Tag Archives: anova

Courtney’s Final Project Post

Research Question

  • “How is the spatial pattern of ion and isotope concentrations in wells tapping the basalt aquifer related to the spatial pattern of mapped faults via the mechanism of groundwater flow as determined by hydraulic transmissivity of the geologic setting?”

Description of dataset that I examined

  • A: In my research I have analytical data for 31 wells, whose XY locations were determined by field confirmation of the Oregon Water Resource Department (OWRD) well log database. As groundwater is a 3D system, I have to consider Z values as well. The well depths and lithology information are also from the OWRD database. My analytical data provides a snapshot of water chemistry during the summer of 2018. I have only one temporal data point per well. At all 31 wells, I collected samples to be analyzed for pH, temperature, specific conductivity, oxygen isotopes 16 and 18, and hydrogen isotopes 1 and 2. At a subset of 18 of those wells I collected additional samples for tritium, carbon 14, and major ion analysis.
  • B: The shapefile of faults mapped at the surface was created by Madin and Geitgey of the USGS in their 2007 publication on the geology of the Umatilla Basin. There is some uncertainty in my analysis as far as extending this surface information into the subsurface. USGS studies have constrained proposed ranges of dip angles for the families of faults that I am studying, but not exact angles for any single mapped fault.
  • C: results of pumping interference tests involving 29 wells, 12 of which I had chemical data for. The data was collected by the OWRD in 2018 and 2019.

Hypotheses

  • Faults can act as conduits or barriers to groundwater flow, depending on how their transmissivity compares to the transmissivity of the host rock.
  • I hypothesize that clusters of similar chemical and isotopic properties of groundwater can indicate a shared aquifer unit/compartment, and that if faults separate clusters then the fault is influencing that difference in chemical/isotopic signatures. If the fault is between two clusters, I hypothesize that it is acting as a barrier. If it crosses through a cluster, I hypothesize that it acts as a conduit.
  • Where faults act as barriers, I hypothesize that parameter values will differ in groups on either side of a fault. Specifically, a barrier fault might cause older, warmer water to rise into upper aquifer layers, and the downstream well might show a signature of more local recharge.
  • Where faults act as conduits, I hypothesize that water chemistry and isotopes of samples from wells on either side of the fault would indicate a relatively direct flowpath from the upstream well to a downstream well. Over a short distance, this means that ion and isotope concentrations would not differ significantly in wells across the fault.
  • My hypotheses depend on a “barrier” fault damming groundwater flow up-gradient of the fault, and compartmentalizing local recharge on the down-gradient side. This hypothesis is only relevant if the fault is roughly perpendicular to the flow direction, and so disrupting transmissivity between a recharge zone and the wells. If a fault that separates two wells is parallel to the flow direction and there is no obstacle between the wells and upstream recharge areas, then the fault might indeed limit communication between the wells but they will have similar chemical signatures. Wells separate by this second kind of fault barrier would be better evaluated by a physical test of communication, such as a pumping interference test.

Analysis Approaches

  • Principal component analysis: used to simplify the multivariate data set (19 variables!) into variable relationships that could represent aquifer processes
  • Analysis of PCA results compared to distance from a flow path
    • Interpolation of well water levels classified by well stratigraphy to estimate a potentiometric surface and evaluate groundwater flow directions.
    • Raster calculations to compare flow direction to fault incidence angle
    • Measuring distance from each well to the nearest fault along the flow path
    • simple linear regression, comparing Non-ion PC1 score of a well with its distance from a fault.
  • Two-sided T-tests comparing distance between wells, presence of a fault, and pumping interference test communication between wells
  • ANOVA F-tests comparing chemical and isotopic variance within groups of wells that communicate with each other and between those groups.

