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Final Project: San Diego Bottlenose Dolphin Sighting Distributions

Final Project: San Diego Bottlenose Dolphin Sighting Distributions

The Research Question:

Originally, I asked the question: do common bottlenose dolphin sighting distances from shore change over time?

However, throughout the research and analysis process, I refined this question for a multitude of reasons. For example, I planned on using all of my dolphin sightings from my six different survey locations along the California coastline. Because the bulk of my sightings are from the San Diego survey site, I chose this data set for completeness and feasibility. Additionally, this data set used the most standard survey methods. Rather than simply looking at distance from shore, which would be at a very fine scale, seeing as all of my sightings are within two kilometers from shore, I chose to try and identify changes in latitude. Furthermore, I wanted to see if changes in latitude (if present, were somehow related to the El Nino Southern Oscillation (ENSO) cycles and then distances to lagoons). This data set also has the largest span of sightings by both year and month. When you see my hypotheses, you will notice that my original research question morphed into much more specific hypotheses.

Data Description:

My dolphin sighting data spans 1981-2015 with a few absent years, and sightings covering all months, but not in all years sampled. The same transects were performed in a small boat with approximately a two kilometer sighting span (one kilometer surveyed 90 degrees to starboard and port of the bow). These data points therefore have a resolution of approximately two kilometers. Much of the other data has a coarser spatial resolution, which is why it was important to use such a robust data set. The ENSO data I used gave a broad brushstroke approach to ENSO indices. Rather than first using the exact ENSO index which is at a fine scale, I used the NOAA database that split month-years into positive, neutral, and negative indices (1, 0, and -1, respectively). These data were at a month-year temporal resolution, which I matched to my month-date information of my sighting data. Lagoon data were sourced from the mid-late 2000s, therefore I treated lagoon distances as static.

Hypotheses:

H1: I predicted that bottlenose dolphin sightings at the pod-scale (usually, one to ten individuals) along the San Diego transect throughout the years 1981-2015 would exhibit clustered distribution patterns as a result of the patchy distributions of both the species’ preferred habitats and prey, as well as the social nature of this species.

H2: I predicted there would be higher densities of bottlenose dolphin sightings at the pod-scale (usually, one to ten individuals) at higher latitudes spanning 1981-2015 due to prey distributions shifting northward and less human activities in the northward sections of the transect. I predicted that during warm (positive) 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. I expect the spatial gradient to shift north and south, in relation to the ENSO gradient (warm, neutral, or cold)

H3: I predicted that along the San Diego coastline, bottlenose dolphin sightings at the pod-scale (usually, one to ten individuals) would be clustered around the six major lagoons within about two kilometers, 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.

Map with bottlenose dolphin sightings on the one-kilometer buffered transect line and the six major lagoons in San Diego.

Approaches:

I utilized multiple approaches with different software platforms including ArcMap, qGIS, GoogleEarth, and R Studio (with some Excel data cleaning).

  • Buffers in ArcMap
  • Calculations in an attribute table
  • ANOVA with Tukey HSD
  • Nearest Neighbor averages
  • Cluster analyses
  • Histograms and Bar plots

Results: 

I produced a few maps (will be), found statistical relationships between sightings and distribution patterns,  ENSO and dolphin latitudes, and distances to lagoons.

H1: I predicted that bottlenose dolphin sightings at the pod-scale (usually, one to ten individuals) along the San Diego transect throughout the years 1981-2015 would exhibit clustered distribution patterns as a result of the patchy distributions of both the species’ preferred habitats and prey, as well as the social nature of this species.

True: The results of the average nearest neighbor spatial analysis in ArcMap 10.6 produced a z-score of -127.16 with a p-value of < 0.000001, which translates into there being less than a 1% likelihood that this clustered pattern could be the result of random chance. Although I could not look directly at prey distributions because of data availability, it is well-known that schooling fishes exist in clustered distributions that could be related to these dolphin sightings also being clustered. 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. Also see Exercise 2 for other, relevant preliminary results, including a histogram of the distribution in differences of sighting latitudes.

Summary from the Average Nearest Neighbor calculation in ArcMap 10.6 displaying that bottlenose dolphin sightings in San Diego are highly clustered.

