Question Asked

The question that I sought to answer in this class was “How is the spatial pattern of forage fish assemblage in the California Current System related to the spatial pattern of seascapes based on the sea-surface conditions used to classify the seascapes?”  This is outlined in my first blog post, which can be found here. Additionally, I was seeking to characterize the spatial distribution and extent of seascape classes and forage fishes. Seascape classes are used as a way to simplify the myriad of physical, biological, and chemical processes that can affect organisms in the ocean. Eventually, the focus of the analyses shifted to young-of-the-year rockfish, as opposed to forage fish. All analyses mentioned in this blog post were conducted using the YOY-Rockfish data as opposed to the forage fish data.

Data

Midwater trawls have been conducted annually by the National Oceanic and Atmospheric Administration’s (NOAA) Southwest Fisheries Science Center (SWFSC) in an attempt to monitor the recruitment of pelagic rockfish (Sebastes spp.) and other epipelagic micronekton at SWFSC stations off California. The trawls have informed a dataset that represents overall abundance of all midwater pelagic species that commonly reside along the majority of the nearshore coast of California from 1998 to 2015. Each trawl collected information about both fish abundance, recorded in absolute abundance, and location data, recorded in the form of latitude and longitude. The dataset also includes a breakdown of species by taxa.

Seascapes have been classified using a combination of in-situ data (from the trawls) and remotely sensed data from NASA’s MODIS program. Seascapes were classified using the methods described in Kavanaugh et al., 2014 and represent the seascape class in the immediate area that each trawl occurred. Seascapes are classified at 1 km and 4 km spatial resolution and at 8-day and monthly temporal resolution. Each seascape has been assigned an ID number which is used to identify similar conditions throughout the dataset.

The current seascape classification methods are able to classify surface conditions into one of 33 distinct seascape classes. The variables used to classify the seascapes are listed below:

• Sea-surface temperature (Celsius)
• Sea-surface salinity (Practical salinity units)
• Absolute Dynamic Topography (meters)
• Ice cover (% of area)
• Colored dissolved organic matter (m^-1)
• Spatial averaged sea-surface chlorophyll a (mg m^-3)
• Phytoplankton physiological state (NFLH) (W m^-2 um^-1 sr^-1)
• NFLH:Chlorophyll ratio

It is inferred that certain, carefully-selected sea-surface conditions can imply favorable conditions to support healthy ocean ecosystems in the water column and near the ocean floor. The conditions listed above have been studied and selected for this reason. However, this may not be a perfect science – any attempt to delineate and simplify the multitude of factors and conditions that facilitate life in the ocean is likely to leave out important factors. This underscores the importance of research that tests these classification methods and measures their ability to replicate and predict the true natural environment. The analyses conducted for this class attempt to do that in the context of one functional group of fishes within one major ecosystem in the Pacific Ocean.

Hypotheses

Simply put, I hypothesized that any measurable spatial changes in the spatial extent of certain seascape classes will also be identifiable in the spatial variability of forage fish assemblage over time. The California Current ecosystem is meticulously studied and examined by a myriad of researchers from a number of different affiliate institutions. Studies reviewing the physical and biogeochemical conditions of the area indicate that from an environmental perspective, many areas off of the coast of California should support areas of high fish abundance.

Specifically, I was expecting areas of high forage fish and young of the year rockfish abundance to exist in seascape classes that represent nutrient-rich, upwelled water. These conditions have been shown to support thriving ecosystems throughout the water column due to an abundance of energy and food for fishes that live higher in the water column. Upwelling brings cold, nutrient-rich water to the surface and occurs seasonally in most places along the California Coast. Using the variables listed above, that would mean below average sea-surface temperature, high dissolved organic matter, and high chlorophyll a. Some classes that represent conditions similar to this are:

• Temperate Transition (Class 7)
• Tropical/Subtropical Upwelling (Class 11)
• Subpolar (Class 12)
• Temperate Blooms Upwelling (Class 14)
• Warm, Blooms, High Nutrients (Class 21)

Preliminary multivariate community structure analysis has shown some statistically significant relationships between certain species and certain seascape classes using this data. If spatial patterns do exist, I expect there to be some relationship between the surface conditions and the fish found at depth of the midwater trawls.