Results

  • Principal component analysis – Patterns of variation between wells are statistically explained primarily by total ion concentration, a relationship between chemical evolution from ion exchange and decreasing stable isotope ratios, and the combination of well depth and water temperature. Moran’s I indicates that only Non-ion PC2 is spatially clustered, while the other PC models have a distribution not significantly different than random. The other PC models are useful to understand the groundwater system, but not specifically to analyze clustering correlated to faults.
  • Interpolation of water level, and comparison of fault incidence angle with flow direction, indicates faults that are and are not able to be tested by my hypotheses.
  • Analysis of PCA results compared to distance from a fault along flow path – some wells that are “within” a fault zone have very old signatures and others have very young signatures. This could be related to the angle of the dip of the fault and the accuracy of mapping compared to the depth of the well. I hypothesize that the wells that are in the fault zone but have high PC1 scores are on the up-gradient side of the fault where older water is upwelling along a barrier. Wells in fault zones with low PC1 scores could indicate wells open to downgradient areas of the fault, where vertical recharge through the fault damage zone is able to reach the well.
  • Returning to the conclusions I wrote in that blog post after I found improved stratigraphic data, I’m not sure if I can make conclusions other than those about the wells are that mapped as “inside” a fault. Several wells that are closer to faults are also open to shallower aquifer units, and so the effect of lower PC1 scores closer downgradient to faults might be confounded by lower PC1 scores caused by vertical inflow from the sedimentary aquifer and upper Saddle Mountain aquifer.
  • Two-sided T-tests comparing distance between wells, presence of a fault, and communication between wells show that the presence of a fault has a greater effect on communication than the distance between the wells.
  • ANOVA F-tests comparing chemical and isotopic variance within groups of wells that communicate with each other and between those groups – stable isotopes and specific conductivity both show more variation between well groups than within well groups.
  • Not covering in these blog posts, I also ran Moran’s I on my inputs to see which ones are clustered and so might be more related to horizontal grouping factors (such as faults) than vertical grouping parameters (such as stratigraphic unit). Of the PCA and individual variables, only d18O, d2H, and Non-ion PC2(combination of well depth and water temperature) were clustered. The other PCA models, temperature, pH, and specific conductivity were not significantly spatially clustered.

Significance –  Groundwater signatures are related to faults agree/disagree with past understandings of differences between wells in the region, and can inform well management. If a senior permit holder puts a call on the water right and asks for junior users to be regulated off, it would not help that senior user if on of those junior permit holders’ wells is not hydraulically connected to the senior users.

  • More wells would need to be sampled to be better able to disentangle the effects of faults from the effects of well stratigraphy.

My learning – I learned significantly more about how to code and troubleshoot in R. Additionally, I learned about the process of performing spatial clustering analysis in ArcGIS.

What did I learn about statistics?

  • PCA was completely new to me, and it’s a cool method for dealing with multivariate data once I dealt with the steep learning curve involved in setting it up and interpreting the results. It was useful getting more practice performing and interpreting t-tests and Anova F-tests. I had not used spatial clustering before, and learning how to apply it was interesting. It gave me a much more concrete tool to try to disentangle the patterns in my effectively 3D data on X,Y plane, as opposed to the Z direction.

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

Exercise 3: Lagoons, ENSO Indices, and Dolphin Sightings

Exercise 3: Are bottlenose dolphin sightings distances to nearest lagoon related to ENSO indices in the San Diego, CA survey site?

1. Question that you asked

I was looking to see a pattern at more than one scale, specifically the relationship with ENSO and sighting distributions off of San Diego. I asked the question: do bottlenose dolphin sighting distributions change latitudinally with ENSO related to distance from the nearest lagoon. The greater San Diego area has six major lagoons that contribute the major estuarine habitat to the San Diego coastline and are all recognized as separate estuaries. All of these lagoons/estuaries sit at the mouths of broad river valleys along the 18 miles of coastline between Torrey Pines to the south and Oceanside to the north. The small boat survey transects cover this entire stretch with near-exact overlap from start to finish. These habitats are known to be highly dynamic, experience variable environmental conditions, and support a wide range of native vegetation and wildlife species.

Distribution of common bottlenose dolphin sightings in the San Diego study area along boat-based transects with the six major lagoons.

