Tag: El Nino

Zooming in: A closer look at bottlenose dolphin distribution patterns off of San Diego, CA

By: Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Data analysis is often about parsing down data into manageable subsets. My project, which spans 34 years and six study sites along the California coast, requires significant data wrangling before full analysis. As part of a data analysis trial, I first refined my dataset to only the San Diego survey location. I chose this dataset for its standardization and large sample size; the bulk of my sightings, over 4,000 of the 6,136, are from the San Diego survey site where the transect methods were highly standardized. In the next step, I selected explanatory variable datasets that covered the sighting data at similar spatial and temporal resolutions. This small endeavor in analyzing my data was the first big leap into understanding what questions are feasible in terms of variable selection and analysis methods. I developed four major hypotheses for this San Diego site.

The study species: common bottlenose dolphin (Tursiops truncatus) seen along the California coastline in 2015. Image source: Alexa Kownacki.

Hypotheses:

H1: I predict that bottlenose dolphin sightings along the San Diego transect throughout the years 1981-2015 exhibit clustered distribution patterns as a result of the patchy distributions of both the species’ preferred habitats, as well as the social nature of bottlenose dolphins.

H2: I predict there would be higher densities of bottlenose dolphin at higher latitudes spanning 1981-2015 due to prey distributions shifting northward and less human activities in the northerly sections of the transect.

H3: I predict that during warm (positive) El Niño Southern Oscillation (ENSO) months, the dolphin sightings in San Diego would be distributed more northerly, predominantly with prey aggregations historically shifting northward into cooler waters, due to (secondarily) increasing sea surface temperatures.

H4: I predict that along the San Diego coastline, bottlenose dolphin sightings are clustered within two kilometers of the six major lagoons, with no specific preference for any lagoon, because the murky, nutrient-rich waters in the estuarine environments are ideal for prey protection and known for their higher densities of schooling fishes.

Data Description:

The common bottlenose dolphin (Tursiops truncatus) sighting data spans 1981-2015 with a few gap years. Sightings cover all months, but not in all years sampled. The same transect in San Diego was surveyed in a small, rigid-hulled inflatable boat with approximately a two-kilometer observation area (one kilometer surveyed 90 degrees to starboard and port of the bow).

I wanted to see if there were changes in dolphin distribution by latitude and, if so, whether those changes had a relationship to ENSO cycles and/or distances to lagoons. For ENSO data, I used the NOAA database that provides positive, neutral, and negative indices (1, 0, and -1, respectively) by each month of each year. I matched these ENSO data to my month-date information of dolphin sighting data. Distance from each lagoon was calculated for each sighting.

Figure 1. Map representing the San Diego transect, represented with a light blue line inside of a one-kilometer buffered “sighting zone” in pale yellow. The dark pink shapes are dolphin sightings from 1981-2015, although some are stacked on each other and cannot be differentiated. The lagoons, ranging in size, are color-coded. The transect line runs from the breakwaters of Mission Bay, CA to Oceanside Harbor, CA.

Results: 

H1: True, dolphins are clustered and do not have a uniform distribution across this area. Spatial analysis indicated a less than a 1% likelihood that this clustered pattern could be the result of random chance (Fig. 1, z-score = -127.16, p-value < 0.0001). It is well-known that schooling fishes have a patchy distribution, which could influence the clustered distribution of their dolphin predators. In addition, bottlenose dolphins are highly social and although pods change in composition of individuals, the dolphins do usually transit, feed, and socialize in small groups.

Figure 2. Summary from the Average Nearest Neighbor calculation in ArcMap 10.6 displaying that bottlenose dolphin sightings in San Diego are highly clustered. When the z-score, which corresponds to different colors on the graphic above, is strongly negative (< -2.58), in this case dark blue, it indicates clustering. Because the p-value is very small, in this case, much less than 0.01, these results of clustering are strongly significant.

H2: False, dolphins do not occur at higher densities in the higher latitudes of the San Diego study site. The sightings are more clumped towards the lower latitudes overall (p < 2e-16), possibly due to habitat preference. The sightings are closer to beaches with higher human densities and human-related activities near Mission Bay, CA. It should be noted, that just north of the San Diego transect is the Camp Pendleton Marine Base, which conducts frequent military exercises and could deter animals.

Figure 3. Histogram comparing the latitudes with the frequency of dolphin sightings in San Diego, CA. The x-axis represents the latitudinal difference from the most northern part of the transect to each dolphin sighting. Therefore, a small difference would translate to a sighting being in the northern transect areas whereas large differences would translate to sightings being more southerly. This could be read from left to right as most northern to most southern. The y-axis represents the frequency of which those differences are seen, that is, the number of sightings with that amount of latitudinal difference, or essentially location on the transect line. Therefore, you can see there is a peak in the number of sightings towards the southern part of the transect line.

H3: False, during warm (positive) El Niño Southern Oscillation (ENSO) months, the dolphin sightings in San Diego were more southerly. In colder (negative) ENSO months, the dolphins were more northerly. The differences between sighting latitude and ENSO index was significant (p<0.005). Post-hoc analysis indicates that the north-south distribution of dolphin sightings was different during each ENSO state.

