Phenological synchronization disrupts trophic interactions between Kodiak brown bears and salmon

Study Area.

We conducted this work in the Karluk watershed (638,000 km²) of Kodiak Island, Alaska (Fig. 1A), which has a dense population of brown bears (∼250 individuals/1,000 km²) (36). Although four species of salmon spawn in Karluk waters, sockeye salmon (Fig. 1C) are the most abundant; the average sockeye salmon run size during the last 56 y (after commercial harvest) was 549,507 ± 34,029 (SEM). Pink salmon are also abundant (average run size = 491,375 ± 96,849 SEM), but spawn primarily in the Karluk River, where they are less vulnerable to bears. Sockeye salmon in our focal system spawn in rivers, along lakeshores, and in lake tributaries. Sockeye salmon are most vulnerable to bear predation in tributary streams (21). In addition, sockeye salmon in smaller tributaries experience greater overall predation rates and allow selective predation of prespawn fish that are higher in energy content (22, 23). The tributary streams in Karluk are 3–16 m wide, representing the size of stream in which bears can prey on prespawn fish and kill a large fraction of the run [∼25–75% (21)].

In addition to salmon, Kodiak brown bears routinely consume several species of berries, including red elderberry (Fig. 1B), salmonberry (Rubus spectabilis Pursh), crowberry (Empetrum nigrum), and blueberry (Vaccinium spp.) (37). Red elderberry is found primarily at midelevation slopes, with greater concentrations on northern aspects (Fig. 1A). Prior studies found that red elderberry was the dominant fruit item found in bear scats on Kodiak during late summer and fall (38).

Elderberry Phenology.

To estimate elderberry phenology, we reviewed images from a time-lapse camera placed near a salmon spawning stream in 2010 and in 2013–2016 (Fig. S3). There were numerous red elderberry shrubs within the image viewshed, which had an area of ∼10,000 m² (Fig. S3). For each year, we noted the first date when ripe red elderberries were visible on at least half of the shrubs. We defined this as the “ripening date” for each year. Although we have observed spatial variation in elderberry phenology across elevations and aspects, our time-lapse method still provides a consistent index of ripening date to calibrate an elderberry development model. Due to bird consumption, decomposition, and other factors, red elderberries decline in abundance after initial ripening.

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Fig. S3.

Example of image used to estimate bear occupancy of salmon spawning habitat and red elderberry phenology. Picture from a time-lapse camera at Canyon Creek. A Kodiak brown bear is in the stream, and the fruits of red elderberry are visible in the foreground at right and to the right of the stream.

To place observed red elderberry phenology into historical context, we compiled a 1949–2016 record of air temperature. We acquired daily minimum and maximum temperatures for our field site (Karluk Lake) and the Kodiak Benny Benson State Airport in Kodiak, Alaska, from the National Climatic Data Center. Data for Karluk Lake were limited to spring–fall of 1965–1969, while daily records starting in 1949 were available for the airport. There were strong positive correlations between Karluk Lake and airport minimum (Pearson’s r = 0.82; P < 0.0001) and maximum (Pearson’s r = 0.81, P < 0.0001) air temperatures. To benefit from the longer airport time series, we fit least squares linear regression models of Karluk maximum and minimum temperatures as functions of airport temperatures and then used these models to predict 1949–2016 Karluk Lake maximum/minimum temperatures (Fig. S1A).

To understand how red elderberry phenology varies with climate, we modeled ripening date as a function of growing degree days (39) (GDDs), a method frequently used in research and application to predict the phenology of temperate vegetation and insects (40⇓⇓⇓⇓⇓⇓–47). GDD models predict phenology from daily temperature data on the basis of two parameters: B, the minimum (base) temperature required for development, and Y, the cumulative growing degrees required to reach a specific stage of development (e.g., fruiting) (39). We found no published values for B or Y in red elderberries, so we estimated them using the 5 y of observed red elderberry fruiting phenology (from 2010 and 2013–2016) and standard methods (39, 48).

To select B, we calculated the GDDs for each of the five observed ripening dates with a B ranging from 0 to 10 °C. We calculated growing degrees (GD) for each day using the equation: {GD = [(max temp − min temp)/2] − B; if GD < 0, GD = 0} (39). GDDs is the cumulative sum of GDs starting from January 1 (39). We selected the value of B that minimized the SD of GDDs required to produce ripe berries in the 5 observed years [2 °C; minimum SD = 16.9 GDDs (42, 48)]. Y was the mean of the five GDD values calculated using B (941 GDDs). Finally, using our estimates of B and Y, we hindcast the red elderberry ripening date for each year in the 1949–2016 record of air temperature. We calculated 95% confidence intervals for each ripening date, using the 95% confidence intervals on the mean of Y (Fig. S1C).

