WILDLIFE HOTSPOTS IN THE CALIFORNIA CURRENT SYSTEM

Transcription

1 WILDLIFE HOTSPOTS IN THE CALIFORNIA CURRENT SYSTEM TECHNICAL REPORT TO THE RESOURCES LEGACY FUND FOUNDATION Nadav Nur, Jaime Jahncke, Mark Herzog, Julie Howar, John A. Wiens, Diana Stralberg PRBO Conservation Science 3820 Cypress Drive, #11 Petaluma, CA, USA January PRBO Conservation Science Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 0

2 EXECUTIVE SUMMARY PRBO Conservation Science (PRBO) conducted analyses and developed predictive models to identify areas that support aggregations of foraging seabirds ( hotspots ) and to inform Marine Spatial Planning (MSP) in the California Current System (CCS). We developed habitat associations for 16 species of seabirds using information from at-sea observations of individual species collected over an 11-year period ( ). Environmental covariates reflected both spatial and temporal variation and included bathymetric variables, including proximity to oceanic habitat types (e.g., continental shelf and slope) and remote-sensed satellite data (sea-surface temperature, chlorophyll-a, sea-level height). At-sea surveys were conducted by numerous agencies and monitoring programs and extended from north of Vancouver Island to the US/Mexico border, extending out 600 km from the coast. We developed single-species predictive models using bagged decision trees, one type of machine-learning algorithm. Bathymetric variables, including proximity to land, were often important predictive variables. Oceanographic variables derived from remotely sensed data were generally less important. Model predictions were applied to the entire California Current for 4 months (February, May, July, October) as a proxy for seasons in each of 11 years. Single-species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas ( core areas ) to individual species, and (3) predicted persistence of hotspots among years. Potential hotspots were well aligned with currently protected areas (e.g., National Marine Sanctuaries). In addition, we found potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank) and Southern California (adjacent to the Channel Islands) that are not included in current protected areas. Prioritization and identification of multi-species hotspots will depend on which subset of species is of highest management priority. Modeling hotspots on a broad spatial scale, as in this study, provides a valuable contribution to MPA planning, but we also advocate incorporation of other considerations (other biota, economic constraints, specific threats, etc.) into modeling analyses applied to the California Current. Next steps include a concerted outreach effort to share our results with federal and state agencies involved in marine spatial planning and a regional analysis focused on the northern California coast. In view of the likelihood that changes in climate and oceanographic conditions will have major impacts on coastal marine ecosystems, it is also important to conduct a similar analysis that incorporates climate change projections to identify future hotspots and to assess the vulnerability of areas that are currently under protection. Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 1

3 INTRODUCTION We investigate the relationships between seabird foraging aggregations and habitat features to identify areas of high concentrations of marine wildlife, or hotspots, so as to inform Marine Spatial Planning (MSP) in the highly productive California Current System (CCS). The goals of MSP are to determine how to best use coasts and oceans sustainably, to decrease governance conflicts among agencies, and to achieve long-term benefits to society (Ehler and Douvere 2009). However, effective MSP depends on sound scientific information to achieve its longterm goals. The use of strategies such as establishment of marine reserves or marine protected areas (MPAs) to advance Marine Spatial Planning has been widely viewed as an effective way to address threats and restore ecosystems and populations (Boersma and Parrish 1999, Worm et al. 2003, Norse et al. 2005). Although some progress has been made in establishing protected areas in coastal regions (BirdLife International 2009), protection for pelagic ecosystems is currently inadequate (Game et al. 2009) and the need for pelagic marine reserves is becoming increasingly apparent (Hooker and Gerber, 2004:27). Development of a scientific basis for the establishment of MPAs has been lagging compared to other habitats (Hyrenbach et al. 2000, Halpern 2003, Cañadas et al. 2005). The identification and protection of foraging areas has been deemed most important to conserve and maintain populations of marine predatory species, such as seabirds (Hooker and Gerber 2004). Focusing on seabirds can benefit other top predators, many of which are threatened (IUCN 2008); seabirds can also serve as indicators of ecosystem condition, and thus provide benefit to other components of the ecosystem (Hooker and Gerber 2004, Harris et al. 2007, Durant et al. 2009, Ojeda-Martínez et al. 2009). We conducted research to inform MSP and to help guide the design of MPAs along the California Current System. This large ecosystem stretches from British Columbia to Baja California and extends out from coast for hundreds of kilometers (Fig. 1). The CCS supports important populations of marine wildlife, including many migratory bird species, as well as valuable commercial fisheries. Recently, the CCS has suffered from depleted fisheries and climate and oceanographic anomalies, highlighting the need to protect important foraging areas for wildlife. However, such areas within the CCS have not yet been identified in a comprehensive and objective manner. In particular, we cannot rely on direct observations of seabird aggregations to locate these hotspots; many areas in the CCS have received spotty survey coverage or none at all. Marine birds and mammals aggregate at predictable locations or hotspots where food availability is high (Hunt et al. 1999, Cañadas et al. 2005, Suryan et al. 2006, Piatt et al. 2006). Areas of aggregation in part reflect bathymetry as well as proximity to the continental shelf, shelf break, shelf slope, and deep water (Hyrenbach et al. 2000, Yen et al. 2004). Locations of such hotspots may also reflect currents and eddies (Yen et al. 2006, Hyrenbach et al. 2006, Ballance et al. 2006), which act to concentrate prey and are reflected in measurements of sealevel height (or, as it is sometimes referred to, sea-surface height). Additionally, seabird aggregations have been shown to reflect primary productivity (indicated by chlorophyll Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 2