Since my shift focused from forage fish to YOY rockfish, my hypothesis shifted from a prediction of correlation between certain seascape classes and forage fish abundance to relationships between rockfish and other seascape classes (as determined by the aforementioned multivariate analyses).

My hypothesis, taking into account all of the aforementioned considerations, can be restated as follows:

It is expected that the spatial pattern of areas of high young-of-the-year rockfish abundance will be related to the spatial patterns of seascape classes that represent areas of high nutrient availability and upwelled water along the California Coast.

Additionally, I expect even higher areas of abundance to occur in areas where two or more seascapes representing these favorable conditions converge or border one another. These border areas are likely to indicate much larger swaths of ocean that hold habitable conditions that are likely to be able to support entire communities rather than smaller numbers of fishes. While this hypothesis was not tested in this project, future work can be conducted using FRAGSTATS, patch analysis software, and other landscape ecology methods to seek an answer related to this prediction.

Approaches Used

Exercise 1: For the first exercise, different interpolation methods were used to explore their effects on the point trawls data. The interpolations (Kriging and IDW were tested) were used to model the abundance of rockfish in four selected years.

Exercise 2: Next, a neighborhood analysis was used to determine what the dominant seascapes were in the areas around trawls that produced high rockfish abundance (both according to the data and the interpolation) and low rockfish abundance. Buffers were set at 5, 10, and 25km distances around two trawls of each kind and seascape classes were measured as a function of % area of each buffer.

Exercise 3: Finally, a confusion matrix was calculated that measured the agreement between occurrence of the significant seascapes (as determined by exercise 2) and areas of high abundance (as determined by the interpolations in exercise 1).

Results

Exercise 1: The interpolations produced a number of maps of varying spatial extents and with varying resolution. Methods had to be refined multiple times to procure workable results, but maps interpolating rockfish abundance for 4 distinct years were eventually created and used.

Exercise 2: The neighborhood analysis produced statistics related to the percent of each buffer area occupied by each seascape class. This information was then used to create two plots – one for areas of high abundance and one for areas of low abundance. The most important result was an understanding of which seascape classes represented areas of high rockfish abundance. The dominant seascape classes in areas of high abundance turned out to match two of the seascape classes predicted in my hypothesis: Temperate Transition (Class 7) and Subpolar (Class 12).

Exercise 3: The final exercise produced a confusion matrix that measured true positives, true negatives, false positives, and false negatives as they related to the agreement between the seascape classes and the interpolated maps. The final matrix also produced an overall agreement score (Kappa).

Significance

The confusion matrix ended up being a bit of a failure, as both the seascape information and interpolated information had to be turned into presence-absence data for the matrix to be calculated. This eliminated some of the fine-tuned resolution associated with the interpolation and ultimately led to a Kappa score that suggested less agreement than would be expected by chance. While this specific result is not important to science or managers, these methods can be refined to improve the agreement between the two sources, ultimately providing a way to statistically link physical ocean conditions and rockfish abundances in California.

For these reasons, I consider this project to be an excellent pilot study for these data – one that introduces me to the types of analyses available to researchers who work with these types of data and their strengths and shortcomings.

Learning Outcomes

Software and Statistics

This class gave me an opportunity to brush up on my Arcmap and Modelbuilder skills, which I used to be very proficient in but had lost. I’ve also had the opportunity to explore some analyses through the lenses of R and Python, though I did not end up executing any parts of my project in these programs. The most important statistical learning outcomes for me had to do with interpolation and confusion matrices. The first exercise taught me all about the different ways point data can be interpolated and the consequences that that can have for results. I was familiar with the different types of hotspot analysis available to users in ArcGIS, but was not aware of their differences. The confusion matrix introduced me to an entirely new way to connect independent modeling methods. Methodologically, I learned about the importance of recording your methods as you execute processes and about the benefits of naming files sequentially. Had I not been organized, I would have had a hard time keeping track of the different processes executed and files used during each step.