We validated our model using leave-one-out cross-validation, in which we removed one of the five observed elderberry ripening dates, reestimated the B and Y parameters, and then predicted the ripening date for the excluded year. Leave-one-out cross-validation predictions were closely correlated with observed dates of elderberry ripening (Pearson’s r = 0.99; Fig. S1D) and exhibited a mean absolute error of 1.4 d.

Bear Habitat Use.

To assess interannual patterns of bear space use, we analyzed data from three complementary sources: spatially extensive aerial surveys that provided annual snapshots of the entire bear population’s use of salmon predation sites, time-lapse cameras that provided a temporally continuous index of bear activity on salmon streams throughout the salmon spawning season, and GPS telemetry that provided location data for a subset of female individuals with fine spatial and temporal resolution.

To quantify interannual variation in the degree to which bears were foraging on salmon, we conducted aerial counts (from a small airplane) of the number of bears (excluding cubs) seen along 13 streams and rivers, and used the mean annual bear count as an index of bear foraging on salmon. Survey frequency varied (range = 4–22/y; median = 9/y), but generally occurred in the early morning or late evening two to three times per week from early July through late August. We compared annual mean bear counts (±1 SEM) to indices of phenological overlap between elderberry and salmon. We compared bear counts from the Karluk watershed (133 observations across seven streams from 2008 to 2015) to the estimated temporal overlap between salmon and elderberries (Methods). We used least squares linear regression to test the hypothesis that increasing salmon and elderberry overlap was associated with decreased bear activity at salmon predation sites. To test this relationship across a larger temporal and spatial extent (1982–2015, ∼900 km²), we also analyzed bear counts from similar aerial surveys conducted beyond the Karluk watershed (317 observations across six streams). Because we lacked basin-specific information on salmon spawning phenology, we used estimated elderberry ripening date as a proxy for resource overlap and tested the effect of elderberry phenology on bear counts at salmon predation sites, using least squares linear regression. Elderberry ripening date should act as a reliable proxy for resource overlap because salmon phenology is relatively consistent through time, and thus, resource overlap is primarily driven by variation in the date of ripening (Fig. 2B). We also used least squares regression to test for effects of interannual variation in sockeye salmon abundance on bear counts and found no statistically significant relationships (Fig. S4).

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Fig. S4.

Aerial bear counts vs. sockeye salmon escapement (abundance remaining after fishery harvest). (A) Mean bear counts across seven salmon spawning streams in the Karluk watershed vs. abundance of early sockeye salmon run (before July 15), which spawns primarily in streams. Data from 113 aerial surveys spanning 2008–2015. Points show annual mean bear count (n = 8), error bars = 1 SEM, and gray line shows least squares regression. (B) Mean bear counts vs. sockeye salmon escapement into Ayakulik and Dog Salmon watersheds. Data from 317 aerial surveys (1982–2015, n = 31 due to missing years) of six streams and rivers outside of the Karluk watershed. Error bars = 1 SEM, and gray line shows least squares regression.

To determine whether salmon predation varied intra-annually in relation to elderberry availability, we monitored bear occurrence at four tributary streams where salmon are both abundant and vulnerable, using time-lapse photography (streams indicated by yellow stars in Fig. 1A). Although bears occasionally use streams for purposes other than foraging on salmon (e.g., travel corridor or water source), a prior study found that bears were detected by time-lapse cameras 61 times more frequently when salmon were present compared with when salmon were absent (15). Each year, three cameras were placed along each of the four streams (a total of 12 cameras). Cameras were placed in the same location with the same viewshed to ensure count effort was consistent across years. Cameras were deployed in late May or early June before salmon runs began, and were removed in late August in 2013/2014 and late September in 2015, after spawning concluded. Cameras were programmed to take a photo every 5 min during daylight hours. We counted the number of bears (excluding cubs) in each photo and then summed counts by day. These counts do not census the bears that used these streams but, rather, index overall bear use and have been corroborated by more direct monitoring techniques, such as GPS telemetry (15).