4 concentration) sea-surface temperature, and gradients in sea-surface temperature (Yen et al. 2005, Ainley et al. 2009, Garthe et al. 2009, Tremblay et al. 2009). Although sea-level height, sea-surface temperature and chlorophyll concentration are captured through systematic satellite monitoring programs, these variables exhibit temporal and fine-scale spatial variability that makes their use in identifying predictable hotspots problematic (Hyrenbach et al. 2000). Nevertheless, there may be some geographic areas that demonstrate predictably high concentrations of chlorophyll or are characterized by sea-surface temperatures or sea-level heights associated with seabird aggregations (Ainley et al. 2009). Here we describe the development and application of habitat-association models for 16 seabird species based on 11 years of survey data from multiple at-sea monitoring and research programs that, when combined, span the geographic range of the CCS. We provide singlespecies predictive maps of abundance that cover the full range of the CCS, whether previously surveyed or not. We then combine predictions across the 16 species in three ways to highlight different aspects of potential seabird areas of aggregation, all of which are relevant for the MSP process. These distinct criteria for determination of potential hotspots are: (1) overall abundance among species, (2) importance of specific areas ( core areas ) to individual species, and (3) predicted persistence of hotspots among years. Because the modeling is conducted over a large area, it presents an opportunity to consider not only the identification of individual MPAs, but also a regional network of such areas spanning the entire California Current (Sala et al. 2002, BirdLife International 2009). The modeling results can thus inform MPA designation, Important Bird Areas designation (under the auspices of BirdLife International, 2009), and, most broadly, marine spatial planning (Ehler and Douvere 2009). METHODS Overview of Data Collection and Processing Three types of data were used in model development. Seabird observation and survey-transect data were represented by points, corresponding to the mid-point of a survey bin. Distances from the survey points to selected terrestrial and bathymetric boundaries were calculated. Finally, satellite data, in the form of rasters (grids), provided information on physical and biological marine conditions over a large area. Survey Transects and Animal Observations We restricted the geographic coverage of analysis to between 52 degrees and 30 degrees N latitude, and between 0 and 600 km from the mainland (Fig. 2). The northern boundary corresponded to the approximate northern edge of the California Current (Fig. 1); the southern and offshore boundaries were dictated by the extent of survey coverage (Fig. 2). Seabird observation data used for model development were obtained from several research and monitoring programs: California Cooperative Oceanic Fisheries Investigation (CalCOFI), Sardine Survey and California Current Ecosystem Study (National Oceanic and Atmospheric Administration [NOAA]), CSCAPE and ORCAWALE (NOAA), Line P (Canadian Wildlife Service [CWS] of Environment Canada [EC] and Fisheries and Oceans Canada), and National Marine Fisheries Service (NMFS) Rockfish Surveys (NOAA). These are summarized in Table 1; Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 3

5 coverages of each are shown in Figure 2. Areas covered varied by cruise, with some covering the entire west coast from California to Vancouver Island and others restricted to smaller areas. Data used were collected from October 1997 to November 2008 and included GPS position, visibility, and species, number, and behavior of animals observed. Seabird-survey data were available prior to October 1997 but Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite data were not, precluding the use of the earlier data. Each linear survey transect was divided into 3-km segments or bins. Bin center points were used for analysis of distance from land, distance to isobaths, bathymetry, sea-surface temperature (SST), chlorophyll concentration (CHL) and sea-surface height (SSH). 58,966 survey bins were used in the statistical analysis, for all cruises combined. The analysis controlled for the width of the survey transect (and thus the area surveyed); generally, the width was 300 m. Animal observations were summarized within a 3-km bin; counts of individual or groups of animals of a single species and behavior type were summed to a total number of animals of that type observed in the bin. The surveyed area per bin was 0.9 km 2 in over 80% of the cases. We used bins of 3 km in length, because previous analysis indicated generally low levels of autocorrelation among adjacent bins at that scale (Yen et al. 2004); however, autocorrelation is not a major concern for predictive models (Ferguson et al. 2006). Distance Distances from the bin center points to the mainland and to islands were calculated in ArcMap using polyline shapefiles of the terrestrial boundary of western North America provided by California Department of Fish and Game (for California) and ESRI (for North America). The terrestrial boundary was divided into mainland (including Vancouver Island) and island boundaries. Distances from the points to the 200-m, 1000-m, and 3000-m depth isobaths were calculated using data derived from bathymetry data obtained from the General Bathymetric Chart of the Oceans (GEBCO). These three isobaths have been used in similar analyses (e.g., Yen et al. 2005): 200-m depth indicates the shelf break, 1000-m the shelf slope, and 3000-m the ocean floor. Bathymetry Bathymetric data were obtained in raster form from GEBCO with global coverage and a cell size of 1 x 1 arc-minute (approximately 1.8 x 1.8 km). Depth (m) at each bin center was obtained by overlaying the bin center point data on the bathymetric raster and sampling the depth values at each point. We also developed focal cell statistics for depth. For each focal cell, we calculated the mean, minimum, maximum, and standard deviation of depth based on the center cell (i.e., focal cell ) and the 8 adjacent cells. Thus, each focal cell was located within a group of 9 cells (3 x 3 cell), approximately 29 km 2. We also calculated a Contour Index that reflects rugosity (roughness) of the bottom within this same area of 9 cells. Contour Index was defined as [(Max depth Min depth)/max depth * 100]; it varied from 0 to 100%. Satellite Data Two sources of sea-surface temperature data were used to minimize missing data: Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra, and Advanced Very High Resolution Radiometer (AVHRR) Pathfinder. We used daytime temperatures, which were Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 4

6 obtained in composite form over consecutive 8-day periods to improve data coverage. Original spatial coverage was global with a cell size of approximately 4 x 4 km (for MODIS; AVHRR cells were slightly larger). AVHRR quality data were also available; a derived SST grid was created by selecting cells with a quality of 5 or greater (on a scale of 1 to 9, 9 being highest quality). Temperature ( C) at each bin center was obtained by using a Visual Basic script to select the bin center point data that corresponded to the dates of the 8-day composite SST raster. To provide consistent SST metrics in the analysis we used AVHRR data where they were available; where unavailable, we used MODIS as follows. For cells that had both MODIS and AVHRR data for the same time period, we regressed the AVHRR metric (mean, minimum, etc.) on the MODIS value. The regression equation provided unbiased estimates with high predictive ability relating MODIS and AVHRR. We then substituted the MODIS-derived value for SST where the AVHRR value was not available. Focal statistics for MODIS and AVHRR SST metrics (mean, minimum, maximum, and standard deviation) were calculated for each raster cell using a 3 x 3 cell window (approximately 144 km 2 ), similar to the depth calculations (see above). AVHRR SST SD could not be adequately predicted by MODIS SST SD. Hence, SST SD was not included in the statistical analysis. However, because AVHRR SST Average, SST Max, and SST Min could be well predicted by the corresponding MODIS metrics, we included these three metrics as well as SST Max Min, intended as an index of SST variability. Chlorophyll concentration data were obtained from the SeaWiFS satellite. Data were provided in composite form over consecutive 8-day periods to improve data coverage. Original spatial coverage was global, with a cell size of approximately 9 x 9 km. CHL (mg/m 3 ) at each bin center was obtained by using a Visual Basic script to select the bin center point data corresponding to the dates of the 8-day composite CHL raster and using those points to sample the raster. Where SeaWiFS CHL data were unavailable, we used data from MODIS, processed as we did for SST (i.e., used a regression equation to predict SeaWiFS CHL values on the basis of MODIS CHL values). Sea-surface height data were sourced by multiple satellites with the same groundtrack (including Topex/Poseidon, Jason-1 + ERS, Envisat). Data were provided in composite form over consecutive 7-day periods. Original spatial coverage was global, with a cell size of 0.25 x 0.25 degrees (approximately 27 x 27 km). SSH (cm) at each bin center was obtained using a Visual Basic script to select the bin center point data that corresponded to the dates of the 7-day composite SSH raster and using those points to sample the raster. Data Processing for Predictions Physical and biological data for the modeling process were selected spanning 1997 to For prediction we only used the months of October, February, May, and July. That is, 45 months were used for prediction, from October 1997 to October October represented fall and occurs within the Oceanic Season; February represented the winter and falls within the Davidson Current Season; May represented spring and occurs within the middle of the Upwelling Season; July represented summer and falls toward the end of the Upwelling Season (Ford et al. 2004). Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 5

