99%, 84%, 79%, respectively).

The 18 species in our analysis represent a gradient of occurrence probabilities and trends over space. Several species from each group were relatively rare in central and south Puget Sound; the rarest species included two of the alcids (common murre, marbled murrelet), western grebes, all three loon species, and three of the waterfowl species (brant, harlequin duck, white-winged scoter; Figs. 2, 3 and 8). In contrast, horned grebes and three different waterfowl species (bufflehead, common goldeneye, surf scoter) were the most widely occurring (Fig. 8).

Figure 3: Estimated probability of occurrence for the 62 sites included in our analysis. Presented estimates are for loons and waterfowl in December 2013. The color scale used to represent occurrence probabilities ranges from 0 (a species is not present) to 1 (occurrence is 100%).
Figure 4: Estimated hotspots of occurrence for the 62 sites included in our analysis, defined as probabilities in the upper quartile (75–100%) across sites (Fig. 2). Presented estimates are for alcids, cormorants, and grebes in December 2013. The color scale used to represent sites in the upper quartile is red (>75%) or white (<75%).
Figure 5: Estimated hotspots of occurrence for the 62 sites included in our analysis, defined as probabilities in the upper quartile (75–100%) across sites (Fig. 2). Presented estimates are for loons and waterfowl in December 2013. The color scale used to represent sites in the upper quartile is red (>75%) or white (<75%).
Figure 6: Aggregated probabilities of occurrence for each of the five groups in our analysis, as well as for all 18 species. For groups, these represent the probability of seeing any bird that is a member of that group; for all species, these represent the probability of seeing at least 1 bird (of the 18 species in our analysis). Estimates are shown for December 2013. The color scale used to represent occurrence probabilities ranges from 0 (not present) to 1 (occurrence is 100%).
Figure 7: Estimated median probabilities of occurrence by month. Estimates are shown for the most recent year (October 2013–April 2014). Estimates for November, January, and March are not shown.
Figure 8: Estimated probability of occurrence in the 2013–2014 seabird survey (with 25%, 50%, 75% intervals), percent change in the probability of occurrence from 2007 to 2013 (25%, 50%, 75% intervals), and the probability of the annual rate of change from 2007 to 2013 has been positive. All data (2007–2013) are used to estimate intra- and inter-annual trends.


Analyses that incorporate both spatial and temporal variation are becoming increasingly common in ecology. These types of analyses are widely applicable to virtually any type of observed data, from presence–absence to continuous observation measurements (Johnson et al., 2013; Shelton et al., 2014). Incorporating spatially structured random effects introduces a layer of statistical complexity that is warranted in many cases because predicted density estimates (both in space and time) are more precise (Thorson et al., in press).

Spatially-structured citizen-science datasets have been used at a large spatial scale, particularly in quantifying shifts in phenology linked to climate. One of the most frequently documented changes by citizen-science efforts has been shifts in breeding seasons (Hitch & Leberg, 2007; Hurlbert & Liang, 2012; Mayer, 2010). Spatially-structured statistical models have been fit to these types of datasets to improve estimates of trends (Hurlbert & Liang, 2012; Thorson et al., 2014), but few analyses have applied spatiotemporal models to data from citizen-science efforts to identify hotspots or areas of conservation concern at a fine spatial scale. Citizen-science programs, such as the Puget Sound Seabird Survey data analyzed here, offer a unique opportunity because both the temporal and spatial scales of data collection are much finer than national (Breeding Bird Survey) or regional (WA Department of Fish and Wildlife) efforts. If volunteer-driven science can result in relative indices of occurrence or abundance, it provides an extremely cost-effective approach for identifying local areas of risk (Hass, Hyman & Semmens, 2012) or potential hotspots of diversity that may be useful in conservation planning (e.g., establishing reserves) or permitting activities.

Using citizen-science data—either to complement existing datasets or to fill in data gaps when other surveys are absent—is particularly important for areas or habitats at risk. The PSSS may be a good model for adopting similar citizen-science efforts, either in other regions or for other applications that may also be used to study food webs—examples include monitoring water quality and the spread of invasive species (Silvertown, 2009). In addition to the historic decline of many seabird species (Bower, 2009), there are a number of other human impacts that have caused shifts or reorganization in the prey base (Blight et al., 2014) or competitors of seabirds (Harvey, Williams & Levin, 2012). These impacts could include effects of overfishing or bycatch (and associated impacts of derelict fishing gear; Good et al., 2009), climate change, toxins (Good et al., 2014), habitat loss (Raphael et al., in press), altered freshwater flow regimes, and the recovery of many top predators to historic levels (pinnipeds, harbor porpoise, bald eagles).

Although many seabird species in the Puget Sound region are thought to be depleted relative to abundances in the 1960s–1970s (Bower, 2009; Vilchis et al., 2014), our results present a more optimistic picture for a number of species over the last decade. Of the 18 species included in our analysis, we found strong support for 14 having increasing probabilities of occurrence, and these results are in agreement with recent studies in the region (for example, nesting surveys suggest Rhinoceros auklets are also increasing; Pearson et al., 2013). Many of the species that are occurring more frequently are those that breed in the region (Table 2). In the list of indicator species compiled by Pearson & Hamel (2013), some of these species (scoters, murrelets) were declining significantly when considering trends based on total abundance, so it is possible that species in decline have a less aggregated spatial distribution, resulting in their probability of detection increasing. Another possibility is that the PSS survey measures occurrence close to land, while trends from other surveys may represent slightly different habitats. Of the species not increasing, one species provided weak support for declining occurrence (white-winged scoter), and three species provided strong support for continued declines in occurrence (brant, western grebe, red-necked grebe). These three species in decline are also concerning because they are already rarely seen species in the PSSS data (Fig. 8).

There is no obvious mechanism for why the three declining species in our analysis exhibit a declining trend in occupancy, but some of these declines may be occurring at breeding colonies (not in Puget Sound) or resulting from shifts in prey abundance in the Puget Sound region. Some recent evidence suggests that there have been long-term changes in the base of the food web of the Salish Sea (Blight et al., 2014), and over-wintering seabird species that rely on forage fish are declining (Vilchis et al., 2014). Another mechanism that may also be related to shifts in the spatial distribution of prey is the large-scale shifts in seabird species’ ranges. For example, Wilson et al. (2013) used citizen-science data to show that western grebes appear to have shifted out of Puget Sound region to the southern end of the California Current. Our estimated declines in occupancy over the last seven years are largely in agreement with a continued decline in the occurrence of western grebes in the region. Like western grebes, brants and white-winged scoters over-winter in Puget Sound but breed elsewhere, and thus may be affected by threats in other ecosystems. Though the exact mechanisms responsible for these trends are not known, our trend estimates may be useful in prioritizing monitoring efforts or refining existing marine bird or ecosystem indicators in the region (Kershner et al., 2011; Pearson & Hamel, 2013).

Although the focus of our volunteer-driven surveys in the Puget Sound region are focused on identifying spatial hotspots and improving estimates of annual trends, citizen-science efforts like the PSSS may provide additional valuable baseline monitoring. The 7-year dataset analyzed here provides both a baseline for seabird monitoring in 2014, and also allows us to do a retrospective analysis of trends over this time period. For example, in the event of an oil spill in the region, PSSS data could provide 7 + years of pre-spill information on seabird distribution and abundance for comparison. Having a 7-year period as a baseline instead of just a single year is useful in that the year-to-year variability can be quantified. Such citizen-science efforts may also be scalable to different types of data collection that also involve spatially structured threats to marine ecosystems such as harmful algal blooms, ocean acidification, and fisheries activities.