To test whether bears switched to using elderberry habitat when ripe elderberries were available, we analyzed the space use of individually collared female brown bears. More than 101,000 GPS locations were recorded from 36 individuals over the course of 47 bear-years (some bears carried collars for more than 1 y). In 2010, 2014, and 2015, 20, 12, and 15 bears carried collars, respectively. We captured bears in the southwestern region of Kodiak Island, Alaska, by firing immobilization darts from a helicopter (49). We fitted each bear with a GPS radio collar programmed to record a location every hour from mid-May through mid-November. We screened GPS locations for accuracy, removing locations with a positional dilution of precision greater than 10 (n = 6,297) (50). All capture procedures were approved by the U.S. Fish and Wildlife Service Institutional Animal Care and Use Committee (IACUC permit #2012008, 2015-001).

To quantify temporal patterns of bear habitat use, we divided the location data by week. For each week, we calculated the proportion of all observations located in salmon habitat and the proportion located in red elderberry habitat (these were independent; i.e., a bear could be in both habitats at once). We defined salmon habitat as areas within 50 m of a stream, river, or lake beach known to be used by spawning salmon (20) (Fig. 1A shows stream and river sites). To evaluate use of elderberry, we used a 30 × 30 m raster of percentage red elderberry canopy cover taken from the Kodiak Land Cover Classification (51) (Fig. 2). We defined elderberry habitat as any area containing at least 10% elderberry cover (this accounted for 10.7% of the landscape). We assumed bears located in elderberry-classified habitats were indeed foraging on elderberry because past diet studies found that elderberry was the dominant nonsalmon diet item (38, 52) (Fig. S2). We assumed that bears detected in salmon spawning habitats (whether by GPS collar, aerial survey, or camera) were foraging on salmon because bears rarely use these habitats except for when salmon are present (21, 53). In our focal system, bear detections at salmon spawning sites increase 61-fold when salmon are spawning (20).

Bear Fecal Analysis.

To directly test the assumption that bears were eating red elderberry when they overlapped temporally with salmon, we conducted a fecal survey on August 21, 2015, during which both salmon and elderberries were available. We conducted the survey by walking along the four study streams monitored by time-lapse cameras. We classified each encountered scat (n = 161) by its dominant prey item: salmon, elderberries, or other. Elderberry scats are easily recognized by the presence of elderberry stems and seeds, whereas salmon scats have a distinct color and odor, and often contain salmon bones. This survey occurred adjacent to habitat used by bears eating salmon, so although it is spatially biased, it is likely to overestimate use of salmon, rather than red elderberry.

Estimating Salmon and Elderberry Macronutrient Composition.

We compared the protein content of salmon and elderberries with those of optimal diets determined by ad libitum feeding experiments on captive brown bears (25). We quantified protein content as the percentage of metabolizable energy in each food source (26, 54, 55), using published macronutrient values for salmon (23) and carbon-nitrogen analysis (N X 6.25) for elderberries (56). We concluded that bears do not digest elderberry seeds because the crude protein content in seeds extracted from feces was not lower than that of seeds in fresh fruit (11.5% digested vs. 10.2% fresh; n = 3) (56). Due to the difficulty of retaining the nonseed portion of the berry after isolating it from the seed, we estimated the crude protein content of the nonseed portion of elderberries by difference (i.e., nonseed protein = total protein − seed protein). The average crude protein content of whole dried elderberry was 12.7%. The undigested seeds accounted for 36 ± 6% of the dried berry’s mass and accounted for 29% of its total crude protein. Thus, the remaining 64% of the berry was composed of 14% crude protein. Carbohydrate content in the nonseed portion of the berry was estimated as the difference between the digestible dry matter and the protein in the nonseed dry matter, assuming no lipids (25). The metabolizable energy provided by carbohydrates, protein, and fat was estimated for salmon and elderberries using Atwater specific factors (25), and then the percentage of metabolizable energy provided by protein was calculated by dividing protein metabolizable energy by total metabolizable energy.

Salmon Abundance and Phenology.

We estimated salmon abundance in four tributaries that are known to be heavily used by bears and are disproportionately important foraging habitats due to their large salmon runs and small size, which renders salmon highly vulnerable to bears (21, 57, 58) (Fig. 1A). We used a time-lapse photography double-sampling method paired with stream specific estimates of mortality rates (59). These camera systems have been shown to estimate salmon abundance accurately (<10% error), do not obstruct salmon passage, and minimize bear disruption due to human presence.

To estimate historical patterns of spawning phenology, we first calculated the average lag time between the median date of sockeye salmon passage at the Karluk river weir [date at which half of the early run (before July 15th) sockeye salmon had moved through the weir] and the date of peak sockeye salmon abundance in the four tributary streams we monitored in 2013–2015. We estimated past peak spawning dates by adding the resulting lag value (46 d) to the median weir passage dates from 1961 to 2016.