7 Data used for predictions were similar to that of analysis except that only four months were used, and the satellite data were based on monthly average values rather than 8-day values. Monthly data were available for AVHRR SST, MODIS SST, SeaWiFS CHL, MODIS CHL). Monthly data were not available for SSH; instead, monthly SSH was estimated by applying a weighted average to the 7-day datasets that made up each month. After processing, projection information, spatial extent and cell size were standardized for all data; the universal projection was Geographic WGS 84; boundaries were limited to -137 W, 30 N, -117 W, 52 N; and the cell size was resampled to the standard cell size of MODIS SST data (approximately 4 x 4km). For each predictive map (by month, year, and species) we generated 86,876 predicted cells. Single Species Statistical Analyses For most species, only birds seen foraging or on the water were included in the analyses; those observed flying were not included. However, for all gulls, terns, kittiwakes, albatrosses, and storm-petrels, we included flying birds as well as those foraging or on the water. These species are ones that may be foraging (hunting) while flying (Jahncke et al. 2008). Bagged Decision Tree Analysis Decision Tree analysis is a machine-learning modeling tool that recursively partitions feature space into rectangular regions. Within each region a simple model can be fit (Breiman et al. 1984, Hastie et al. 2009). However, simple decision trees are highly unstable to the data at hand; for example, different training data can give very different decision tree results. Bagging, or bootstrap aggregation, enhances the accuracy of the decision tree, by using a large number of bootstrapped datasets (an ensemble ; see Efron and Tibshirani 1993) and performing a decision-tree analysis on each dataset (Breiman 1996, Dietterich 2000, Hochachka et al. 2007, Hastie et al. 2009). The predictions from each decision tree are then averaged together to produce a final prediction. Bagged Decision Tree (BDT) analysis and similar methods are discussed in an ecological context by Elith et al. (2008). Here we used the recursive partitioning package (rpart) within the R statistical programming language (version , R Development Core Team 2009) for the decision-tree analysis. Custom code developed by D. Fink was used to implement the bagging, prediction, model averaging, and model fit (Hochachka et al. 2007). BDTs were used to develop predictive models for each of the 16 species and then used to make predictions for the entire region of interest in the California Current. These predictions were temporally specific (by month and year). As a first step we partitioned 90% of the data for use during model building (i.e. training), leaving 10% available for testing. For each predictive model we used an ensemble of 500 bootstrapped datasets obtained from the training data. A priori tests with these data showed that after 500 datasets little additional little improvement was made to the predictive ability of the bagged ensemble. Given the count nature of our data, we assumed a Poisson distribution for the response variable within our BDT analysis. Results of BDTs, and similar methods, are robust to assumptions regarding autocorrelation among survey bins (Hastie et al. 2009). Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 6

8 Species selection Species selection was a two-step process. We first selected all seabird species that were observed in at least 300 bins (i.e., one or more individuals of the appropriate behavior codes were observed in the survey bin ). This minimum represents approximately 0.5% of all survey bins analyzed. There were 25 seabird species that met this criterion. For the second phase of species selection, we conducted Bagged Decision Tree analysis of the 25 species (described below). Sixteen species produced adequate predictive models and were retained (Table 2); nine species produced inadequate predictive models. Adequacy was determined in a two-part process. First, we examined the proportion of deviance explained in the test dataset (data not used to develop the predictive formulas). The proportion can vary from 0 to 1. If the proportion was > 0.4, the species was retained. If it was < 0.3, the species was dropped. For species for which the proportion of deviance explained was between 0.3 and 0.4, we examined two other diagnostics: (1) the ratio of test to training in terms of deviance explained (a satisfactory result was < 40% drop, in relative terms), and (2) a graph of deviance explained vs. spatial scale (Hochachka et al. 2007). Species that were dropped at this second phase of selection included Black-legged Kittiwake, Black-vented Shearwater, Cook s Petrel, Northern Fulmar, Pacific Loon, Pink-footed Shearwater, Pomarine Jaeger, Red Phalarope, and Rhinoceros Auklet (scientific names in Table 2). Variable selection We initially identified 30 potential predictor variables for inclusion in the modeling (Table 3). There was some redundancy among the full set of variables. Although variable redundancy is not a major concern for machine-learning methods such as BDT (Guyon and Elisseeff 2003), we reduced the variable set in order to improve interpretability of results. We did this in two ways: (1) examination of correlations among the predictor variables, and (2) carrying out initial Bagged Decision Tree analysis with the full set of 30 variables for all candidate species. Where several inter-correlated variables measured similar environmental attributes (e.g., different aspects of depth), those variables that demonstrated low explanatory power (as revealed with BDT analysis) were dropped. Thus, for example, the initial set of six depth variables (minimum, maximum, average, individual cell, and coefficient of variation of depth, and Contour Index) was reduced to two variables (minimum and average depth). Variables that were thought to be ecologically meaningful yet not highly correlated with other variables in the initial set of 30 variables were retained, whether or not they demonstrated high predictive power. Date Julian date (i.e., day of year such that 1 = 1 January) was included in a non-parametric fashion, just as all the quantitative variables were; thus, we made no assumptions regarding the shape of seasonal fluctuations in abundance for individual species. It is important to note that BDT allows for interactions, such as that of Julian date and latitude. An additional variable was transition date, the date at which the upwelling regime changes from a winter pattern to a spring pattern (Holt and Mantua 2009). This variable shows strong year-to-year variation (Holt and Mantua 2009). Although transition dates in the CCS show regional variation, we were not able to adequately characterize transition dates throughout the system. Instead, we relied on a single value based on upwelling and winds (Method 1 of Holt and Mantua 2009), obtained from data near the Farallon Islands in Central California (J. Jahncke, J. Roth, unpublished). Again, BDT Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 7