Figure 4. Boxplot visualizing distributions of dolphin sightings latitudinal differences and ENSO index, with -1,0, and 1 representing cold, neutral, and warm years, respectively.

H4: True, dolphins are clustered around particular lagoons. Figure 5 illustrates how dolphin sightings nearest to Lagoon 6 (the San Dieguito Lagoon) are always within 0.03 decimal degrees. Because of how these data are formatted, decimal degrees is the easiest way to measure change in distance (in this case, the difference in latitude). In comparison, dolphins at Lagoon 5 (Los Penasquitos Lagoon) are distributed across distances, with the most sightings further from the lagoon.

Figure 5. Bar plot displaying the different distances from dolphin sighting location to the nearest lagoon in San Diego in decimal degrees. Note: Lagoon 4 is south of the study site and therefore was never the nearest lagoon.

I found a significant difference between distance to nearest lagoon in different ENSO index categories (p < 2.55e-9): there is a significant difference in distance to nearest lagoon between neutral and negative values and positive and neutral years. Therefore, I hypothesize that in neutral ENSO months compared to positive and negative ENSO months, prey distributions are changing. This is one possible hypothesis for the significant difference in lagoon preference based on the monthly ENSO index. Using a violin plot (Fig. 6), it appears that Lagoon 5, Los Penasquitos Lagoon, has the widest variation of sighting distances in all ENSO index conditions. In neutral years, Lagoon 0, the Buena Vista Lagoon has multiple sightings, when in positive and negative years it had either no sightings or a single sighting. The Buena Vista Lagoon is the most northerly lagoon, which may indicate that in neutral ENSO months, dolphin pods are more northerly in their distribution.

Figure 6. Violin plot illustrating the distance from lagoons of dolphin sightings under different ENSO conditions. There are three major groups based on ENSO index: “-1” representing cold years, “0” representing neutral years, and “1” representing warm years. On the x-axis are lagoon IDs and on the y-axis is the distance to the nearest lagoon in decimal degrees. The wider the shapes, the more sightings, therefore Lagoon 6 has many sightings within a very small distance compared to Lagoon 5 where sightings are widely dispersed at greater distances.

 

Bottlenose dolphins foraging in a small group along the California coast in 2015. Image source: Alexa Kownacki.

Takeaways to science and management: 

Bottlenose dolphins have a clustered distribution which seems to be related to ENSO monthly indices, and likely, their social structures. From these data, neutral ENSO months appear to have something different happening compared to positive and negative months, that is impacting the sighting distributions of bottlenose dolphins off the San Diego coastline. More research needs to be conducted to determine what is different about neutral months and how this may impact this dolphin population. On a finer scale, the six lagoons in San Diego appear to have a spatial relationship with dolphin sightings. These lagoons may provide critical habitat for bottlenose dolphins and/or for their preferred prey either by protecting the animals or by providing nutrients. Different lagoons may have different spans of impact, that is, some lagoons may have wider outflows that create larger nutrient plumes.

Other than the Marine Mammal Protection Act and small protected zones, there are no safeguards in place for these dolphins, whose population hovers around 500 individuals. Therefore, specific coastal areas surrounding lagoons that are more vulnerable to habitat loss, habitat degradation, and/or are more frequented by dolphins, may want greater protection added at a local, state, or federal level. For example, the Batiquitos and San Dieguito Lagoons already contain some Marine Conservation Areas with No-Take Zones within their reach. The city of San Diego and the state of California need better ways to assess the coastlines in their jurisdictions and how protecting the marine, estuarine, and terrestrial environments near and encompassing the coastlines impacts the greater ecosystem.

This dive into my data was an excellent lesson in spatial scaling with regards to parsing down my data to a single study site and in matching my existing data sets to other data that could help answer my hypotheses. Originally, I underestimated the robustness of my data. At first, I hesitated when considering reducing the dolphin sighting data to only include San Diego because I was concerned that I would not be able to do the statistical analyses. However, these concerns were unfounded. My results are strongly significant and provide great insight into my questions about my data. Now, I can further apply these preliminary results and explore both finer and broader scale resolutions, such as using the more precise ENSO index values and finding ways to compare offshore bottlenose dolphin sighting distributions.

Scratching the Surface

By Dr. Leigh Torres, Assistant Professor, Oregon State University, Geospatial Ecology of Marine Megafauna Lab

I have been reminded of a lesson I learned long ago: Never turn your back on the sea – it’s always changing.

The blue whales weren’t where they were last time. I wrongly assumed oceanographic patterns would be similar to our last time out in 2014 and that the whales would be in the same area. But the ocean is dynamic – ever changing. I knew this. And I know it better now.