H2: I predicted there would be higher densities of bottlenose dolphin sightings at the pod-scale (usually, one to ten individuals) at higher latitudes spanning 1981-2015 due to prey distributions shifting northward and less human activities in the northward sections of the transect. With this, I predicted that during warm (positive) 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. I expect the spatial gradient to shift north and south, in relation to the ENSO gradient (warm, neutral, or cold).

False: 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.

I used an ANOVA analysis and found there was a significant difference in sighting latitude distributions between monthly ENSO indices. A Tukey HSD was performed to determine where the differences between treatment(s) were significant. All differences (neutral and negative, positive and negative, and positive and neutral ENSO indices) were significant with p < 0.005.

H3: I predicted that along the San Diego coastline, bottlenose dolphin sightings at the pod-scale (usually, one to ten individuals) would be clustered around the six major lagoons within about two kilometers, 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. See my Exercise 3 results.

Using a histogram, I was able to visualize how distances to each lagoon differed by lagoon. That is dolphin sightings nearest to, Lagoon 6, the San Dieguito Lagoon, are always within 0.03 decimal degrees. In comparison, Lagoon 5, Los Penasquitos Lagoon, is distributed across distances, with the most sightings at a great distance.

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.

After running an ANOVA in R Studio, I found that there was a significant difference between distance to nearest lagoon in different ENSO index categories (p < 2.55e-9) with a Tukey HSD confirming that the significant difference in distance to nearest lagoon being significant between neutral and negative values and positive and neutral years. Therefore, I gather there must be something happening in neutral months that changes the distance to the nearest lagoon, potentially prey are more static or more dynamic in those years compared to the positive and negative months. Using a violin plot, it appears that Lagoon 5, Los Penasquitos Lagoon, has the widest span of sighting distances when it is the nearest lagoon in all ENSO index month values. In neutral years, Lagoon 0, the Buena Vista Lagoon has more than a single sighting (there were none in negative months and only one in positive months). 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.

Takeaways to science and management: 

Bottlenose dolphins have a clustered distribution which seems to be related to ENSO monthly indices, with certain years having more of a difference in distribution, and likely, their sociality on a larger scale. Neutral ENSO months seem to have a different characteristic that impact sighting distribution locations along the San Diego coastline. More research needs to be done in this 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 pod sighting distributions. These lagoons may provide critical habitat for bottlenose dolphin preferred prey species or preferred habitat for the dolphins themselves either for cover or for hunting, and different lagoons may have different spans of impact at different distances, either by creating larger nutrient plumes, or because of static, geographic and geologic features. This could mean that specific areas should be protected more or maintain protection. For example, the Batiquitos and San Dieguito Lagoons have some Marine Conservation Areas with No-Take Zones. It is interesting to see the relationship to different lagoons, which may provide nutrient outflows and protection for key bottlenose dolphin prey species. The city of San Diego and the state of California are need ways to assess the coastlines and how protecting the marine, estuarine, and terrestrial environments near and encompassing the coastlines impact the greater ecosystem. Other than the Marine Mammal Protection Act and small protected zones, there are no safeguards for these dolphins.

My Learning: about software (a) Arc-Info and b) R

  1. a) Arc-Info: buffer creation, creating graphs, nearest neighbor analyses. How to deal with transects, certain data with mismatching information, conflicting shapefiles
  2. b) R: I didn’t know much, except the basics in R. I learned about how to conduct ANOVAs and then how to interpret results. Mainly I learned about how to visualize my results and use new packages.

My Learning: about statistics

Throughout this project I learned that spatial statistics requires clear hypothesis testing in order to clearly step through a spatial process. Most specifically, I learned about spatial analyses in ArcMap, and how I could utilize nearest neighbor calculations to assess distribution patters. Furthermore, I now have a better understanding of spatial distribution patterns and how they are assessed, such as clustering versus random versus equally dispersed distributions. For more data analysis and cleaning, I also learned how to apply my novice understanding of ANOVAs and then display results relating to spatial relationships (distances) using histograms and other graphical displays in R Studio.