9 allows for an interaction between transition date and latitude, hence the effect of transition date in the analysis can vary with latitude. Use of a single transition date for the CCS in the BDT analysis may be an over-simplification, but it was not our intention to develop a detailed accounting of transition date itself, just to characterize how variation in abundance of the target seabird species was related to variation in year, date, and related variables. Hotspot Determination We used three criteria to identify potential hotspots of seabird aggregation. All three criteria were implemented at the scale of a single prediction cell (4 km x 4 km; see above). Standardized abundance, summed over all species Predicted detections were standardized for each species such that mean of detections = 0 and SD of detections = 1. We use the term abundance henceforth, but recognize that observations and predictions refer to individuals detected during surveys, which only provide an index of abundance. Standardization was implemented so that each species contributed equally to the composite results, at least in the initial phase. Otherwise, species with very high mean abundance would swamp less common species, though the latter may be of high conservation concern; species with high variance could also drive the composite results. We refer to this metric as summed standardized abundance, to emphasize that it is a multi-species metric and not a single-species metric. Important, core areas for individual species This index was calculated for individual species and then averaged over all species, as follows. For each species, we ranked all prediction cells according to their predicted abundance. We then identified the smallest set of cells that together constituted 25% of the species total abundance within the study region. These highest predicted density cells were considered part of a species core area and were scored 2. The next set of cells, based on predicted density, that made up 50% of the species total abundance but were not in the first core stratum were considered important shoulder areas, and were scored 1. All other cells received a score of 0. Importance was calculated for each species, by month and year, and a weighted-average was then calculated over all species as follows. The smaller the number of cells that made up the 25% core area for that species, the greater the weight. Thus, if species X required 100 cells to reach the 25% criterion while species Y required 1000 cells, each core cell for species X received 10 times the weight as each core cell for species Y. The weighting function was scaled to have mean = 1. We used this weighting function to equalize the contribution that each species had to the overall importance score. More specifically, for each species, the product of the weighting factor and the number of core cells was the same across species. Persistence of hotspots across years The persistence index was first calculated for individual species for each season (month). The number of years (out of 11) that a cell was in the top quantile of predicted abundance for that species was scored. We used two different quantiles to define the threshold: the 95 th percentile and the 99 th percentile; here we only show results for the 95 th percentile. The persistence score for a cell pertaining to the chosen quantile was then averaged over all species. Results were also averaged across months. Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 8

10 RESULTS Individual Species Results Sample sizes of observations (total number of individuals and number of bins with 1 or more individuals) for all 16 seabird species producing adequate models are shown in Table 4. Results of BDT analyses, species by species, are summarized in Table 5. The Proportion of Deviance explained in the training dataset for the set of predictor variables exceeded for all species except one (Leach s Storm-petrel, for which the value was 0.527) (Table 5). Also shown in Table 5 is Proportion of Deviance explained for individual variables, when the other 19 variables were included in the model. For example, the predictive model explained over 60% of the deviance in the training dataset for Black-footed Albatross. The best predictive variable for this species was distance to 1000 m isobath (10.6% of deviance explained), which reflects proximity to the shelf slope. Proportion of deviance explained for the 10 most important variables for each species are shown in the Table; results for the other 10 variables, for each species, are not shown. We emphasize that the predictive models themselves include all 20 variables. Important variables are shown in a more summarized form in Table 6. Latitude and Julian date were included in the set of top 10 variables (in terms of predictive ability) for every species. Distance to nearest land was in the set of top 10 variables for every species except Fork-tailed Storm-Petrel. Table 6 also tallies the number of variables that we consider to be strong predictor variables, defined as Proportion of Deviance explained >0.075 for that variable. Distance to nearest Land was most often included (8 species) as a criterion of predictor strength. Latitude was a strong predictor for 5 out of 16 species. Tied for third-most were Julian date and Sea-surface Height. Thus, SSH was in the set of top 10 variables for 12 out of 16 species, the most of the three remotely sensed oceanographic metrics. For Sabine s Gull, SSH was by far the best predictor variable (Table 5). In general, depth variables (average, minimum depth; contour index) were important predictors. Sooty Shearwater was the only species for which none of the three depth variables was important. Proximity to (or distance from) 200-m isobath, 1000-m isobath, and 3000-m isobath was also important for a high proportion of the species. Thus, proximity to depthrelated oceanic habitat types was a good predictor of species-specific abundance even when information on depth of the survey bin or its neighbors was already included in the model. Among the temporal variables, Year was not often important (i.e., not in the top 10 variables for that species). This may seem surprising, but seven other predictor variables also reflected annual variation (transition date and two each of SOI, PDO, and NGPO). Besides Julian date, of the eight variables that reflected temporal variation on a longer time scale (3 to 12 months), the NPGO value 4 to 6 months prior to the survey was the best predictor (Tables 5 and 6). Single-species predictive maps We created predictive maps of abundance for each of the 16 species. For each species, predictions were constructed for four months (February, May, July, October) in each of 11 Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 9

11 years ( ) plus October These species-specific, month-and-year specific predictions were then used in the multi-species Hotspot Determination (see below). To compare predicted distributions among species and seasons, we averaged predictions over the 11 years for each of the 4 months (12 years for October). For example, February and May predicted distributions for the entire California Current for Common Murre (Fig. 3) show differences between in the pre-breeding period (February) and the mid-breeding period (May). The San Francisco Bay area and the area north of Cape Mendocino to Cape Blanco appear as hotspots in both seasons. A small hotspot is also apparent in central Oregon (Heceta Bank). The predicted distribution of moderate to high density is more restricted (closer to the coast) in May, during the breeding season, compared to February. For Brown Pelican, we contrast predicted distribution in February with that in July (Fig. 4). Black-footed Albatrosses are generally absent from the California Current in winter (as predicted by the BDT model), so we only show predicted distribution for July for this species (Fig. 5A). A notable predicted hotspot for this species is west of the Olympic Peninsula. For Cassin s Auklet, we show predicted distribution for May, corresponding to the period of chickrearing (Fig. 5B). Cassin s Auklets demonstrate three zones of high predicted abundance: Northern Vancouver Island, San Francisco Bay region to southern Oregon, and southern California. The central Oregon hotspot apparent for Common Murres, and to a lesser extent for Black-footed Albatrosses, is absent for Cassin s Auklet. Overall, coastal regions are often areas of predicted hotspots, but in some cases (Black-footed Albatross, Brown Pelican), areas of high abundance extend up to 100 km off the coast. Hotspot Determination Summed abundance was determined for each month and year; results are shown for each of the four months, February, May, July, and October, averaged over all years (Fig. 6). Predicted hotspots of abundance are apparent for the Olympic Peninsula, northern-most California (near Cape Mendocino), coastal areas near the Golden Gate, Monterey Bay, and coastal areas of Southern California, especially south of Point Conception. Much of the Channel Islands region is predicted to be a hotspot. No hotspots are apparent more than about 90 km offshore. To illustrate areas reflecting the importance criterion (i.e., core and shoulder areas), we first show results for a single species, Sooty Shearwater, as an example (Fig. 7A). Several core areas are apparent, especially off the Olympic Peninsula, extending as far as southern Vancouver Island. Results averaged and weighted over all species are depicted in Figure 8B. These importance hotspots show overlap with the hotspots identified through the summed abundance criterion (Figs. 8A and 8B), as in San Francisco Bay and the adjacent coastal region. However, some areas appear as importance hotspots that were not as apparent using only summed abundance. This includes central Oregon somewhat north of Cape Blanco and extending to Heceta Bank. The area south of Cape Blanco extending to the California border is also an importance hotspot. Hotspots gauged by persistence are shown for an individual species, Cassin s Auklet (Fig. 7B). Areas of high persistence for this species include northern Vancouver Island and southern Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 10

