Tag: plots

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.

Photogrammetry Insights

By Leila Lemos, PhD Candidate, Fisheries and Wildlife Department, Oregon State University

After three years of fieldwork and analyzing a large dataset, it is time to finally start compiling the results, create plots and see what the trends are. The first dataset I am analyzing is the photogrammetry data (more on our photogrammetry method here), which so far has been full of unexpected results.

Our first big expectation was to find a noticeable intra-year variation. Gray whales spend their winter in the warm waters of Baja California, Mexico, period while they are fasting. In the spring, they perform a big migration to higher latitudes. Only when they reach their summer feeding grounds, that extends from Northern California to the Bering and Chukchi seas, Alaska, do they start feeding and gaining enough calories to support their migration back to Mexico and subsequent fasting period.

 

Northeastern gray whale migration route along the NE Pacific Ocean.
Source: https://journeynorth.org/tm/gwhale/annual/map.html

 

Thus, we expected to see whales arriving along the Oregon coast with a skinny body condition that would gradually improve over the months, during the feeding season. Some exceptions are reasonable, such as a lactating mother or a debilitated individual. However, datasets can be more complex than we expect most of the times, and many variables can influence the results. Our photogrammetry dataset is no different!

In addition, I need to decide what are the best plots to display the results and how to make them. For years now I’ve been hearing about the wonders of R, but I’ve been skeptical about learning a whole new programming/coding language “just to make plots”, as I first thought. I have always used statistical programs such as SPSS or Prism to do my plots and they were so easy to work with. However, there is a lot more we can do in R than “just plots”. Also, it is not just because something seems hard that you won’t even try. We need to expose ourselves sometimes. So, I decided to give it a try (and I am proud of myself I did), and here are some of the results:

 

Plot 1: Body Area Index (BAI) vs Day of the Year (DOY)

 

In this plot, we wanted to assess the annual Body Area Index (BAI) trends that describe how skinny (low number) or fat (higher number) a whale is. BAI is a simplified version of the BMI (Body Mass Index) used for humans. If you are interested about this method we have developed at our lab in collaboration with the Aerial Information Systems Laboratory/OSU, you can read more about it in our publication.

The plots above are three versions of the same data displayed in different ways. The first plot on the left shows all the data points by year, with polynomial best fit lines, and the confidence intervals (in gray). There are many overlapping observation points, so for the middle plot I tried to “clean up the plot” by reducing the size of the points and taking out the gray confidence interval range around the lines. In the last plot on the right, I used a linear regression best fit line, instead of polynomial.

We can see a general trend that the BAI was considerably higher in 2016 (red line), when compared to the following years, which makes us question the accuracy of the dataset for that year. In 2016, we also didn’t sample in the month of July, which is causing the 2016 polynomial line to show a sharp decrease in this month (DOY: ~200-230). But it is also interesting to note that the increasing slope of the linear regression line in all three years is very similar, indicating that the whales gained weight at about the same rate in all years.

 

Plot 2: Body Area Index (BAI) vs Body Condition Score (BCS)

 

In addition to the photogrammetry method of assessing whale body condition, we have also performed a body condition scoring method for all the photos we have taken in the field (based on the method described by Bradford et al. 2012). Thus, with this second set of plots, we wanted to compare both methods of assessing whale body condition in order to evaluate when the methods agree or not, and which method would be best and in which situation. Our hypothesis was that whales with a ‘fair’ body condition would have a lower BAI than whales with a ‘good’ body condition.

The plots above illustrate two versions of the same data, with data in the left plot grouped by year, and the data in the right plot grouped by month. In general, we see that no whales were observed with a poor body condition in the last analysis months (August to October), with both methods agreeing to this fact. Additionally, there were many whales that still had a fair body condition in August and September, but less whales in the month of October, indicating that most whales gained weight over the foraging seasons and were ready to start their Southbound migration and another fasting period. This result is important information regarding monitoring and conservation issues.

However, the 2016 dataset is still a concern, since the whales appear to have considerable higher body condition (BAI) when compared to other years.

 

Plot 3:Temporal Body Area Index (BAI) for individual whales

 

In this last group of plots, we wanted to visualize BAI trends over the season (using day of year – DOY) on the x-axis) for individuals we measured more than once. Here we can see the temporal patterns for the whales “Bit”, “Clouds”, “Pearl”, “Scarback, “Pointy”, and “White Hole”.

We expected to see an overall gradual increase in body condition (BAI) over the seasons, such as what we can observe for Pointy in 2018. However, some whales decreased their condition, such as Bit in 2018. Could this trend be accurate? Furthermore, what about BAI measurements that are different from the trend, such as Scarback in 2017, where the last observation point shows a lower BAI than past observation points? In addition, we still observe a high BAI in 2016 at this individual level, when compared to the other years.

My next step will be to check the whole dataset again and search for inconsistencies. There is something causing these 2016 values to possibly be wrong and I need to find out what it is. The overall quality of the measured photogrammetry images was good and in focus, but other variables could be influencing the quality and accuracy of the measurements.

For instance, when measuring images, I often struggled with glare, water splash, water turbidity, ocean swell, and shadows, as you can see in the photos below. All of these variables caused the borders of the whale body to not be clearly visible/identifiable, which may have caused measurements to be wrong.

 

Examples of bad conditions for performing photogrammetry: (1) glare and water splash, (2) water turbidity, (3) ocean swell, and (4) a shadow created in one of the sides of the whale body.
Source: GEMM Lab. Taken under NMFS permit 16111 issued to John Calambokidis.

 

Thus, I will need to check all of these variables to identify the causes for bad measurements and “clean the dataset”. Only after this process will I be able to make these plots again to look at the trends (which will be easy since I already have my R code written!). Then I’ll move on to my next hypothesis that the BAI of individual whales varied by demographics including sex, age and reproductive state.

To carry out robust science that produces results we can trust, we can’t simply collect data, perform a basic analysis, create plots and believe everything we see. Data is often messy, especially when developing new methods like we have done here with drone based photogrammetry and the BAI. So, I need to spend some important time checking my data for accuracy and examining confounding variables that might affect the dataset. Science can be challenging, both when interpreting data or learning a new command language, but it is all worth it in the end when we produce results we know we can trust.