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Contact information: this post was written by Alexa Kownacki, Wildlife Science Ph.D. Student at Oregon State University. Twitter: @lexaKownacki

The Biogeography of Coastal Bottlenose Dolphins off of California, USA between 1981-2016

Background/Description:

Common bottlenose dolphins (Tursiops truncatus), hereafter referred to as bottlenose dolphins, are long-lived, marine mammals that inhabit the coastal and offshore waters of the California Current Ecosystem. Because of their geographical diversity, bottlenose dolphins are divided into many different species and subspecies (Hoelzel, Potter, and Best 1998). Bottlenose dolphins exist in two distinct ecotypes off the west coast of the United States: a coastal (inshore) ecotype and an offshore (island) ecotype. The coastal ecotype inhabits nearshore waters, generally less than 1 km from shore, between Ensenada, Baja California, Mexico and San Francisco, California, USA (Bearzi 2005; Defran and Weller 1999). Less is known about the range of the offshore ecotype , which is broadly defined as more than 2 km offshore off the entire west coast of the USA (Carretta et al. 2016). Current population abundance estimates are 453 coastal individuals and 1,924 offshore individuals (Carretta et al. 2017). The offshore and coastal bottlenose dolphins off of California are genetically distinct (Wells and Scott 1990).

Both ecotypes breed in summer and calve the following summer, which may be thermoregulatory adaptation (Hanson and Defran 1993). These dolphins are crepuscular feeders that predominantly hunt prey in the early morning and late afternoon (Hanson and Defran 1993), which correlates to the movement patterns of their fish prey. Out of 25 prey fish species, surf perches and croakers make up nearly 25% of coastal T. truncatus diet (Hanson and Defran 1993). These fish, unlike T. truncatus, are not federally protected, and neither are their habitats. Therefore, major threats to dolphins and their prey species include habitat degradation, overfishing, and harmful algal blooms (McCabe et al. 2010).

This project aims to better understand that distribution of coastal bottlenose dolphins in the waters off of California, specifically in relation to distance from shore, and how that distance has changed over time.

Data:

This part of the overarching project focuses on understanding the biogeography of coastal bottlenose dolphins. Later stages in the project will require the addition of offshore bottlenose sightings to compare population habitats.

Beginning in 1981, georeferenced sighting data of coastal bottlenose dolphin off the California, USA coast were collected by R.H. Defran and team. The data were provided in the datum, NAD 1983. Small boats less than 10 meters in length were used to collect the majority of the field data, including GPS points, photographs, and biopsy samples. These surveys followed similar tracklines with a specific start and end location, which will be used to calculate the sighting per unit effort. Over the next four decades, varying amounts of data were collected in six different regions (Fig. 1). Coastal T. truncatus sightings from 1981-2015 parallel much of the California land mass, concentrating in specific areas (Fig. 2). Many of the sightings are clustered nearby larger cities due to logistics of port locations. The greater number of coastal dolphin sightings is due to the bias in effort toward proximity to shore and longer study period. All samples were collected under a NOAA-NMFS permit.Additional data required will likely be sourced from publicly-available, long-term data collections, such as ERDDAP or MODIS.

Distance from shore will be calculated in a program such as ArcGIS or R package. These data will be used later in the project to compare to additional static, dynamic, and long-term environmental drivers. These factors will be tested as possible layers to add in mapping and finally estimating population distribution patterns of the dolphins.

Figure 1. Breakdown of coastal bottlenose dolphin sightings by decade. Image source: Alexa Kownacki.

 

 

 

 

 

 

 

 

 

 

 

Hypotheses:

I predict that the coastal bottlenose dolphins will be associated with different bathymetry patterns and appear clustered based on a depth profile via mechanisms such as prey distribution and abundance, nutrient plumes, and predator avoidance.

Approaches:

My objective is to first find a bathymetric layer that covers the coast of the entirety of California, USA to import into ArcMap 10.6. Then I need to interpolate the data to create a smooth surface. Then, I can add my dolphin sighting points and create a way to associate each point with a depth. These depth and point data would be exported to R for further analysis. Once I have extracted these data, I can run a KS-test to compare the shape of distribution based on two different factors, such as points from El Niño years versus La Niña years to see if there is a difference in average sighting depth or more common sighting depths based on the climatic patterns. I am also interested in using the spatial statistic analysis tool, Moran’s I, to see if the sightings are clustered. If so, I would run a cluster analysis to see if the sightings are clustered by depth. If not, then maybe there are other drivers that I can test, such as distance from shore, upwelling index values, or sea surface temperature. Additionally, these patterns would be analyzed over different time scales, such as monthly, seasonally, or decadally.