12 California, especially areas near San Diego and the Channel Islands. Persistence calculations are also shown averaged over all 16 species and all 4 months (Fig. 8C). Again, there is substantial overlap between areas identified as highly persistent and those identified as having high summed abundance and/or high species importance (Figs. 8A, 8B, 8C). However, some areas show especially high persistence relative to others; for example, Central and Southern California score high on all-species persistence, but Oregon less so. DISCUSSION Although several studies have developed spatial predictive models for a few seabird species at finer, regional scales (e.g., Louzao et al. 2006, Ainley et al. 2009, Louzao et al. 2009), this is the first study to statistically model a large set of seabird species over a large geographic area. The identification and implementation of multiple criteria to identify hotspots of multi-species seabird aggregations has rarely been carried out (but see Harris et al. 2007). Important predictive features Our findings indicate that, at the broad spatial scale of the California Current, seabird hotspots are best predicted by bathymetric features, especially those related to ocean depth and proximity to or distance from land. As a result, there was marked consistency of predicted hotspots from season to season and year to year. Except for sea-surface height (which is correlated with strength and productivity of the CCS), oceanographic variables contributed little directly to the predictive models, Hyrenbach et al. (2000) identified three types of features to explain aggregations of pelagic predators: static bathymetric features, persistent hydrographic features (e.g., fronts occurring at predictable locations), and ephemeral hydrographic features (cf. Hooker et al. 1999). Our results point to the value of the first class of features in identifying hotspots, but do not rule out the second class of features as important, although perhaps not directly so. For example, there are likely geographic locations at which oceanographic features favoring seabird aggregations may predictably occur (Ainley et al. 2009). If so, the oceanographic variables may be important in explaining why some geographic locations are associated with seabird hotspots and others are not. The rankings of variables in the predictive models also reflect the spatial scale of the modeling. In explaining variation in abundance of individual species from British Columbia to Southern California, it is not surprising that geographically based variables predominate over variables such as Sea-surface Temperature or change in SST, which may explain variation in abundance at finer spatial scales (10 km or less). In fact, SST has often been found to be an important explanatory variable with respect to seabird distribution and abundance (Trembly et al. 2009), but such studies have been conducted at finer spatial scales than that of our analysis (e.g., Garthe et al. 2009). There was considerable variation among species in the importance of specific predictor variables. For example, distance to nearest land was a highly important variable for six species (Proportion of total deviance explained > 0.15). For other species (especially Fork-tailed Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 11

13 Storm-Petrel), however, this variable was of low predictive value. Other than Julian date, the only variable considered important for all 16 species was latitude. It is difficult to generalize across the 16 species, but SSH did appear to be important primarily for species that do not breed in the CCS (e.g., Sooty Shearwater, Sabine s Gull). The implication of among-species variability is that predicted hotspots for one species may not show high overlap with those for other species. The identification of multi-species hotspots as candidate MPAs will therefore depend on which set of species is being considered, which in turn will reflect management and conservation priorities. If these priorities reflect individual species (e.g., focusing only on those that are considered Threatened), then predicted hotspots will vary according to species chosen or to species weighting. Our results for seabirds also show some parallels with those found in studies of cetaceans in this region (Ferguson et al. 2006, Redfern et al. 2008, L. Ballance et al., unpublished). For example, habitat association models for delphinids in the eastern Pacific ocean (Ferguson et al. 2006) revealed the importance of offshore distance, latitude, SST, and CHL, in addition to several variables that we did not analyze (sea surface salinity, thermocline depth). There is therefore a good opportunity to synthesize information from cetacean predictive models with that on seabirds for the California Current. Predicted locations of hotspots The hotspots identified during this study occurred over the shelf along most of the western coast of the continental U.S. and southern Canada. These hotspots were relatively close to the coast, sometimes within 15 km, other times up to 80 km from the coast, as in the Olympic Peninsula in Washington. Individual species showed predicted hotspots 100 km or more from the coast in some months, but such areas were not apparent when predictions were synthesized across species using any of the three criteria. There was considerable overlap in areas identified by each of the three criteria. Thus, the identification of a predictive hotspot does not depend on a single criterion. Use of all three criteria provides a robust methodology for combining predictions across species. We utilized specific algorithms for identification of hot spots, but recognize that there is much room for improvement in the algorithms. Nevertheless, use of multiple criteria, rather than a single criterion, increases the robustness of our results, in comparison to the use of a single criterion, no matter how well developed it may be. There are several ways that predictions can be combined across the multiple criteria, a fruitful area for further work. Many of the predicted hotspots are currently within National Marine Sanctuary boundaries. These include the Cordell Bank, Gulf of the Farallones, and Monterey Bay National Marine Sanctuaries (NMS) in central California; Channel Island NMS in southern California; and Olympic Coast NMS in Washington. While current legislation may provide limited protection for marine wildlife, the National Marine Sanctuary System provides a legal framework that can be used in the future to develop a network of protected MPAs with greater protection than at present. Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 12

14 Our analysis also indicated predicted hotspots that are not currently protected. These areas of concern include the broader areas around the Channel Islands and the coastal area north of Cape Mendocino (northern California) to central Oregon (Heceta Bank), where there are currently no marine reserves. A third area predicted to be a hotspot but lacking protection is the coastal area to the west and northwest of Vancouver Island. Recent ranking and mapping of cumulative human impacts to marine ecosystems conducted by Halpern et al. (2009) indicated that human threats to the marine ecosystem are especially great in northern California, Oregon and Washington, more so than in central and southern California. Halpern et al. s (2009) results imply that the hotspot region extending from Heceta Bank (central Oregon) to Cape Mendocino (northern California) may represent a high priority for conservation. This area has not been extensively surveyed for marine wildlife in comparison to marine areas farther north (Washington and southern British Columbia) or farther south (central and southern California). Limitations of the modeling We used only widely available satellite data for our study, leaving out oceanographic variables that may play an important role in predicting seabird aggregations but for which remotely sensed data do not currently exist. For example, salinity and changes in salinity, which have been demonstrated to be important predictors of seabird aggregations (Ainley et al. 2009, Tremblay et al. 2009), were not included in our analysis. We were only able to use information on SSH at a spatial scale (27-km cell) that provides only a coarse resolution of currents and eddies. Information at a finer scale (e.g., 4-km cell, the scale used for analysis of SST) would have helped highlight the importance of proximity to eddies, which has been identified as an important predictor in other studies (Ballance et al. 2006, Hyrenbach et al. 2006, Louzao et al. 2006, Ainley et al. 2009). Identification of candidate MPA s would benefit from considering life-history constraints. For example, for some seabird species the bottleneck may be the period of migration, when pelagic foraging areas are of high value, yet for others it may be the breeding season, when foraging areas near breeding colonies are most important (Hooker and Gerber 2004; BirdLife International 2009). We were not able to incorporate information on proximity to breeding colonies for the entire California Current, but other distributional modeling studies have done so for more local analyses (Louzao et al. 2006, Ainley et al. 2009). Next steps for modeling Several extensions of this modeling work seem warranted. As many factors as feasible that should be considered in the establishment of MPAs should be incorporated into models predicting seabird aggregations. Such factors include human threats (Halpern et al. 2009), economic considerations (costs to fisheries of closing or restricting take), and other biota of concern (e.g., marine mammals, fish, sharks, turtles; Hooker and Gerber 2004; Leathwick et al. 2008). Including such factors will also help delineate proposed MPA boundaries. Such an exercise should be conducted using software such as Marxan (Wilson et al. 2005, Game and Grantham 2008) and Zonation (Moilanen and Wintle 2006, Moilanen 2007, Leathwick et al. Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 13