 

FID NAME
0 Buena Vista Lagoon
1 Agua Hedionda Lagoon
2 Batiquitos Lagoon
3 San Elijo Lagoon
4 Tijuana Estuary
5 Los Penasquitos Lagoon
6 San Dieguito Lagoon

2. Name of the tool or approach that you used.

I utilized the “Near” tool in ArcMap 10.6 that calculated the distance from points to polygons and associated the point with FID of that nearest polygon. I also used R Studio for basic analysis, graphic displays, and ANOVA with Tukey HSD.

3. Brief description of steps you followed to complete the analysis.

  1. I researched the San Diego GIS database for the layer that would be most helpful and found the lagoon shapefile.
  2. Imported the shapefile into ArcMap where I already had my sightings, transect line, and 1000m buffered transect polygon.
  3. I used the “Near” tool in the Analysis toolbox, part of the of the “proximity toolset”. I chose the point to polygon option with my dolphin sightings as the point layer and the lagoon polygons as the polygon layer.
  4. I opened the attribute table for my dolphin sightings and there was now a NEAR_FID and NEAR_DIST which represented the identification (number) related to the nearest lagoon and the distance in kilometers to the nearest lagoon, respectively.
  5. I exported using the “conversion” tool to Excel and then imported into R studio for further analyses (ANOVA between the differences in sighting distances to lagoons and ENSO indices).

4. Brief description of results you obtained

After a quick histogram in ArcMap, it was visually clear that the distribution of points with nearest lagoons appeared clustered, skewed, or to have a binomial distribution, without considering ENSO. Then, after importing into R studio, I created a box plot of the distance to nearest lagoon compared to the ENSO index (-1, 0, or 1). I ran an ANOVA which returned a very small p-value of 2.55 e-9. Further analysis using a Tukey HSD found that the differences between ENSO states of neutral (0) and -1 and neutral and 1 were significant, but not between 1 and -1. These results are interesting because this means the sightings of dolphins differ most during neutral ENSO years. This could be that certain lagoons are preferred during extremes compared to the neutral years. Therefore, yes, there is a difference in dolphin sightings distances to lagoons during different ENSO phases, specifically the neutral years.

Histogram comparing the distance from the dolphin sighting to nearest lagoon in San Diego during the three major indices of El Niño Southern Oscillation (ENSO): -1, 0, and 1.

 

Violin plot showing the breakdown of distributions of dolphin sighting distances to lagoons (numbered 0-6) during the three different ENSO indices.

5. Critique of the method – what was useful, what was not?

This method was incredibly helpful and also was the easiest to apply once I got started, in comparison to my previous steps. It allowed to both visualize and quantify interesting results. I also learned some tricks for how to better graph my data and to symbolize my data in ArcMap.

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

Exercise 2: Possible Influence of ENSO Index on Dolphin Sighting Latitudes

Exercise 2

Question Asked: Are latitudinal differences in dolphin sightings in the San Diego, CA survey area related to El Niño Southern Oscillation (ENSO) index values on a monthly temporal scale?

  1. My previous question for Exercise 1 was: do the number of dolphin sightings in the San Diego, CA survey region differ latitudinally? I was finally able to answer this question with a histogram of sighting count by latitudinal difference. I defined latitudinal difference as the difference from the highest latitude of dolphin sightings (the Northernmost sighting point along the San Diego transect line) to the other sighting points, in decimal degrees. Therefore it becomes a simple mathematical subtraction in ArcMap. Smaller differences would be the result of a small difference and therefore mean more Northerly sighting, with large differences being from more Southerly areas. I used all sightings in the San Diego region (from 1981 through 2015). As you can see from below, there is an unequal distribution of sightings at different latitudes. Because I had visual confirmation of differences at least when all sightings are binned (in terms of all years from 1981-2015 treated the same), I looked for what process could be affecting these differences in latitude.