Expected Outcome:

Ideally, I would produce multiple maps from ArcGIS representing different spatial scales at defined increments, such as by month (all Januaries, all Februaries, etc.), by year or binned time increment (i.e. 1981-1989, 1990-1999), and also potentially grouping based on El Niño or La Niña year. Different symbologies would represent coastal dolphin sightings distances from shore. The maps would visually display seafloor depths in a color spectrum by 10 meter difference. Because the coastlines of California vary in terms of depth profiles, I would expect there to be clusters of sightings at different distances from shore, but similar depth profiles if my hypothesis is true. Also, data with the quantified values of seafloor depth would be associated with each data point (dolphin sighting) for further analysis in R.

Significance:

This project draws upon decades of rich spatiotemporal and biological information of two neighboring long-lived cetacean populations that inhabit contrasting coastal and offshore waters of the California Bight. The coastal ecotype has a strong, positive relationship with distance to shore, in that it is usually sighted within five kilometers, and therefore is in frequent contact with human-related activities. However, patterns of distances to shore over decades, related to habitat type and possibly linked to prey species distribution, or long-term environmental drivers, is largely unknown. By better understanding the distribution and biogeography of these marine mammals, managers can better mitigate the potential effects of humans on the dolphins and see where and when animals may be at higher risk of disturbance.

Preparation:

I have a moderate amount of experience in ArcMap from past coursework (GEOG 560 and 561), as well as practical applications and map-making. I have very little experience in Modelbuilder and Python-based GIS programming. I am becoming more familiar with the R program after two statistics courses and analyzing some of my own preliminary data. I am experienced in image processing in ACDSee, PhotoShop, ImageJ, and other analyses mainly from marine vertebrate data through NOAA Fisheries.

Literature Cited:

Bearzi, Maddalena. 2005. “Aspects of the Ecology and Behaviour of Bottlenose Dolphins (Tursiops Truncatus) in Santa Monica Bay, California.” Journal of Cetacean Research Managemente 7 (1): 75–83. https://doi.org/10.1118/1.4820976.

Carretta, James V., Kerri Danil, Susan J. Chivers, David W. Weller, David S. Janiger, Michelle Berman-Kowalewski, Keith M. Hernandez, et al. 2016. “Recovery Rates of Bottlenose Dolphin (Tursiops Truncatus) Carcasses Estimated from Stranding and Survival Rate Data.” Marine Mammal Science 32 (1): 349–62. https://doi.org/10.1111/mms.12264.

Carretta, James V, Karin A Forney, Erin M Oleson, David W Weller, Aimee R Lang, Jason Baker, Marcia M Muto, et al. 2017. “U.S. Pacific Marine Mammal Stock Assessments: 2016.” NOAA Technical Memorandum NMFS, no. June. https://doi.org/10.7289/V5/TM-SWFSC-5.

Defran, R. H., and David W Weller. 1999. “Occurrence , Distribution , Site Fidelity , and School Size of Bottlenose Dolphins ( Tursiops T R U N C a T U S ) Off San Diego , California.” Marine Mammal Science 15 (April): 366–80.

Hanson, Mark T, and R.H. Defran. 1993. “The Behavior and Feeding Ecology of the Pacific Coast Bottlenose Dolphin, Tursiops Truncatus.” Aquatic Mammals 19 (3): 127–42.

Hoelzel, A. R., C. W. Potter, and P. B. Best. 1998. “Genetic Differentiation between Parapatric ‘nearshore’ and ‘Offshore’ Populations of the Bottlenose Dolphin.” Proceedings of the Royal Society B: Biological Sciences 265 (1402): 1177–83. https://doi.org/10.1098/rspb.1998.0416.

McCabe, Elizabeth J.Berens, Damon P. Gannon, Nélio B. Barros, and Randall S. Wells. 2010. “Prey Selection by Resident Common Bottlenose Dolphins (Tursiops Truncatus) in Sarasota Bay, Florida.” Marine Biology 157 (5): 931–42. https://doi.org/10.1007/s00227-009-1371-2.

Wells, Randall S., and Michael D. Scott. 1990. “Estimating Bottlenose Dolphin Population Parameters From Individual Identification and Capture-Release Techniques.” Report International Whaling Commission, no. 12.

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Contact information: this post was written by Alexa Kownacki, Wildlife Science Ph.D. Student at Oregon State University. Twitter: @lexaKownacki