15 2008). Utilizing an approach that considers multiple objectives will also inform Marine Spatial Planning (Ehler and Douvere 2009). Delineation of proposed boundaries, whether MPA or IBA, would also benefit from additional spatial modeling focused on a single region within the CCS. The CCS-wide modeling presented here has the advantage of allowing us to compare areas of predicted aggregation in one part of the CCS with areas elsewhere in the CCS, areas that may not have been well studied in the past. For international and national entities it is useful to compare hotspots in British Columbia with those in California in prioritizing efforts. However, predictive models developed on a finer scale may capture habitat associations that are more relevant at that spatial scale, providing predictions that may be useful in refining the placement of protected areas. Broad-scale, CCSwide and fine-scale, local modeling efforts are complementary. We will pursue the following next steps: 1) Outreach Our findings highlight areas with specific conservation needs. This information will be useful to agencies involved in managing these areas (e.g., National Marine Sanctuaries, Marine Protected Areas, etc.) and managing wildlife species (US Fish and Wildlife Service, CA Department of Fish and Game, etc.) as well as other groups advancing improved marine spatial planning and conservation efforts. Our findings should be communicated to a selected group of agencies and partners via in-person meetings, PowerPoint presentations, a short brief on our findings, the full technical report, and some informational resources (e.g., interactive maps) on our website. Our goal is to ensure that the relevant agencies and partners are aware of our findings and can use them in improving marine spatial planning and conservation efforts. We will aim to develop collaborations with appropriate agencies and partners that can integrate our information with their efforts. 2) Regionalization Management and planning would be well-served by the development of a regional predictive model of seabird hotspots that focuses on the Northern California to Southern Oregon region, which can maximize the amount of information available for this specific area. The model will be used to provide recommendations on location and extent of a potential MPA in this region. We propose to build upon our CCS-wide models by developing a more detailed, region-specific model for this area. We will use the same data used in our CCS-wide modeling effort, as well as additional surveys that were not available at that time. These additional surveys will provide a much more detailed picture of predictable hotspots in the nearshore region than was possible in our CCS analysis, thus filling an important data gap and providing valuable information to guide the MLPA planning process for the North Coast Study Region. 3) Climate Change It is likely that climate change will impact marine hotspots in the CCS over the next years. We will build upon our CCS-wide seabird hotpots analysis by developing an additional set of models that can be used to forecast seabird distribution into the future, based on outputs from Regional Climate Models developed specifically for the CCS. For this analysis, we will collaborate with Dr. Mark Snyder (Snyder et al. 2003), a climate modeler from the University of Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 14

16 California-Santa Cruz. We will consolidate the seabird distribution data to identify hotspots in a similar way as in the current hotspot analysis. The results will inform decisions about where to establish new marine protected areas, permit an assessment of the vulnerability of areas that are currently protected, and provide information on which species may be affected sooner than others by climate change in this region. CONCLUSIONS Standardization of methods allowed us to combine data over multiple organizations and investigators to cover a large geographic extent and draw on 11 years of monitoring data. This has provided a robust basis for identifying aggregation hotspots of seabird species. Features that were constant with time, such as depth, appeared to be of high value in predicting seabird distribution and abundance. As a result, we were able to identify locations that were consistently predicted, year in and year out, to be seabird hotspots. This finding indicates the value of permanently sited MPAs. Given that bathymetry and other fixed features predicted seabird hotspots to a large measure, and that such features are consistently important across years, this study suggests that current hotspots will continue to be important in the future as well. Many of the predicted hotspots corresponded well with established marine preserves, especially National Marine Sanctuaries. While current legislation may provide limited protection to marine wildlife within Sanctuaries, the National Marine Sanctuary system provides a legal framework that can be enhanced to provide adequate protection against specific threats and also to consolidate a network of federally protected MPAs. Several predicted hotspots (such as north of Cape Mendocino and Heceta Bank in central Oregon) are not currently protected, nor have they been as well studied. These areas warrant additional research and efforts to provide adequate protection. Integrating this analysis based on seabirds with data for other marine predators (especially marine mammals) and other important components of the food web can strengthen predictions of critical areas to encompass more marine wildlife. This should be a high priority for future analysis. ACKNOWLEDGMENTS Financial support was provided by the Resources Legacy Fund Foundation. Thanks to all those who contributed to data collection and processing on the many at-sea surveys. We especially thank David Hyrenbach, David Ainley, Lisa Ballance, Ken Morgan, and Jen Zamon for their important contributions, providing data and numerous insights and suggestions regarding this project. We thank Glenn Ford for discussion. Numerous agencies have provided long-term support of the at-sea surveys, in particular, NOAA, CWS/EC, and Scripps Institute of Oceanography. We thank Daniel Fink (Cornell Lab of Ornithology) for the custom R code for the bagging portion of our analysis. This is PRBO Contribution Number Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 15