Below (Fig. 1) are two satellite images of sea surface temperature (SST) within the South Taranaki Bight and west coast region of New Zealand that we surveyed in Jan-Feb 2014 and again recently during Jan-Feb 2016. The plot on the left describes ocean surface conditions in 2014 and illustrates how SST primarily ranged between 15 and 18 ⁰C. By comparison, the panel on the right depicts the sea surface conditions we just encountered during the 2016 field season, and a huge difference is apparent: this year SST ranged between 18 and 23 ⁰C, barely overlapping with the 2014 field season conditions.

Figure 1. A comparison of satellite images of sea surface temperature (SST) in the South Taranaki Bight region of New Zealand between late January 2014 and early February 2016. The white circles on each image denote where the majority of blue whales were encountered during each field season.
Figure 1. A comparison of satellite images of sea surface temperature (SST) in the South Taranaki Bight region of New Zealand between late January 2014 and early February 2016. The white circles on each image denote where the majority of blue whales were encountered during each field season.

While whales can live in a wide range of water temperatures, their prey is much pickier. Krill, tiny zooplankton that blue whales seek and devour in large quantities, tend to aggregate in pockets of nutrient-rich, cool water in this region of New Zealand. During the 2014 field season, we encountered most blue whales in an area where SST was about 15 ⁰C (within the white circle in the left panel of Fig. 1). This year, there was no cool water anywhere and we mainly found the whales off the west coast of Kahurangi shoals in about 21 ⁰C water (within the white circle in the right panel of Fig. 1. NB: the cooler water in the Cook Strait in the southeast region of the right panel is a different water mass than preferred by blue whales and does not contain their prey.)

The hot water we found this year across the survey region can likely be attributed, at least in part, to the El Niño conditions that are occurring across the Pacific Ocean currently. El Niño has brought unusually settled conditions to New Zealand this summer, which means relatively few high wind events that normally churn up the ocean and mix the cool, nutrient rich deep water with the hot surface layer water. These are ideal conditions for Kiwi sun-bathers, but the ocean remains highly stratified with a stable layer of hot water on top. However, this stratification does not necessarily mean the ocean is un-productive – it only means that the SST satellite images are virtually useless for helping us to find whales this year.

Although SST data can be informative about ocean conditions, it only reflects what is happening in the thin, top slice of the ocean. Sub-surface conditions can be very different. Ocean conditions during our two survey periods in 2014 and 2016 could be more similar when compared underwater than when viewed from above. This is why sub-surface sensors and data collection is critical to marine studies. Ocean conditions in 2014 and 2016 could both potentially provide good habitat for the whales. In fact, where and when we encountered whales during both 2014 and 2016 we also detected high densities of krill through hydro-acoustics (Fig. 2). However, in 2014 we observed many surface swarms of krill that we rarely saw this recent field season, which could be due to elevated SST. But, we did capture cool drone footage this year of a brief sub-surface foraging event:

An overhead look of a blue whale foraging event as the animal approaches the surface. Note how the distended ventral (throat) grooves of the buccal cavity (mouth) are visible. This is a big gulp of prey (krill) and water. The video was captured using a DJI Phantom 3 drone in the South Taranaki Bight of New Zealand in on February 2, 2016 under a research permit from the New Zealand Department of Conservation (DOC) permit # 45780-MAR issued to Oregon State University.

Figure 2. An echo-sounder image of dense krill patches at 50-80 m depth captured through hydroacoustics in the South Taranaki Bight region of New Zealand.
Figure 2. An echo-sounder image of dense krill patches at 50-80 m depth captured through hydroacoustics in the South Taranaki Bight region of New Zealand.

Below are SST anomaly plots of January 2014 and January 2016 (Fig. 3). These anomaly plots show how different the SST was compared to the long-term average SST across the New Zealand region. As you can see, in 2014 (left panel) SST conditions in our study area were ~1 ⁰C below average, while in 2016 (right panel) SST conditions were ~1 ⁰C above average. So, what are normal conditions? What can we expect next year when we come back to survey again for blue whales across this region? These are challenging questions and illustrate why marine ecology studies like this one must be conducted over many years. One year is just a snap shot in the lifetime of the oceans.

Figure 3. Comparison of sea surface temperature (SST) anomaly plots of the New Zealand region between January 2014 (left) and January 2016 (right). The white box in both plots denotes the general location of our blue whale study region. (Apologies for the different formats of these plots - the underlying data is directly comparable.)
Figure 3. Comparison of sea surface temperature (SST) anomaly plots of the New Zealand region between January 2014 (left) and January 2016 (right). The white box in both plots denotes the general location of our blue whale study region. (Apologies for the different formats of these plots – the underlying data is directly comparable.)

Like all marine megafauna, blue whales move far and fast to adjust their distribution patterns according to ocean conditions. So, I can’t tell you what the ocean will be like in January 2017 or where the whales will be, but as we continue to study this marine ecosystem and its inhabitants our understanding of ocean patterns and whale ecology will improve. With every year of new data we will be able to better predict ocean and blue whale distribution patterns, providing managers with the tools they need to protect our marine environment. For now, we are just beginning to scratch the (sea) surface.