    Comparing the Latitudes with the frequency of dolphin sightings in San Diego, CA

ENSO is a large-scale climate phenomena where the climate modes periodically fluctuate (Sprogis et al. 2018). The climate variability produced by ENSO affects physical oceanic and coastal conditions that can both directly and indirectly influence ecological and biological processes. ENSO can alter food webs because climate changes may impact animal physiology, specifically metabolism. This creates further trophic impacts on predator-prey dynamics, often because of prey availability (Barber and Chavez 1983). During the surveys of bottlenose dolphins in California, multiple ENSO cycles have caused widespread changes in the California Current Ecosystem (CCE), such as the squid fishery collapse (Nezlin, Hamner, and Zeidberg 2002). With this knowledge, I wanted to see if the frequency of dolphin sightings in different latitudes of the most-consistently studied area was driven by ENSO.

Tool/Approach:

Primarily R Studio, some ArcMap 10.6 and Excel

Step by Step:

  1. 1.For this portion of the analysis, I exported my table of latitudinal differences within my attribute table for dolphin sightings from ArcMap 10.6. I saved this as a .csv and imported it into R Studio.
  2. Some of the sighting data needed to be changed because R didn’t recognize the dates as dates, rather as factors. This is important in order to join ENSO data by month and year.
  3. Meanwhile, I found NOAA data on a publicly-sourced website that had months as the columns and years as the rows for a matching ENSO index value of either: 1, 0, or -1 for each month/year combination. A value of 1 is a positive (warm) year, a value of 0 is a neutral year, and a value of -1 is a negative (cold) year. This is a broad-value, because indices range from 1 to -1. But, to simplify my question this was the most logical first step.
  4. I had to convert the NOAA data into two-column data with the date in one column by MM/YYYY and then the Index value in the other column. After multiple attempts in R studio, I hand-corrected them in Excel. Then, imported this data into R studio.
  5. I was then able to tell R to match the sighting date’s month and year to the ENSO data’s month and year, and assign the respective ENSO value. Then I assigned the ENSO values as factors.
  6. I created a boxplot to visualize if there were differences in distributions of latitudinal differences and ENSO index. (See figure)Illustrating the number of sightings grouped by ENSO index values (1, 0, and -1).
  7. Then I ran an ANOVA to see if there was a reportable, strong difference in sighting latitudinal difference and ENSO index value.

    Results:

     

    From the boxplot, it appears that in warm years (ENSO index level of “1”), the dolphins are sighted more frequently in lower latitudes, closer to Mexican waters when compared to the neutral (“0”) and cold years (“-1”). This result is intriguing because I would have expected dolphins to move northerly during warm months to maintain similar body temperatures in the same water temperatures. However, warm ENSO years could shift prey availability or nutrients southerly, which is why there are more sightings further south.  The result of the ANOVA, was a p-value of <2e-16, providing very strong evidence to reject the null of hypothesis of no difference. I followed up with a Tukey HSD and found that there is strong evidence for differences between both the 0 and -1, -1 and 1, and 1 and 0 values. Therefore, the different ENSO indices on a monthly scale are significantly contributing to the differences in sighting latitudes in the San Diego study area.

Tukey HSD output:

diff               lwr                        upr           p adj

0–1 0.01161047 0.004250827 0.01897011 0.0006422

1–1 0.04101170 0.030844193 0.05117920 0.0000000

1-0 02940123 0.020689737 0.03811272 0.0000000

 Critique of the Method(s):

These methods worked very well for visualization and finally solidifying that there was a difference on sighting latitude related to ENSO index value on a broad level. Data transformation and clean-up was challenging in R, and took much longer than I’d expected.

 

References:

Barber, Richard T., and Francisco P. Chavez. 1983. “Biological Consequences of El Niño.” Science 222 (4629): 1203–10.

Sprogis, Kate R., Fredrik Christiansen, Moritz Wandres, and Lars Bejder. 2018. “El Niño Southern Oscillation Influences the Abundance and Movements of a Marine Top Predator in Coastal Waters.” Global Change Biology 24 (3): 1085–96. https://doi.org/10.1111/gcb.13892.

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