17 Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 16

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19 areas: the missing dimension in ocean conservation. Trends in Ecology and Evolution 24: Garthe, S., Markones, N., Hüppop, O., and Adler, S Effects of hydrographical and meteorological factors on seasonal seabird abundance in the southern North Sea. Marine Ecology Progress Series 391: Guyon, I., and A. Elisseeff An Introduction to variable and feature Selection. Journal of Machine Learning 3: Halpern, B.S The impact of marine reserves: Do reserves work and does reserve size matter? Ecological Applications 13:S117-S137. Halpern, B.S., Kapperl, C.V., Selkoe, K.A., Micheli, F., Ebert, C.M., Kongtis, C., Crain, C.M., Martone, R.G., Shearer, C. and Teck, S.J Mapping cumulative human impacts to California Current marine ecosystems. Conservation Letters 2: Harris, J., Haward, M., Jabour, J., and Woehler, E.J A new approach to selecting Marine Protected Areas (MPAs) in the Southern Ocean. Antarctic Science 19: Hastie, T., Tibshirani, R., and Friedman, J The Elements of Statistical Learning. Springer: New York. Holt, C.A., and Mantua, N Defining spring transition: Regional indices for the California Current System. Marine Ecology Progress Series 393: Hochachka, W.M., Caruana, R., Fink, D., Munson, A., Riedewald, M., Sorokina, D., and Kelling, S Data-mining and discovery of pattern and process in ecological systems. Journal of Wildlife Management 71: Hooker, S.K., and Gerber, L.R Marine reserves as a tool for ecosystem-based management: the potential importance of megafauna. BioScience 54: Hooker S.K., Whitehead, H., and Gowans, S Marine protected area design and the spatial and temporal distribution of cetaceans in a submarine canyon. Conservation Biology 13: Hunt, G.L., Mehlum, F., Russell, R.W., Irons, D., Decker, M.B., and Becker, P.H Physical processes, prey abundance, and the foraging ecology of seabirds. Proceedings 22nd International Ornithological Congress 22: Hyrenbach, K.D., Forney K.A., and Dayton, P.K Marine protected areas and ocean basin management. Aquatic Conservation: Marine and Freshwater Ecosystems 10: Hyrenbach, K.D., Veit, R.R., Weimerskirch, H., and Hunt, G.L Seabird associations with mesoscale eddies: the subtropical Indian Ocean. Marine Ecology Progress Series 324: IUCN Status of the world s marine species. Accessed 01/29/2010. Jahncke, J., Vlietstra, L.S. Decker, M.B. and Hunt, G.L., Jr Marine bird abundance around the Pribilof Islands: A multi-year comparison. Deep-Sea Research Part II 55: Leathwick, J., Moilanen, A., Francis, M., Elith, J., Taylor, P., Julian, K., Hastie, T., and Duffy, C Design and evaluation of large-scale marine protected areas. Conservation Letters 1: Louzao, M., Hyrenbach, K.D., Arcos, J.M., Abello, P., De Sola, L.G., and Oro, D Oceanographic habitat of an endangered Mediterranean procellariiform: implications for marine protected areas. Ecological Applications 16: Louzao, M., Becares, J., Rodriguez, B., Hyrenbach, K.D., Ruiz, A., and Arcos, J.M Combining vessel-based surveys and tracking data to identify key marine areas for seabirds. Marine Ecology Progress Series 391: Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 18

20 Moilanen, A Landscape Zonation, benefit functions and target-based planning: Unifying reserve selection strategies. Biological Conservation 134: Moilanen, A., and Wintle, B.A Uncertainty analysis favours selection of spatially aggregated reserve networks. Biological Conservation 129: Norse, E. A., Crowder, L. B., Gjerde, K., Hyrenbach, K. D., Roberts, C., and Soule, M. E The potential for reserves as an ecosystem-based management tool in the open ocean. Pages in E. A. Norse and L. B. Crowder, editors. Marine Conservation Biology. Island Press, Washington, D.C., USA. Ojeda-Martínez, C., Casalduero, F.G., Bayle-Sempere, J.T., et al A conceptual framework for the integral management of marine protected areas. Ocean and Coastal Management 52: Piatt J.F., Wetzel, J., Bell, K., DeGange, A.R., Balogh, G.R., Drew, G.S., Geernaert, T. Ladd, C., and Byrd, G.V Predictable hotspots and foraging habitat of the endangered short-tailed albatross (Phoebastria albatrus) in the North Pacific: Implications for conservation. Deep Sea Research II: 53: R Development Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna. Redfern, J.V., Barlow, J., Ballance, L.T., Gerrodette, T., and Becker, E.A Absence of scale dependence in dolphin-habitat models for the eastern tropical Pacific Ocean. Marine Ecology Progress Series 363:1-14. Snyder, M.A., Sloan, L.C., Diffenbaugh, N.S., Bell, J.L., Future climate change and upwelling in the California Current. Geophysical Research Letters 30, Article No Suryan, R.M., Sato, F., Balogh, G.R., Hyrenbach, K.D., Sievert, P.R., and Ozaki, K Foraging destinations and marine habitat use of short-tailed albatrosses: A multi-scale approach using first-passage time analysis. Deep Sea Research II: 53: Tremblay, Y., Bertrand, S., Henry, R.W., Kappes, M.A., Costa, D.P., and Shaffer, S.A Analytical approaches to investigating seabird environment interactions: a review. Marine Ecology Progress Series 391: Wilson, K.A., Westphal, M.I., Possingham, H.P., and Elith, J Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biological Conservation 122: Worm, B., Lotze H.K., and Myers, R.A Predator diversity hotspots in the blue ocean. Proceedings of the National Academy of Science USA. 100: Yen, P.P.W., Sydeman, W.J., and Hyrenbach, K.D Marine bird and cetacean associations with bathymetric habitats and shallow-water topographies: implications for trophic transfer and conservation. Journal of Marine Systems 50: Yen, P.P.W., Sydeman, W.J., Morgan, K.H., and Whitney, F.A Top predator distribution and abundance across the eastern Gulf of Alaska: Temporal variability and ocean habitat associations. Deep-Sea Research II 52: Yen, P.P.W., Sydeman, W.J., Bograd, S.J., and Hyrenbach, K.D Spring-time distributions of migratory marine birds in the southern California Current: Oceanic eddy associations and coastal habitat hotspots over 17 years. Deep-Sea Research II 53: Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 19

21 Table 1. Survey coverage. A) Coverage by Year. Number of survey bins shown. Note: coverage began with October 1997 (see text). No surveys from 2007 are included in the statistical analysis. Number Year bins Percent Total B) Coverage by Month. Number of survey bins shown for months 1 (January) to 12 (December). Predictions made for months 2, 5, 7, and 10 only, in this study (see text). Month Number bins Percent Total Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 20

22 Table 1. Survey coverage, cont. C) Survey Coverage By Research/Monitoring Program (Note no data used before October 1997; see text.) Name Principal Investigator(s) Affiliation Years Available Number of bins included CalCOFI Richard Veit and John McGowan David Hyrenbach Bill Sydeman Scripps Institution of Oceanography PRBO ,541 Line P Ken Morgan and Bill Sydeman CWS/EC and PRBO ,950 NMFS Rockfish Bill Sydeman PRBO ,185 ORCAWALE, C SCAPE Lisa Ballance Southwest Fisheries Science Center 2005, ,192 NMFS Sardine Lisa Ballance and Bill Sydeman Southwest Fisheries Science Center and PRBO ,584 CCES Jen Zamon Jaime Jahncke Northwest Fisheries Science Center and PRBO ,544 Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 21

23 Table 2. Species analyzed and modeled. Shown is whether a species is of concern by the Barrow to Baja Project (B2B, Audubon Society), indicated by x, and species status according to IUCN. Conservation Concern? Abbreviation Common Name Scientific Name B2B IUCN BFAL Black-footed Albatross Phoebastria nigripes x Endangered BOGU Bonaparte's Gull Larus philadelphia x Least Concern BRAC Brandt's Cormorant Phalacrocorax penicillatus x Least Concern BRPE Brown Pelican Pelecanus occidentalis x Least Concern CAAU Cassin's Auklet Ptychoramphus aleuticus x Least Concern CAGU California Gull Larus occidentalis x Least Concern COMU Common Murre Uria aalge x Least Concern FTSP Fork-tailed Storm- Petrel Oceanodroma furcata x Least Concern GWGU Glaucous-Winged Gull Larus glaucescens x Least Concern HEEG Heermann's Gull Larus heermanni x Least Concern HERG Herring Gull Larus argentinus Least Concern LHSP Leach's Storm-Petrel Oceanodroma homochroa x Least Concern RNPH Red-necked Phalarope Phalaropus lobatus x Least Concern SAGU Sabine's Gull Xema sabini x Least Concern SOSH Sooty Shearwater Puffinus griseus x Near- Threatened WEGU Western Gull Larus occidentalis x Least Concern Note: In addition, there were 9 species analyzed but which did not yield adequate predictive models (see text). The species not further modeled were: Black-legged Kittiwake (Rissa tridactyla), Rhinoceros Auklet (Cerorhinca monocerata), Black-vented Shearwater (Puffinus opisthomelas), Red Phalarope (Phalaropus fulicarius), Pacific Loon (Gavia pacifica), Pomarine Jaeger (Stercorarius pomarinus), Northern Fulmar (Fulmarus glacialis), Cook s Petrel (Pterodroma cookii), and Pink-footed Shearwater (Puffinus creatopus). Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 22

24 Table 3. Variables examined for modeling. Twenty of the 30 variables were retained (see text). Variables Type of variability Used in model? Latitude spatial yes Longitude spatial no Depth_max spatial no Depth_min spatial yes Depth_avg spatial yes Depth_cv spatial no Contour Index spatial yes Dist 200 m depth spatial yes Dist 1 km isobath spatial yes Dist 3 km isobath spatial yes Dist Island spatial Dist. to Land, instead Dist Mainland spatial Dist. to Land, instead Chlorophyll spatial and temporal yes SST_single cell spatial and temporal yes SST_max spatial and temporal no SST_min spatial and temporal no SST _max-min spatial and temporal no SSH spatial and temporal yes Julian Date temporal yes Transition Date temporal yes Year temporal yes SOI 6 month temporal no PDO 6 month temporal no NPGO 6 month temporal no SOI last 3 month temporal yes PDO last 3 months temporal yes NPGO last 3 months temporal yes SOI 4-6 months prev temporal yes PDO 4-6 months prev temporal yes NPGO 4-6 months prev temporal yes Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 23

25 Table 4. Summarization of Observations. Total number individuals detected and number of bins species was detected. Includes only individuals of appropriate behavior codes (see text). Shown is percent of total survey bins (n = 58,996) in which 1 or more individuals were detected. See Table 2 for species names. Species Abbreviation Total number of individuals observed Mean number observed per bin Number of survey bins where present Percent occurrence, per bin BFAL % BOGU % BRAC % BRPE % CAAU % CAGU % COMU % FTSP % GWGU % HEEG % HERG % LHSP % RNPH % SAGU % SOSH % WEGU % Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 24

26 Table 5. Important variables for each species-specific predictive model. Top 10 variables (out of 20) are shown for each species; variables with proportion of deviance explained > are highlighted (see text). See Table 2 for species names. Species Variable BFAL BOGU BRAC BRPE CAAU CAGU COMU FTSP GWGU HEEG HERG LHSP RNPH SAGU SOSH WEGU Latitude Depth_min Depth_avg Contour Index Dist 200 m isobath Dist 1000 m isobath Dist 3000 m isobath Dist Nearest Land Chlorophyll SST SSH Julian date Year Transition date SOI last 3 months PDO last 3 months NPGO last 3 months SOI 4-6 months prev PDO 4-6 months prev NPGO 4-6 months prev Proportion Deviance Explained Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 25

27 Table 6. Contribution and strength of modeled variables. For each of the 20 variables, the number of species for which the variable was among the top 10 variables for that speciesspecific model is shown, as well as the number of species for which the variable was considered a strong predictor (Proportion Deviance explained >0.075, with all other 19 variables included). Variables Total number species for which variable is among top 10 variables in model Total number species for which variable is strong predictor Latitude 16 5 Depth_min 10 1 Depth_avg 11 2 Contour Index 8 1 Dist 200 m isobath 14 3 Dist 1 km isobath 13 2 Dist 3 km isobath 14 1 Dist Nearest Land 15 8 Chlorophyll 8 0 SST 6 0 SSH 12 4 Julian date 16 4 Year 3 1 Transition date 0 0 SOI last 3 months 1 0 PDO last 3 months 3 0 NPGO last 3 months 2 0 SOI 4-6 months prev 0 0 PDO 4-6 months prev 2 0 NPGO 4-6 months prev 6 0 Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 26

28 Figure 1. Map of Eastern Pacific Ocean, depicting California Current and other features. Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 27

29 Figure 2. Map of surveys used in this study. Individual data points are shown classified by cruise project or program. Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 28

30 Figure 3. Common Murre, predicted abundance by specified month, averaged over all years (birds per km 2 ). A) February. B) May. A) B) Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 29

31 Figure 4. Brown Pelican, predicted abundance by specified month, averaged over all years (birds per km 2 ). A) February. B) July. A) B) Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 30

32 Figure 5. Example of predicted abundance for individual month and species, averaged over all years (birds per km 2 ). A) Black-footed Albatross, July. B) Cassin s Auklet, May. A) B) Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 31

33 Figure 6. Standardized abundance, by month, summed over all 16 species and averaged over all years. A) February. B) May. C) July. D) October. A) B) Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 32

34 C) D) Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 33

35 Figure 7. Individual Species results for areas of importance and persistence. A) Example of importance map for Sooty Shearwater; coded as core areas = 2; shoulder areas = 1. Results are averaged over all months and years. B) Example of persistence map for Cassin s Auklet, for all seasons. Shown is the number of years (out of 11) that area was within the top 5% of predicted abundance. A) B) Wildlife Hotspots in the California Current System, Final Report to RLFF, January 2010 Page 34